Plot for 6c
ERM
2025-02-26
Last updated: 2025-02-26
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a3bcc33 | E. Renee Matthews | 2025-02-26 | wflow_publish("analysis/Correlation_of_GWASnPEAK.Rmd") |
| html | 7ea74a3 | E. Renee Matthews | 2025-02-10 | Build site. |
| Rmd | 3a37987 | E. Renee Matthews | 2025-02-10 | update heatmap legend |
| html | 047da51 | E. Renee Matthews | 2025-02-07 | Build site. |
| Rmd | 7fbe175 | E. Renee Matthews | 2025-02-07 | adding in Park data |
| html | 5e56c1b | E. Renee Matthews | 2025-01-24 | Build site. |
| Rmd | 8a21094 | E. Renee Matthews | 2025-01-24 | new final figure updates |
| html | bdb9ba0 | E. Renee Matthews | 2025-01-21 | Build site. |
| Rmd | b3248cc | E. Renee Matthews | 2025-01-21 | adding in protein data |
| html | fb6aa7a | E. Renee Matthews | 2025-01-21 | Build site. |
| Rmd | 3eb76dd | E. Renee Matthews | 2025-01-21 | adding in CELSR2 |
| html | a505a0a | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | 4a1d2bf | E. Renee Matthews | 2025-01-17 | updates to plots |
| html | ae1542c | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | e179f61 | E. Renee Matthews | 2025-01-17 | updates with comments |
| html | d09c7db | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | 20ed2fe | E. Renee Matthews | 2025-01-17 | additional analysis |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(arrow)
library(circlize)Notes to self(and anyone else who is reading this!):
This is me applying the same code from my correlation_of_SNPnPeak.rmd
document.
Summary of what I am doing: 1: create a list of peaks within +/-20 kb,
+/-10 kb, and +/- 5 kb of an RNA expressed gene TSS. (3 separate
lists)
2: making a dataframe that has all ATAC 3 hour and 24 hr LFC by peak for
later ease of use. 3: creating lists of gwas SNPs (HF and ARR lists
only) that are either 1bp, 10kb, 20kb, or 50kb in length to determine
impact of the SNP on surrounding peaks.
Collapsed_new_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
Collapsed_new_peaks_gr <- Collapsed_new_peaks %>% dplyr::select(chr:Peakid) %>% GRanges()
peak_10kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<5000&dist_to_NG>-5000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
peak_20kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<10000&dist_to_NG>-10000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
peak_40kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<20000&dist_to_NG>-20000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
RNA_median_3_lfc <- readRDS("data/other_papers/RNA_median_3_lfc.RDS")
RNA_median_24_lfc <- readRDS("data/other_papers/RNA_median_24_lfc.RDS")
ATAC_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")
ATAC_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
#### AS of 1/23/24, I am pulling updated gwas for HF and ARR (now the term is Atrial fib) These are stored as RDS in the other papers folder
# saveRDS(gwas_ud_HF, "data/other_papers/HF_gwas_association_downloaded_2025_01_23_EFO_0003144_withChildTraits.RDS")
# saveRDS(gwas_ud_AF,"data/other_papers/AF_gwas_association_downloaded_2025_01_23_EFO_0000275.RDS")
gwas_HF <- readRDS("data/other_papers/HF_gwas_association_downloaded_2025_01_23_EFO_0003144_withChildTraits.RDS")
gwas_ARR <- readRDS("data/other_papers/AF_gwas_association_downloaded_2025_01_23_EFO_0000275.RDS")
Short_gwas_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS, end=CHR_POS, width=1) %>%
GRanges()
Short_gwas_5k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS-5000, end=CHR_POS+4999) %>%
GRanges()
Short_gwas_20k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=(CHR_POS-10000),end=(CHR_POS+9999), width=20000) %>%
distinct() %>%
GRanges()
Short_gwas_50k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=(CHR_POS-25000),end=(CHR_POS+24999), width=50000) %>%
distinct() %>%
GRanges()
gwas_peak_check <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_gr) %>%
as.data.frame()
#
gwas_peak_check_10k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_5k_gr) %>%
as.data.frame()
gwas_peak_check_20k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_20k_gr) %>%
as.data.frame()
gwas_peak_check_50k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_50k_gr) %>%
as.data.frame()
ATAC_LFC <- Collapsed_new_peaks %>%
dplyr::select(Peakid) %>%
left_join(.,(ATAC_3_lfc %>% dplyr::select(peak, med_3h_lfc)), by=c("Peakid"="peak")) %>%
left_join(.,(ATAC_24_lfc %>% dplyr::select(peak, med_24h_lfc)), by=c("Peakid"="peak"))
Short_gwas_gr %>% as.data.frame(
) seqnames start end width strand CHR_ID CHR_POS SNPS
1 chr4 110485663 110485663 1 * 4 110485663 rs192667187
2 chr4 110491909 110491909 1 * 4 110491909 rs192833524
3 chr15 69714534 69714534 1 * 15 69714534 rs4776472
4 chr13 46259582 46259582 1 * 13 46259582 rs958546
5 chr11 99622442 99622442 1 * 11 99622442 rs10501920
6 chr1 203065778 203065778 1 * 1 203065778 rs3737883
7 chr4 173678308 173678308 1 * 4 173678308 rs4615152
8 chr1 170600176 170600176 1 * 1 170600176 rs3903239
9 chr4 110784612 110784612 1 * 4 110784612 rs6817105
10 chr10 103539854 103539854 1 * 10 103539854 rs6584555
11 chr16 73017721 73017721 1 * 16 73017721 rs2106261
12 chr6 32039119 32039119 1 * 6 32039119 rs6455
13 chr8 74007478 74007478 1 * 8 74007478 rs10504554
14 chr10 18705654 18705654 1 * 10 18705654 rs11015781
15 chr5 135024741 135024741 1 * 5 135024741 rs31209
16 chr12 25905670 25905670 1 * 12 25905670 rs117640426
17 chr1 10730740 10730740 1 * 1 10730740 rs284277
18 chr1 21956126 21956126 1 * 1 21956126 rs7529220
19 chr1 41078607 41078607 1 * 1 41078607 rs2885697
20 chr1 48844092 48844092 1 * 1 48844092 rs11590635
21 chr1 51069367 51069367 1 * 1 51069367 rs146518726
22 chr1 111840049 111840049 1 * 1 111840049 rs11102343
23 chr1 111916271 111916271 1 * 1 111916271 rs72694603
24 chr1 115755137 115755137 1 * 1 115755137 rs4073778
25 chr1 147783720 147783720 1 * 1 147783720 rs79187193
26 chr1 154425113 154425113 1 * 1 154425113 rs12118770
27 chr1 154741530 154741530 1 * 1 154741530 rs4999127
28 chr1 154828771 154828771 1 * 1 154828771 rs1218550
29 chr1 154836777 154836777 1 * 1 154836777 rs11264273
30 chr1 154837103 154837103 1 * 1 154837103 rs77328013
31 chr1 154851069 154851069 1 * 1 154851069 rs2878411
32 chr1 154890476 154890476 1 * 1 154890476 rs11264280
33 chr1 154919912 154919912 1 * 1 154919912 rs151107921
34 chr1 155013615 155013615 1 * 1 155013615 rs3753639
35 chr1 155051158 155051158 1 * 1 155051158 rs78266397
36 chr1 170151690 170151690 1 * 1 170151690 rs535709
37 chr1 170197680 170197680 1 * 1 170197680 rs7415755
38 chr1 170207618 170207618 1 * 1 170207618 rs113116849
39 chr1 170225682 170225682 1 * 1 170225682 rs72700118
40 chr1 170226090 170226090 1 * 1 170226090 rs4399218
41 chr1 170503391 170503391 1 * 1 170503391 rs1333135
42 chr1 170541318 170541318 1 * 1 170541318 rs17346300
43 chr1 170618199 170618199 1 * 1 170618199 rs577676
44 chr1 170733116 170733116 1 * 1 170733116 rs76074817
45 chr1 170793539 170793539 1 * 1 170793539 rs10919470
46 chr1 203057086 203057086 1 * 1 203057086 rs10753933
47 chr1 205716224 205716224 1 * 1 205716224 rs951366
48 chr2 25927904 25927904 1 * 2 25927904 rs6728684
49 chr2 61242991 61242991 1 * 2 61242991 rs11125871
50 chr2 65052671 65052671 1 * 2 65052671 rs2723064
51 chr2 69879700 69879700 1 * 2 69879700 rs6747542
52 chr2 86367364 86367364 1 * 2 86367364 rs72926475
53 chr2 126675889 126675889 1 * 2 126675889 rs28387148
54 chr2 145025041 145025041 1 * 2 145025041 rs56014517
55 chr4 111085761 111085761 1 * 4 111085761 rs297006
56 chr4 111172387 111172387 1 * 4 111172387 rs72674110
57 chr4 111257689 111257689 1 * 4 111257689 rs1472076
58 chr4 113507558 113507558 1 * 4 113507558 rs55754224
59 chr4 148031831 148031831 1 * 4 148031831 rs6839459
60 chr4 173553446 173553446 1 * 4 173553446 rs11933956
61 chr4 173721638 173721638 1 * 4 173721638 rs74500426
62 chr5 107091908 107091908 1 * 5 107091908 rs6596717
63 chr5 114401365 114401365 1 * 5 114401365 rs337705
64 chr5 115036973 115036973 1 * 5 115036973 rs7704089
65 chr5 128881021 128881021 1 * 5 128881021 rs1345608
66 chr5 137403961 137403961 1 * 5 137403961 rs739701
67 chr5 137676798 137676798 1 * 5 137676798 rs6888113
68 chr5 138029106 138029106 1 * 5 138029106 rs17171711
69 chr5 138322040 138322040 1 * 5 138322040 rs143731384
70 chr5 138444679 138444679 1 * 5 138444679 rs2240331
71 chr5 143438558 143438558 1 * 5 143438558 rs6580277
72 chr5 143569948 143569948 1 * 5 143569948 rs6896317
73 chr5 168959084 168959084 1 * 5 168959084 rs12188351
74 chr5 173173285 173173285 1 * 5 173173285 rs6890182
75 chr5 173237160 173237160 1 * 5 173237160 rs6882776
76 chr5 173965957 173965957 1 * 5 173965957 rs56180201
77 chr6 16415520 16415520 1 * 6 16415520 rs73366713
78 chr6 18361432 18361432 1 * 6 18361432 rs10949499
79 chr6 36679512 36679512 1 * 6 36679512 rs3176326
80 chr6 87111783 87111783 1 * 6 87111783 rs2031522
81 chr6 117559547 117559547 1 * 6 117559547 rs210634
82 chr6 118244502 118244502 1 * 6 118244502 rs4946333
83 chr6 118460100 118460100 1 * 6 118460100 rs139811148
84 chr6 118699842 118699842 1 * 6 118699842 rs62424001
85 chr6 121778006 121778006 1 * 6 121778006 rs9401451
86 chr6 122077095 122077095 1 * 6 122077095 rs72966339
87 chr6 148987899 148987899 1 * 6 148987899 rs1564602
88 chr6 149077964 149077964 1 * 6 149077964 rs117984853
89 chr6 149088724 149088724 1 * 6 149088724 rs1320575
90 chr7 14332384 14332384 1 * 7 14332384 rs55734480
91 chr7 28376208 28376208 1 * 7 28376208 rs6462079
92 chr7 74720592 74720592 1 * 7 74720592 rs35005436
93 chr7 92648802 92648802 1 * 7 92648802 rs56201652
94 chr7 116210621 116210621 1 * 7 116210621 rs11549785
95 chr7 116364407 116364407 1 * 7 116364407 rs11974466
96 chr7 116540796 116540796 1 * 7 116540796 rs4730751
97 chr7 116551247 116551247 1 * 7 116551247 rs11773845
98 chr7 128776990 128776990 1 * 7 128776990 rs55985730
99 chr7 150964321 150964321 1 * 7 150964321 rs7789146
100 chr8 11642399 11642399 1 * 8 11642399 rs35620480
101 chr8 18056461 18056461 1 * 8 18056461 rs7508
102 chr8 21964267 21964267 1 * 8 21964267 rs7834729
103 chr8 123539735 123539735 1 * 8 123539735 rs62521286
104 chr8 123540063 123540063 1 * 8 123540063 rs7835298
105 chr2 174563911 174563911 1 * 2 174563911 rs17507821
106 chr2 174690986 174690986 1 * 2 174690986 rs56181519
107 chr2 178546938 178546938 1 * 2 178546938 rs2288327
108 chr2 200304035 200304035 1 * 2 200304035 rs56326533
109 chr2 212395133 212395133 1 * 2 212395133 rs6738011
110 chr3 12800724 12800724 1 * 3 12800724 rs4642101
111 chr3 24421744 24421744 1 * 3 24421744 rs73041705
112 chr3 38668824 38668824 1 * 3 38668824 rs7373065
113 chr3 38736063 38736063 1 * 3 38736063 rs10428132
114 chr3 66403767 66403767 1 * 3 66403767 rs34080181
115 chr3 69357030 69357030 1 * 3 69357030 rs17005647
116 chr3 89440379 89440379 1 * 3 89440379 rs6771054
117 chr3 111835579 111835579 1 * 3 111835579 rs10804493
118 chr3 111985572 111985572 1 * 3 111985572 rs73232036
119 chr3 136095167 136095167 1 * 3 136095167 rs1278493
120 chr3 179455191 179455191 1 * 3 179455191 rs7612445
121 chr3 195080124 195080124 1 * 3 195080124 rs60902112
122 chr4 80243569 80243569 1 * 4 80243569 rs1458038
123 chr4 110401104 110401104 1 * 4 110401104 rs141752220
124 chr4 110485340 110485340 1 * 4 110485340 rs13118687
125 chr4 110524540 110524540 1 * 4 110524540 rs28705758
126 chr4 110546303 110546303 1 * 4 110546303 rs524788
127 chr4 110574086 110574086 1 * 4 110574086 rs10028326
128 chr4 110589415 110589415 1 * 4 110589415 rs1470618
129 chr4 110609843 110609843 1 * 4 110609843 rs72656974
130 chr4 110639637 110639637 1 * 4 110639637 rs62338990
131 chr4 110659017 110659017 1 * 4 110659017 rs60452787
132 chr4 110665288 110665288 1 * 4 110665288 rs17746631
133 chr4 110674360 110674360 1 * 4 110674360 rs2595117
134 chr4 110714136 110714136 1 * 4 110714136 rs2595082
135 chr4 110716779 110716779 1 * 4 110716779 rs78734303
136 chr4 110735436 110735436 1 * 4 110735436 rs112599895
137 chr4 110743002 110743002 1 * 4 110743002 rs17042098
138 chr4 110766821 110766821 1 * 4 110766821 rs77668866
139 chr4 110796991 110796991 1 * 4 110796991 rs13105878
140 chr4 110809291 110809291 1 * 4 110809291 rs7683219
141 chr4 110822697 110822697 1 * 4 110822697 rs28601812
142 chr4 110823850 110823850 1 * 4 110823850 rs113832645
143 chr4 110844339 110844339 1 * 4 110844339 rs6838973
144 chr4 110882594 110882594 1 * 4 110882594 rs4590107
145 chr4 110908811 110908811 1 * 4 110908811 rs561873
146 chr4 110941992 110941992 1 * 4 110941992 rs13149878
147 chr4 110978126 110978126 1 * 4 110978126 rs55947985
148 chr4 110992909 110992909 1 * 4 110992909 rs514739
149 chr14 76960182 76960182 1 * 14 76960182 rs10873298
150 chr15 73173136 73173136 1 * 15 73173136 rs1979409
151 chr15 73374914 73374914 1 * 15 73374914 rs7172038
152 chr15 80384583 80384583 1 * 15 80384583 rs12908004
153 chr15 80701947 80701947 1 * 15 80701947 rs2759301
154 chr15 98727906 98727906 1 * 15 98727906 rs6598541
155 chr16 1953015 1953015 1 * 16 1953015 rs140185678
156 chr16 1964282 1964282 1 * 16 1964282 rs2286466
157 chr16 2215270 2215270 1 * 16 2215270 rs77316573
158 chr16 73033862 73033862 1 * 16 73033862 rs876727
159 chr16 73053790 73053790 1 * 16 73053790 rs9930504
160 chr16 73091264 73091264 1 * 16 73091264 rs138619337
161 chr17 1406556 1406556 1 * 17 1406556 rs7225165
162 chr17 7549660 7549660 1 * 17 7549660 rs9899183
163 chr17 12709614 12709614 1 * 17 12709614 rs55941572
164 chr17 39893336 39893336 1 * 17 39893336 rs7359623
165 chr17 45579891 45579891 1 * 17 45579891 rs79742625
166 chr17 46797087 46797087 1 * 17 46797087 rs1563304
167 chr17 78777556 78777556 1 * 17 78777556 rs12604076
168 chr18 49000656 49000656 1 * 18 49000656 rs866245
169 chr21 34746814 34746814 1 * 21 34746814 rs2834618
170 chr22 18114735 18114735 1 * 22 18114735 rs464901
171 chr22 25768112 25768112 1 * 22 25768112 rs133902
172 chr22 25847092 25847092 1 * 22 25847092 rs8141828
173 chr10 110816937 110816937 1 * 10 110816937 rs10749053
174 chr11 19988745 19988745 1 * 11 19988745 rs4757877
175 chr11 121774297 121774297 1 * 11 121774297 rs2156664
176 chr11 128894675 128894675 1 * 11 128894675 rs76097649
177 chr12 24562114 24562114 1 * 12 24562114 rs2291437
178 chr12 24610889 24610889 1 * 12 24610889 rs10842379
179 chr12 24619033 24619033 1 * 12 24619033 rs10842383
180 chr12 26192593 26192593 1 * 12 26192593 rs17380837
181 chr12 32825503 32825503 1 * 12 32825503 rs12809354
182 chr12 56712154 56712154 1 * 12 56712154 rs2860482
183 chr12 69619635 69619635 1 * 12 69619635 rs71454237
184 chr12 69702594 69702594 1 * 12 69702594 rs775439
185 chr12 75844207 75844207 1 * 12 75844207 rs12426679
186 chr12 114254231 114254231 1 * 12 114254231 rs1247933
187 chr12 114355435 114355435 1 * 12 114355435 rs883079
188 chr12 114374821 114374821 1 * 12 114374821 rs11067089
189 chr13 22794804 22794804 1 * 13 22794804 rs9506925
190 chr13 113200401 113200401 1 * 13 113200401 rs3751413
191 chr14 23395595 23395595 1 * 14 23395595 rs422068
192 chr14 32455299 32455299 1 * 14 32455299 rs1957021
193 chr14 32514611 32514611 1 * 14 32514611 rs7140396
194 chr14 34704569 34704569 1 * 14 34704569 rs73241997
195 chr14 64213242 64213242 1 * 14 64213242 rs2738413
196 chr14 72782711 72782711 1 * 14 72782711 rs74884082
197 chr14 72888423 72888423 1 * 14 72888423 rs2110552
198 chr8 140730769 140730769 1 * 8 140730769 rs6994744
199 chr9 94835868 94835868 1 * 9 94835868 rs7019540
200 chr9 94951177 94951177 1 * 9 94951177 rs10821415
201 chr9 95036847 95036847 1 * 9 95036847 rs899268
202 chr9 136202927 136202927 1 * 9 136202927 rs2274115
203 chr10 63561387 63561387 1 * 10 63561387 rs12245149
204 chr10 73660422 73660422 1 * 10 73660422 rs7915134
205 chr10 75119277 75119277 1 * 10 75119277 rs7072512
206 chr10 76176912 76176912 1 * 10 76176912 rs10458662
207 chr10 101899844 101899844 1 * 10 101899844 rs1374471
208 chr10 102380845 102380845 1 * 10 102380845 rs3781295
209 chr10 102626337 102626337 1 * 10 102626337 rs149754618
210 chr10 103538219 103538219 1 * 10 103538219 rs7088041
211 chr10 103575399 103575399 1 * 10 103575399 rs3781370
212 chr10 103578969 103578969 1 * 10 103578969 rs77260060
213 chr10 103582915 103582915 1 * 10 103582915 rs11598047
214 chr10 103631542 103631542 1 * 10 103631542 rs7918134
215 chr10 103648362 103648362 1 * 10 103648362 rs141221125
216 chr10 103663571 103663571 1 * 10 103663571 rs138461584
217 chr10 103668394 103668394 1 * 10 103668394 rs3781339
218 chr10 103694357 103694357 1 * 10 103694357 rs3758576
219 chr10 103733419 103733419 1 * 10 103733419 rs11596328
220 chr10 103756892 103756892 1 * 10 103756892 rs7898224
221 chr10 103930273 103930273 1 * 10 103930273 rs12220140
222 chr10 103959161 103959161 1 * 10 103959161 rs35339759
223 chr1 164093291 164093291 1 * 1 164093291 rs528903211
224 chr1 164354848 164354848 1 * 1 164354848 rs750729995
225 chr1 165247437 165247437 1 * 1 165247437 rs747358876
226 chr1 165362063 165362063 1 * 1 165362063 rs745582874
227 chr2 20576675 20576675 1 * 2 20576675 rs182836018
228 chr4 102192657 102192657 1 * 4 102192657 rs77616117
229 chr8 9026103 9026103 1 * 8 9026103 rs189051215
230 chr13 100490655 100490655 1 * 13 100490655 rs768476991
231 chr13 100743542 100743542 1 * 13 100743542 rs535535747
232 chr17 17199699 17199699 1 * 17 17199699 rs12939857
233 chr1 111849738 111849738 1 * 1 111849738 rs12044963
234 chr1 170368215 170368215 1 * 1 170368215 rs4656762
235 chr2 65049602 65049602 1 * 2 65049602 rs2540951
236 chr4 110707883 110707883 1 * 4 110707883 rs16997168
237 chr4 173526992 173526992 1 * 4 173526992 rs10024737
238 chr5 114413763 114413763 1 * 5 114413763 rs1013168
239 chr7 116551643 116551643 1 * 7 116551643 rs9886216
240 chr10 76190062 76190062 1 * 10 76190062 rs16932995
241 chr10 103713072 103713072 1 * 10 103713072 rs10509768
242 chr12 114351421 114351421 1 * 12 114351421 rs2384407
243 chr16 72877507 72877507 1 * 16 72877507 rs7197197
244 chr2 25937505 25937505 1 * 2 25937505 rs7604968
245 chr10 67826634 67826634 1 * 10 67826634 rs138645114
246 chr4 173523751 173523751 1 * 4 173523751 rs17059534
247 chr4 110796911 110796911 1 * 4 110796911 rs6843082
248 chr4 110840331 110840331 1 * 4 110840331 rs3853445
249 chr1 203057463 203057463 1 * 1 203057463 rs17461925
250 chr2 65045228 65045228 1 * 2 65045228 rs2540953
251 chr4 173682953 173682953 1 * 4 173682953 rs7698692
252 chr10 20868692 20868692 1 * 10 20868692 rs2296610
253 chr10 103717405 103717405 1 * 10 103717405 rs2047036
254 chr5 140333293 140333293 1 * 5 140333293 rs13385
255 chr9 90890978 90890978 1 * 9 90890978 rs1675334
256 chr4 110793733 110793733 1 * 4 110793733 rs2220427
257 chr7 116560326 116560326 1 * 7 116560326 rs1049334
258 chr10 103565017 103565017 1 * 10 103565017 rs60572254
259 chr6 122093011 122093011 1 * 6 122093011 rs13219206
260 chr1 170643732 170643732 1 * 1 170643732 rs639652
261 chr12 111269073 111269073 1 * 12 111269073 rs4766566
262 chr4 110742777 110742777 1 * 4 110742777 rs78073007
263 chr19 47639489 47639489 1 * 19 47639489 rs11881441
264 chr20 38213512 38213512 1 * 20 38213512 rs3746471
265 chr22 21644940 21644940 1 * 22 21644940 rs5754508
266 chr22 41793403 41793403 1 * 22 41793403 rs139557
267 chr1 10736809 10736809 1 * 1 10736809 rs880315
268 chr1 51539068 51539068 1 * 1 51539068 rs12022114
269 chr1 147756741 147756741 1 * 1 147756741 rs11240121
270 chr1 170659114 170659114 1 * 1 170659114 rs680084
271 chr1 203063025 203063025 1 * 1 203063025 rs4950913
272 chr1 205722188 205722188 1 * 1 205722188 rs4951258
273 chr2 25937071 25937071 1 * 2 25937071 rs6546620
274 chr2 61449805 61449805 1 * 2 61449805 rs2694635
275 chr2 65057097 65057097 1 * 2 65057097 rs2540949
276 chr2 86356069 86356069 1 * 2 86356069 rs13387570
277 chr2 144924368 144924368 1 * 2 144924368 rs11679718
278 chr2 148042141 148042141 1 * 2 148042141 rs12992231
279 chr2 174648092 174648092 1 * 2 174648092 rs7574892
280 chr2 178548383 178548383 1 * 2 178548383 rs3731748
281 chr2 200315300 200315300 1 * 2 200315300 rs3820888
282 chr1 983237 983237 1 * 1 983237 rs4970418
283 chr1 15872556 15872556 1 * 1 15872556 rs9782984
284 chr1 38920042 38920042 1 * 1 38920042 rs75414548
285 chr1 99683752 99683752 1 * 1 99683752 rs1933723
286 chr4 70911218 70911218 1 * 4 70911218 rs12512502
287 chr4 82989559 82989559 1 * 4 82989559 rs6841049
288 chr5 140323701 140323701 1 * 5 140323701 rs17118812
289 chr6 22598030 22598030 1 * 6 22598030 rs7766436
290 chr6 75454873 75454873 1 * 6 75454873 rs12209223
291 chr6 134797951 134797951 1 * 6 134797951 rs4896104
292 chr7 105972290 105972290 1 * 7 105972290 rs2727757
293 chr8 117851173 117851173 1 * 8 117851173 rs17430357
294 chr9 116419515 116419515 1 * 9 116419515 rs17303101
295 chr10 32483806 32483806 1 * 10 32483806 rs11527634
296 chr10 49277389 49277389 1 * 10 49277389 rs76460895
297 chr10 79139212 79139212 1 * 10 79139212 rs1769758
298 chr11 3868829 3868829 1 * 11 3868829 rs7126870
299 chr11 14014642 14014642 1 * 11 14014642 rs10500790
300 chr11 95356718 95356718 1 * 11 95356718 rs517938
301 chr12 12733093 12733093 1 * 12 12733093 rs10845620
302 chr12 104098225 104098225 1 * 12 104098225 rs2629755
303 chr13 21537382 21537382 1 * 13 21537382 rs11841562
304 chr13 73946049 73946049 1 * 13 73946049 rs1886512
305 chr16 15808858 15808858 1 * 16 15808858 rs9284324
306 chr18 79396537 79396537 1 * 18 79396537 rs8096658
307 chr2 212386779 212386779 1 * 2 212386779 rs13019524
308 chr3 12800305 12800305 1 * 3 12800305 rs7650482
309 chr3 24431375 24431375 1 * 3 24431375 rs73032363
310 chr3 38725824 38725824 1 * 3 38725824 rs6801957
311 chr4 10102854 10102854 1 * 4 10102854 rs12640611
312 chr4 80248758 80248758 1 * 4 80248758 rs11099098
313 chr4 102791180 102791180 1 * 4 102791180 rs223449
314 chr4 110789811 110789811 1 * 4 110789811 rs17042175
315 chr4 148016386 148016386 1 * 4 148016386 rs10213171
316 chr4 173551270 173551270 1 * 4 173551270 rs4282143
317 chr5 114410483 114410483 1 * 5 114410483 rs337708
318 chr5 128854670 128854670 1 * 5 128854670 rs2012809
319 chr5 138107797 138107797 1 * 5 138107797 rs529526
320 chr5 168966222 168966222 1 * 5 168966222 rs77328981
321 chr5 173243742 173243742 1 * 5 173243742 rs6891790
322 chr6 16418331 16418331 1 * 6 16418331 rs9370893
323 chr6 18209878 18209878 1 * 6 18209878 rs34969716
324 chr6 34221089 34221089 1 * 6 34221089 rs12214804
325 chr6 87176617 87176617 1 * 6 87176617 rs7757330
326 chr6 118653635 118653635 1 * 6 118653635 rs9481842
327 chr6 133111184 133111184 1 * 6 133111184 rs6902225
328 chr6 149092383 149092383 1 * 6 149092383 rs78811127
329 chr7 855648 855648 1 * 7 855648 rs6461461
330 chr7 14334089 14334089 1 * 7 14334089 rs12154315
331 chr7 28377595 28377595 1 * 7 28377595 rs9639575
332 chr7 92620826 92620826 1 * 7 92620826 rs42044
333 chr7 116558567 116558567 1 * 7 116558567 rs1997571
334 chr7 150972888 150972888 1 * 7 150972888 rs3778872
335 chr8 11927032 11927032 1 * 8 11927032 rs6996342
336 chr8 18055243 18055243 1 * 8 18055243 rs399485
337 chr8 21947754 21947754 1 * 8 21947754 rs6998692
338 chr8 123597926 123597926 1 * 8 123597926 rs58847541
339 chr9 20235006 20235006 1 * 9 20235006 rs4977397
340 chr10 20953453 20953453 1 * 10 20953453 rs7910227
341 chr10 67854979 67854979 1 * 10 67854979 rs12360521
342 chr10 73654586 73654586 1 * 10 73654586 rs60212594
343 chr10 103556539 103556539 1 * 10 103556539 rs74154539
344 chr11 121783619 121783619 1 * 11 121783619 rs7946552
345 chr12 26195496 26195496 1 * 12 26195496 rs113819537
346 chr12 75845075 75845075 1 * 12 75845075 rs1565765
347 chr12 111193961 111193961 1 * 12 111193961 rs4766552
348 chr12 122827504 122827504 1 * 12 122827504 rs897393
349 chr12 124328132 124328132 1 * 12 124328132 rs3741508
350 chr12 132515028 132515028 1 * 12 132515028 rs4883571
351 chr13 22794695 22794695 1 * 13 22794695 rs3904323
352 chr13 113210523 113210523 1 * 13 113210523 rs2316443
353 chr14 32521231 32521231 1 * 14 32521231 rs11156751
354 chr15 70171653 70171653 1 * 15 70171653 rs2415081
355 chr15 98725621 98725621 1 * 15 98725621 rs4965430
356 chr16 714753 714753 1 * 16 714753 rs3809666
357 chr16 1954717 1954717 1 * 16 1954717 rs2815301
358 chr16 73014468 73014468 1 * 16 73014468 rs67329386
359 chr17 1408752 1408752 1 * 17 1408752 rs61248729
360 chr17 7511639 7511639 1 * 17 7511639 rs2071502
361 chr17 12715363 12715363 1 * 17 12715363 rs72811294
362 chr17 45942346 45942346 1 * 17 45942346 rs242557
363 chr17 47060667 47060667 1 * 17 47060667 rs145153053
364 chr17 70425662 70425662 1 * 17 70425662 rs1396517
365 chr18 48947822 48947822 1 * 18 48947822 rs9953366
366 chr20 62557186 62557186 1 * 20 62557186 rs6089752
367 chr1 170662622 170662622 1 * 1 170662622 rs629234
368 chr1 203062910 203062910 1 * 1 203062910 rs11579558
369 chr2 65128252 65128252 1 * 2 65128252 rs75251643
370 chr2 200298731 200298731 1 * 2 200298731 rs4673904
371 chr3 12831496 12831496 1 * 3 12831496 rs75387493
372 chr4 110795357 110795357 1 * 4 110795357 rs12644625
373 chr4 148015263 148015263 1 * 4 148015263 rs10213376
374 chr4 173542765 173542765 1 * 4 173542765 rs187693118
375 chr6 133122523 133122523 1 * 6 133122523 rs6941949
376 chr8 123622957 123622957 1 * 8 123622957 rs4871397
377 chr9 95129587 95129587 1 * 9 95129587 rs149672087
378 chr10 67504002 67504002 1 * 10 67504002 rs548764966
379 chr10 103574950 103574950 1 * 10 103574950 rs185158502
380 chr12 24648922 24648922 1 * 12 24648922 rs7973464
381 chr12 26443292 26443292 1 * 12 26443292 rs74763618
382 chr12 111171923 111171923 1 * 12 111171923 rs3809297
383 chr14 32442886 32442886 1 * 14 32442886 rs10138310
384 chr16 73019123 73019123 1 * 16 73019123 rs2359171
385 chr16 30608424 30608424 1 * 16 30608424 rs1055894680
386 chrX 23381384 23381384 1 * X 23381384 rs73205368
387 chrX 138708418 138708418 1 * X 138708418 rs778479352
388 chr2 65155127 65155127 1 * 2 65155127 rs9989843
389 chr4 173530848 173530848 1 * 4 173530848 rs2276940
390 chr6 133244597 133244597 1 * 6 133244597 rs1582725060
391 chr9 95006476 95006476 1 * 9 95006476 rs147288039
392 chr12 24650915 24650915 1 * 12 24650915 rs60634518
393 chr14 32505638 32505638 1 * 14 32505638 rs8010040
394 chr4 110776497 110776497 1 * 4 110776497 rs78229461
395 chr10 20913431 20913431 1 * 10 20913431 rs3802729
396 chr10 103575604 103575604 1 * 10 103575604 rs373205748
397 chr4 110789013 110789013 1 * 4 110789013 rs2200733
398 chr16 72995261 72995261 1 * 16 72995261 rs7193343
399 chr4 110799605 110799605 1 * 4 110799605 rs10033464
400 chr10 73661450 73661450 1 * 10 73661450 rs10824026
401 chr10 103720629 103720629 1 * 10 103720629 rs35176054
402 chr1 50525780 50525780 1 * 1 50525780 rs56202902
403 chr2 178624999 178624999 1 * 2 178624999 rs12614435
404 chr1 154858667 154858667 1 * 1 154858667 rs34245846
405 chr1 170224684 170224684 1 * 1 170224684 rs72700114
406 chr4 110782354 110782354 1 * 4 110782354 rs77831929
407 chr16 72998133 72998133 1 * 16 72998133 rs4404097
408 chr2 144977379 144977379 1 * 2 144977379 rs12621647
409 chr12 122843353 122843353 1 * 12 122843353 rs10773657
410 chr17 46789236 46789236 1 * 17 46789236 rs199497
411 chr1 111921382 111921382 1 * 1 111921382 rs1545300
412 chr2 126679788 126679788 1 * 2 126679788 rs113949548
413 chr6 75478614 75478614 1 * 6 75478614 rs12211255
414 chr7 150954728 150954728 1 * 7 150954728 rs2269001
415 chr10 67905124 67905124 1 * 10 67905124 rs7096385
416 chr10 76175587 76175587 1 * 10 76175587 rs11001667
417 chr10 63320967 63320967 1 * 10 63320967 rs10822156
418 chr12 24626557 24626557 1 * 12 24626557 rs4963776
419 chr12 132573624 132573624 1 * 12 132573624 rs6560886
420 chr13 113218398 113218398 1 * 13 113218398 rs35569628
421 chr14 23418974 23418974 1 * 14 23418974 rs28631169
422 chr15 63811578 63811578 1 * 15 63811578 rs7170477
423 chr3 179441371 179441371 1 * 3 179441371 rs75880040
424 chr4 102994461 102994461 1 * 4 102994461 rs3960788
425 chr6 87112623 87112623 1 * 6 87112623 rs13210074
426 chr7 28368690 28368690 1 * 7 28368690 rs6948592
427 chr17 78776206 78776206 1 * 17 78776206 rs7224711
428 chr18 51182178 51182178 1 * 18 51182178 rs8088085
429 chr10 101306718 101306718 1 * 10 101306718 rs144361223
430 chr12 111803962 111803962 1 * 12 111803962 rs671
431 chr1 170653968 170653968 1 * 1 170653968 rs588837
432 chr1 203059583 203059583 1 * 1 203059583 rs4590732
433 chr4 110789946 110789946 1 * 4 110789946 rs4540107
434 chr3 179452706 179452706 1 * 3 179452706 rs4855075
435 chr3 69350572 69350572 1 * 3 69350572 rs9310148
436 chr5 114423288 114423288 1 * 5 114423288 rs337684
437 chr5 123120403 123120403 1 * 5 123120403 rs17149944
438 chr5 138070140 138070140 1 * 5 138070140 rs141654122
439 chr5 173247874 173247874 1 * 5 173247874 rs6874428
440 chr6 117208687 117208687 1 * 6 117208687 rs11153653
441 chr6 118370282 118370282 1 * 6 118370282 rs77710920
442 chr6 122068760 122068760 1 * 6 122068760 rs868155
443 chr6 16413394 16413394 1 * 6 16413394 rs7770062
444 chr6 36678191 36678191 1 * 6 36678191 rs730506
445 chr6 87258847 87258847 1 * 6 87258847 rs9362415
446 chr8 123533862 123533862 1 * 8 123533862 rs78332318
447 chr8 140677101 140677101 1 * 8 140677101 rs13268718
448 chr8 17939376 17939376 1 * 8 17939376 rs139743358
449 chr1 10329982 10329982 1 * 1 10329982 rs551033057
450 chr1 111908825 111908825 1 * 1 111908825 rs2120436
451 chr1 115768197 115768197 1 * 1 115768197 rs4484922
452 chr1 154873058 154873058 1 * 1 154873058 rs11264278
453 chr2 178549906 178549906 1 * 2 178549906 rs890578
454 chr2 200306468 200306468 1 * 2 200306468 rs10931898
455 chr2 61138961 61138961 1 * 2 61138961 rs148785604
456 chr2 65056838 65056838 1 * 2 65056838 rs74181299
457 chr2 69918585 69918585 1 * 2 69918585 rs6546558
458 chr3 111873329 111873329 1 * 3 111873329 rs397874511
459 chr14 72894562 72894562 1 * 14 72894562 rs3814864
460 chr19 50399948 50399948 1 * 19 50399948 rs181513970
461 chr22 18114652 18114652 1 * 22 18114652 rs362021
462 chrX 119698761 119698761 1 * X 119698761 rs77806999
463 chrX 138333934 138333934 1 * X 138333934 rs2129742
464 chr15 73375705 73375705 1 * 15 73375705 rs7178084
465 chr17 39910014 39910014 1 * 17 39910014 rs1008723
466 chr8 21946224 21946224 1 * 8 21946224 rs7846485
467 chr8 76948760 76948760 1 * 8 76948760 rs113304312
468 chr10 63229171 63229171 1 * 10 63229171 rs7916868
469 chr10 67504839 67504839 1 * 10 67504839 rs10823051
470 chr11 128898062 128898062 1 * 11 128898062 rs78907918
471 chr11 14014033 14014033 1 * 11 14014033 rs7116230
472 chr11 19989899 19989899 1 * 11 19989899 rs10741807
473 chr12 111730205 111730205 1 * 12 111730205 rs11066015
474 chr12 124016178 124016178 1 * 12 124016178 rs556992087
475 chr12 24609567 24609567 1 * 12 24609567 rs11047527
476 chr12 32837485 32837485 1 * 12 32837485 rs34791177
477 chr12 56709370 56709370 1 * 12 56709370 rs7978685
478 chr12 69675839 69675839 1 * 12 69675839 rs710719
479 chr13 22792608 22792608 1 * 13 22792608 rs9510344
480 chr14 23392602 23392602 1 * 14 23392602 rs365990
481 chr14 32453874 32453874 1 * 14 32453874 rs8011444
482 chr10 76176818 76176818 1 * 10 76176818 rs10458660
483 chr11 121790799 121790799 1 * 11 121790799 rs4935786
484 chr15 57632516 57632516 1 * 15 57632516 rs147301839
485 chr1 154423470 154423470 1 * 1 154423470 rs6689306
486 chr18 51153152 51153152 1 * 18 51153152 rs9963878
487 chr4 110603473 110603473 1 * 4 110603473 rs61501369
488 chr4 111004500 111004500 1 * 4 111004500 rs79399769
489 chr4 111533139 111533139 1 * 4 111533139 rs138311480
490 chr4 112408189 112408189 1 * 4 112408189 rs7687819
491 chr5 173966108 173966108 1 * 5 173966108 rs28439930
492 chr2 25942659 25942659 1 * 2 25942659 rs7578393
493 chr2 145002786 145002786 1 * 2 145002786 rs67969609
494 chr15 73384923 73384923 1 * 15 73384923 rs74022964
495 chr17 39874911 39874911 1 * 17 39874911 rs11658278
496 chr1 147760632 147760632 1 * 1 147760632 rs10465885
497 chr3 38592651 38592651 1 * 3 38592651 rs7374540
498 chr4 110334761 110334761 1 * 4 110334761 rs244017
499 chr4 110675204 110675204 1 * 4 110675204 rs6850025
500 chr4 111244056 111244056 1 * 4 111244056 rs1532170
501 chr4 111683665 111683665 1 * 4 111683665 rs114904067
502 chr4 173526198 173526198 1 * 4 173526198 rs10520260
503 chr10 103517717 103517717 1 * 10 103517717 rs55693294
504 chr12 55662031 55662031 1 * 12 55662031 rs11614818
505 chr12 69677733 69677733 1 * 12 69677733 rs775498
506 chr16 1626803 1626803 1 * 16 1626803 rs118159104
507 chr6 118238495 118238495 1 * 6 118238495 rs3951016
508 chr6 122082413 122082413 1 * 6 122082413 rs13195459
509 chr2 212401279 212401279 1 * 2 212401279 rs35544454
510 chr3 38730434 38730434 1 * 3 38730434 rs6790396
511 chr4 102969823 102969823 1 * 4 102969823 rs10006327
512 chr4 110778529 110778529 1 * 4 110778529 rs67249485
513 chr4 113527500 113527500 1 * 4 113527500 rs6829664
514 chr4 173720033 173720033 1 * 4 173720033 rs12648245
515 chr5 138084300 138084300 1 * 5 138084300 rs2040862
516 chr11 123009573 123009573 1 * 11 123009573 rs12420422
517 chr12 3060327 3060327 1 * 12 3060327 rs12310617
518 chr2 9956965 9956965 1 * 2 9956965 rs16867253
519 chr2 146120964 146120964 1 * 2 146120964 rs222826
520 chr14 92945686 92945686 1 * 14 92945686 rs4905014
521 chr3 123019460 123019460 1 * 3 123019460 rs7632505
522 chr16 72963084 72963084 1 * 16 72963084 rs7190256
523 chr17 7718146 7718146 1 * 17 7718146 rs3803802
524 chr7 19177581 19177581 1 * 7 19177581 rs17140821
525 chr18 8522684 8522684 1 * 18 8522684 rs8082812
526 chr9 22125348 22125348 1 * 9 22125348 rs1333048
527 chr21 41463567 41463567 1 * 21 41463567 rs460976
528 chr10 26969741 26969741 1 * 10 26969741 rs7081476
529 chr10 112996282 112996282 1 * 10 112996282 rs4506565
530 chr1 11792459 11792459 1 * 1 11792459 rs17375901
531 chr4 110787131 110787131 1 * 4 110787131 rs17042171
532 chr1 154841877 154841877 1 * 1 154841877 rs13376333
533 chr20 47796832 47796832 1 * 20 47796832 rs13038095
534 chr19 19296909 19296909 1 * 19 19296909 rs10401969
535 chr19 44919689 44919689 1 * 19 44919689 rs4420638
536 chr19 44744370 44744370 1 * 19 44744370 rs4803750
537 chr1 109275684 109275684 1 * 1 109275684 rs629301
538 chr2 21065449 21065449 1 * 2 21065449 rs562338
539 chr2 21176344 21176344 1 * 2 21176344 rs478442
540 chr2 27263727 27263727 1 * 2 27263727 rs6759518
541 chr2 27412596 27412596 1 * 2 27412596 rs1728918
542 chr2 27518370 27518370 1 * 2 27518370 rs780094
543 chr2 215086907 215086907 1 * 2 215086907 rs940274
544 chr4 102363708 102363708 1 * 4 102363708 rs13114738
545 chr4 110810780 110810780 1 * 4 110810780 rs6533530
546 chr4 139829967 139829967 1 * 4 139829967 rs1869717
547 chr5 75329662 75329662 1 * 5 75329662 rs7703051
548 chr5 75463358 75463358 1 * 5 75463358 rs4704221
549 chr5 75584065 75584065 1 * 5 75584065 rs5744680
550 chr5 75701931 75701931 1 * 5 75701931 rs10057967
551 chr7 73462836 73462836 1 * 7 73462836 rs2074755
552 chr7 73637727 73637727 1 * 7 73637727 rs799165
553 chr11 61803311 61803311 1 * 11 61803311 rs174547
554 chr11 116778201 116778201 1 * 11 116778201 rs964184
555 chr12 89656726 89656726 1 * 12 89656726 rs12579302
556 chr8 20008763 20008763 1 * 8 20008763 rs765547
557 chr8 125466108 125466108 1 * 8 125466108 rs2980853
558 chr9 22125504 22125504 1 * 9 22125504 rs1333049
559 chr20 41147406 41147406 1 * 20 41147406 rs760762
560 chr20 41322165 41322165 1 * 20 41322165 rs2866611
561 chr11 117037567 117037567 1 * 11 117037567 rs7115242
562 chr12 122479003 122479003 1 * 12 122479003 rs12369179
563 chr15 58435126 58435126 1 * 15 58435126 rs261332
564 chr16 53786615 53786615 1 * 16 53786615 rs9939609
565 chr16 56956804 56956804 1 * 16 56956804 rs247617
566 chr1 154841792 154841792 1 * 1 154841792 rs6666258
567 chr7 116546187 116546187 1 * 7 116546187 rs3807989
568 chr14 64214130 64214130 1 * 14 64214130 rs1152591
569 chr15 73359833 73359833 1 * 15 73359833 rs7164883
570 chr11 128894676 128894676 1 * 11 128894676 rs75190942
571 chr15 57351688 57351688 1 * 15 57351688 rs2921421
572 chr6 122142045 122142045 1 * 6 122142045 rs12664873
573 chr15 73376665 73376665 1 * 15 73376665 rs7183206
574 chr4 110767596 110767596 1 * 4 110767596 rs2723334
575 chr10 73661890 73661890 1 * 10 73661890 rs7394190
576 chr10 103562124 103562124 1 * 10 103562124 rs60848348
577 chr16 73025260 73025260 1 * 16 73025260 rs4499262
578 chr2 69811100 69811100 1 * 2 69811100 rs6546550
579 chr12 32820006 32820006 1 * 12 32820006 rs1454934
580 chr1 154845927 154845927 1 * 1 154845927 rs36004974
581 chr1 170622169 170622169 1 * 1 170622169 rs651386
582 chr4 110791276 110791276 1 * 4 110791276 rs2129977
583 chr9 94730238 94730238 1 * 9 94730238 rs7026071
584 chr5 114412874 114412874 1 * 5 114412874 rs337711
585 chr1 170669192 170669192 1 * 1 170669192 rs520525
586 chr7 116558774 116558774 1 * 7 116558774 rs1997572
587 chr1 170216500 170216500 1 * 1 170216500 rs10800507
588 chr2 69749252 69749252 1 * 2 69749252 rs62133983
589 chr5 137912251 137912251 1 * 5 137912251 rs6864727
590 chr6 118252898 118252898 1 * 6 118252898 rs281868
591 chr4 110789386 110789386 1 * 4 110789386 rs61303432
592 chr7 116519907 116519907 1 * 7 116519907 rs2109514
593 chr6 34272799 34272799 1 * 6 34272799 rs1307274
594 chr6 122070990 122070990 1 * 6 122070990 rs13191450
595 chr4 110733185 110733185 1 * 4 110733185 rs143269342
596 chr1 111919280 111919280 1 * 1 111919280 rs1443926
597 chr15 63512308 63512308 1 * 15 63512308 rs146311723
598 chr4 110859850 110859850 1 * 4 110859850 rs149829837
599 chr6 118245024 118245024 1 * 6 118245024 rs17079881
600 chr5 143270839 143270839 1 * 5 143270839 rs174048
601 chr3 111869032 111869032 1 * 3 111869032 rs17490701
602 chr7 116514132 116514132 1 * 7 116514132 rs17516287
603 chr4 110815726 110815726 1 * 4 110815726 rs17570669
604 chr11 19988967 19988967 1 * 11 19988967 rs1822273
605 chr1 10107367 10107367 1 * 1 10107367 rs187585530
606 chr6 117559179 117559179 1 * 6 117559179 rs210632
607 chr7 28373568 28373568 1 * 7 28373568 rs6462078
608 chr10 73660356 73660356 1 * 10 73660356 rs6480708
609 chr2 69884579 69884579 1 * 2 69884579 rs6546553
610 chr2 61541610 61541610 1 * 2 61541610 rs6742276
611 chr3 12799435 12799435 1 * 3 12799435 rs6810325
612 chr4 110775495 110775495 1 * 4 110775495 rs6847935
613 chr6 87146285 87146285 1 * 6 87146285 rs6907805
614 chr8 140752560 140752560 1 * 8 140752560 rs6993266
615 chr5 114400719 114400719 1 * 5 114400719 rs716845
616 chr17 70341044 70341044 1 * 17 70341044 rs7219869
617 chr20 62560732 62560732 1 * 20 62560732 rs7269123
618 chr22 18117816 18117816 1 * 22 18117816 rs465276
619 chr9 106870072 106870072 1 * 9 106870072 rs4743034
620 chr3 179450674 179450674 1 * 3 179450674 rs4855074
621 chr1 205748695 205748695 1 * 1 205748695 rs4951261
622 chr1 170665943 170665943 1 * 1 170665943 rs503706
623 chr1 170648165 170648165 1 * 1 170648165 rs608930
624 chr15 63507814 63507814 1 * 15 63507814 rs62011291
625 chr7 107215557 107215557 1 * 7 107215557 rs62483627
626 chr3 111873991 111873991 1 * 3 111873991 rs73228543
627 chr8 134800173 134800173 1 * 8 134800173 rs7460121
628 chr7 74696373 74696373 1 * 7 74696373 rs74910854
629 chr4 110737238 110737238 1 * 4 110737238 rs75021220
630 chr6 36677811 36677811 1 * 6 36677811 rs762624
631 chr3 89485227 89485227 1 * 3 89485227 rs7632427
632 chr3 196767831 196767831 1 * 3 196767831 rs9872035
633 chr8 124847575 124847575 1 * 8 124847575 rs35006907
634 chr2 178556567 178556567 1 * 2 178556567 rs35504893
635 chr12 123962792 123962792 1 * 12 123962792 rs3789967
636 chr4 10117121 10117121 1 * 4 10117121 rs3822259
637 chr17 70351197 70351197 1 * 17 70351197 rs3844438
638 chr8 140736225 140736225 1 * 8 140736225 rs4355822
639 chr9 94886305 94886305 1 * 9 94886305 rs4385527
640 chr16 1955981 1955981 1 * 16 1955981 rs30252
641 chr3 66361431 66361431 1 * 3 66361431 rs332388
642 chr1 154839924 154839924 1 * 1 154839924 rs34292822
643 chr5 138098483 138098483 1 * 5 138098483 rs34750263
644 chr2 200330879 200330879 1 * 2 200330879 rs295114
645 chr12 69703684 69703684 1 * 12 69703684 rs35349325
646 chr17 46969002 46969002 1 * 17 46969002 rs76774446
647 chr10 63556040 63556040 1 * 10 63556040 rs7919685
648 chr13 22797335 22797335 1 * 13 22797335 rs7987944
649 chr17 7531723 7531723 1 * 17 7531723 rs8073937
650 chr14 76961126 76961126 1 * 14 76961126 rs8181996
651 chr11 121758299 121758299 1 * 11 121758299 rs949078
652 chr13 22799267 22799267 1 * 13 22799267 rs9580438
653 chr17 7516977 7516977 1 * 17 7516977 rs9675122
654 chr4 148025539 148025539 1 * 4 148025539 rs10027347
655 chr7 836590 836590 1 * 7 836590 rs11768850
656 chr10 101845957 101845957 1 * 10 101845957 rs1044258
657 chr5 138052751 138052751 1 * 5 138052751 rs10479177
658 chr1 170224718 170224718 1 * 1 170224718 rs12122060
659 chr1 170724397 170724397 1 * 1 170724397 rs12142379
660 chr12 123934127 123934127 1 * 12 123934127 rs12298484
661 chr14 76960368 76960368 1 * 14 76960368 rs10873299
662 chr12 75830037 75830037 1 * 12 75830037 rs11180703
663 chr12 114653212 114653212 1 * 12 114653212 rs12810346
664 chr15 98744146 98744146 1 * 15 98744146 rs12908437
665 chr2 69889883 69889883 1 * 2 69889883 rs10165883
666 chr7 92655809 92655809 1 * 7 92655809 rs11773884
667 chr14 34717488 34717488 1 * 14 34717488 rs11846704
668 chr9 124415987 124415987 1 * 9 124415987 rs10760361
669 chr1 154451288 154451288 1 * 1 154451288 rs12129500
670 chr10 102230055 102230055 1 * 10 102230055 rs10786662
671 chr6 133153164 133153164 1 * 6 133153164 rs12208899
672 chr15 70161800 70161800 1 * 15 70161800 rs12591736
673 chr2 148035096 148035096 1 * 2 148035096 rs12992412
674 chr20 6591367 6591367 1 * 20 6591367 rs2145274
675 chr14 32512278 32512278 1 * 14 32512278 rs2145587
676 chr3 66384219 66384219 1 * 3 66384219 rs2306272
677 chr2 61548229 61548229 1 * 2 61548229 rs2441380
678 chr4 110631977 110631977 1 * 4 110631977 rs2595104
679 chr11 19988805 19988805 1 * 11 19988805 rs2625322
680 chr10 116816095 116816095 1 * 10 116816095 rs740363
681 chr6 160589086 160589086 1 * 6 160589086 rs10455872
682 chr4 45180510 45180510 1 * 4 45180510 rs10938397
683 chr18 23567545 23567545 1 * 18 23567545 rs1652348
684 chr18 60067625 60067625 1 * 18 60067625 rs7234864
685 chr16 53765595 53765595 1 * 16 53765595 rs9937053
686 chr4 110783043 110783043 1 * 4 110783043 rs2129981
687 chr12 42859612 42859612 1 * 12 42859612 rs1520832
688 chr2 126905321 126905321 1 * 2 126905321 rs13418717
689 chr9 18109237 18109237 1 * 9 18109237 rs2210327
690 chr12 29951209 29951209 1 * 12 29951209 rs2046383
691 chr12 91911494 91911494 1 * 12 91911494 rs17019682
692 chr15 63445726 63445726 1 * 15 63445726 rs10519210
693 chr7 27290437 27290437 1 * 7 27290437 rs13225783
694 chr9 27533986 27533986 1 * 9 27533986 rs10812610
695 chr13 75202132 75202132 1 * 13 75202132 rs548097
696 chr19 3159771 3159771 1 * 19 3159771 rs11880198
697 chr12 58865846 58865846 1 * 12 58865846 rs11172782
698 chr8 82756885 82756885 1 * 8 82756885 rs6473383
699 chr10 89204857 89204857 1 * 10 89204857 rs11203032
700 chr11 126158822 126158822 1 * 11 126158822 rs563519
701 chr1 220855166 220855166 1 * 1 220855166 rs11118620
702 chr3 165562421 165562421 1 * 3 165562421 rs1523288
703 chr9 95085566 95085566 1 * 9 95085566 rs137908951
704 chr12 115118502 115118502 1 * 12 115118502 rs35427
705 chr4 110776497 110776497 1 * 4 110776497 rs78229461
706 chr6 22569405 22569405 1 * 6 22569405 rs2073030
707 chr6 36665292 36665292 1 * 6 36665292 rs4713999
708 chr9 133276354 133276354 1 * 9 133276354 rs600038
709 chr9 22122061 22122061 1 * 9 22122061 rs35831924
710 chr15 33904646 33904646 1 * 15 33904646 rs187108425
711 chr16 73014468 73014468 1 * 16 73014468 rs67329386
712 chr18 58252666 58252666 1 * 18 58252666 rs11660748
713 chr10 73657491 73657491 1 * 10 73657491 rs4746140
714 chr1 6219310 6219310 1 * 1 6219310 rs846111
715 chr3 14232793 14232793 1 * 3 14232793 rs56281979
716 chr2 178898903 178898903 1 * 2 178898903 rs7564756
717 chr12 111446804 111446804 1 * 12 111446804 rs3184504
718 chr17 66311864 66311864 1 * 17 66311864 rs4328478
719 chr7 128846309 128846309 1 * 7 128846309 rs34373805
720 chr12 26195496 26195496 1 * 12 26195496 rs113819537
721 chr20 33701957 33701957 1 * 20 33701957 rs57668191
722 chr14 89416793 89416793 1 * 14 89416793 rs71415423
723 chr10 119532469 119532469 1 * 10 119532469 rs148802390
724 chr18 58289633 58289633 1 * 18 58289633 rs10871753
725 chr1 15804829 15804829 1 * 1 15804829 rs113151268
726 chr2 178882341 178882341 1 * 2 178882341 rs142556838
727 chr2 178846649 178846649 1 * 2 178846649 rs2220127
728 chr3 134736952 134736952 1 * 3 134736952 rs13092177
729 chr2 71451390 71451390 1 * 2 71451390 rs4852257
730 chr10 119696417 119696417 1 * 10 119696417 rs11199073
731 chr3 14376944 14376944 1 * 3 14376944 rs34234056
732 chr1 6188122 6188122 1 * 1 6188122 rs114300540
733 chr6 32668263 32668263 1 * 6 32668263 rs9274626
734 chr3 49173299 49173299 1 * 3 49173299 rs7617480
735 chr17 45949373 45949373 1 * 17 45949373 rs242562
736 chr12 115117725 115117725 1 * 12 115117725 rs35432
737 chr17 46969002 46969002 1 * 17 46969002 rs76774446
738 chr6 118346359 118346359 1 * 6 118346359 rs11153730
739 chr3 158569666 158569666 1 * 3 158569666 rs2276773
740 chr17 1466275 1466275 1 * 17 1466275 rs8069650
741 chr10 119667597 119667597 1 * 10 119667597 rs196321
742 chr16 2103932 2103932 1 * 16 2103932 rs9938566
743 chr8 140625230 140625230 1 * 8 140625230 rs1962104
744 chr19 41439932 41439932 1 * 19 41439932 rs13346603
745 chr1 45555123 45555123 1 * 1 45555123 rs666720
746 chr2 36922355 36922355 1 * 2 36922355 rs11124554
747 chr5 139426616 139426616 1 * 5 139426616 rs11242465
748 chr7 128825592 128825592 1 * 7 128825592 rs57573379
749 chr4 16027243 16027243 1 * 4 16027243 rs1850507
750 chr15 84145450 84145450 1 * 15 84145450 rs4842937
751 chr16 940791 940791 1 * 16 940791 rs12598405
752 chr17 55297249 55297249 1 * 17 55297249 rs12452367
753 chr6 54163271 54163271 1 * 6 54163271 rs6915002
754 chr19 45812551 45812551 1 * 19 45812551 rs10421891
755 chr1 236688982 236688982 1 * 1 236688982 rs12724121
756 chr2 200315300 200315300 1 * 2 200315300 rs3820888
757 chr15 84806000 84806000 1 * 15 84806000 rs35630683
758 chr8 124847608 124847608 1 * 8 124847608 rs34866937
759 chr22 23836092 23836092 1 * 22 23836092 rs5760061
760 chr16 53794154 53794154 1 * 16 53794154 rs17817964
761 chr6 36677811 36677811 1 * 6 36677811 rs762624
762 chr17 1370588 1370588 1 * 17 1370588 rs117510670
763 chr2 178975161 178975161 1 * 2 178975161 rs10497529
764 chr2 178888822 178888822 1 * 2 178888822 rs1873164
765 chr10 119611816 119611816 1 * 10 119611816 rs11594596
766 chr3 14250179 14250179 1 * 3 14250179 rs11710541
767 chr1 16021917 16021917 1 * 1 16021917 rs945425
768 chr2 178649706 178649706 1 * 2 178649706 rs2562845
769 chr4 110787848 110787848 1 * 4 110787848 rs1906592
770 chr10 119656173 119656173 1 * 10 119656173 rs72840788
771 chr11 43607199 43607199 1 * 11 43607199 rs4755720
772 chr6 22598030 22598030 1 * 6 22598030 rs7766436
773 chr2 632592 632592 1 * 2 632592 rs12992672
774 chr12 124824136 124824136 1 * 12 124824136 rs10846742
775 chr4 113463172 113463172 1 * 4 113463172 rs17620390
776 chr1 50281325 50281325 1 * 1 50281325 rs72688573
777 chr4 45184122 45184122 1 * 4 45184122 rs10938398
778 chr7 75470858 75470858 1 * 7 75470858 rs6945340
779 chr2 144500878 144500878 1 * 2 144500878 rs7564469
780 chr12 106865692 106865692 1 * 12 106865692 rs7977247
781 chr2 59078490 59078490 1 * 2 59078490 rs1016287
782 chr14 29700781 29700781 1 * 14 29700781 rs959388
783 chr4 102291689 102291689 1 * 4 102291689 rs233806
784 chr2 37006122 37006122 1 * 2 37006122 rs17038861
785 chr6 79075890 79075890 1 * 6 79075890 rs9352691
786 chr19 45824573 45824573 1 * 19 45824573 rs10520390
787 chr1 66524036 66524036 1 * 1 66524036 rs79682748
788 chr9 22102166 22102166 1 * 9 22102166 rs7859727
789 chr4 110748064 110748064 1 * 4 110748064 rs2634071
790 chr16 53768582 53768582 1 * 16 53768582 rs11642015
791 chr6 36679512 36679512 1 * 6 36679512 rs3176326
792 chr1 109278889 109278889 1 * 1 109278889 rs602633
793 chr1 16004613 16004613 1 * 1 16004613 rs1739833
794 chr10 119667372 119667372 1 * 10 119667372 rs17617337
795 chr10 73647154 73647154 1 * 10 73647154 rs34163229
796 chr17 67840105 67840105 1 * 17 67840105 rs113437066
797 chr5 137671073 137671073 1 * 5 137671073 rs11746435
798 chr21 29230673 29230673 1 * 21 29230673 rs2832275
799 chr7 74708526 74708526 1 * 7 74708526 rs7795282
800 chr16 69532406 69532406 1 * 16 69532406 rs12933292
801 chr17 2297577 2297577 1 * 17 2297577 rs216199
802 chr12 111762346 111762346 1 * 12 111762346 rs2013002
803 chr1 222632876 222632876 1 * 1 222632876 rs17163345
804 chr17 39668086 39668086 1 * 17 39668086 rs3764351
805 chr6 12903725 12903725 1 * 6 12903725 rs9349379
806 chr18 38953012 38953012 1 * 18 38953012 rs4327120
807 chr11 123009573 123009573 1 * 11 123009573 rs12420422
808 chr12 3060327 3060327 1 * 12 3060327 rs12310617
809 chr2 9956965 9956965 1 * 2 9956965 rs16867253
810 chr2 146120964 146120964 1 * 2 146120964 rs222826
811 chr14 92945686 92945686 1 * 14 92945686 rs4905014
812 chr3 123019460 123019460 1 * 3 123019460 rs7632505
813 chr16 72963084 72963084 1 * 16 72963084 rs7190256
814 chr17 7718146 7718146 1 * 17 7718146 rs3803802
815 chr7 19177581 19177581 1 * 7 19177581 rs17140821
816 chr18 8522684 8522684 1 * 18 8522684 rs8082812
817 chr9 22125348 22125348 1 * 9 22125348 rs1333048
818 chr21 41463567 41463567 1 * 21 41463567 rs460976
819 chr10 26969741 26969741 1 * 10 26969741 rs7081476
820 chr10 112996282 112996282 1 * 10 112996282 rs4506565
821 chr22 22522600 22522600 1 * 22 22522600 rs361894
822 chr22 22521228 22521228 1 * 22 22521228 rs362079
823 chr6 22571185 22571185 1 * 6 22571185 rs3734214
824 chr2 86536881 86536881 1 * 2 86536881 rs4832298
825 chr6 160584578 160584578 1 * 6 160584578 rs55730499
826 chr16 53772541 53772541 1 * 16 53772541 rs56094641
827 chr7 75432493 75432493 1 * 7 75432493 rs6944634
828 chr2 630075 630075 1 * 2 630075 rs73139123
829 chr2 36920832 36920832 1 * 2 36920832 rs7605601
830 chr4 110843816 110843816 1 * 4 110843816 rs7680240
831 chr11 43612095 43612095 1 * 11 43612095 rs7936836
832 chr1 51332122 51332122 1 * 1 51332122 rs80061532
833 chr6 78635532 78635532 1 * 6 78635532 rs9361413
834 chr4 110707472 110707472 1 * 4 110707472 rs981150
835 chr9 22025494 22025494 1 * 9 22025494 rs10738604
836 chr12 111395984 111395984 1 * 12 111395984 rs10774624
837 chr12 112172910 112172910 1 * 12 112172910 rs11066188
838 chr13 18618893 18618893 1 * 13 18618893 rs114352564
839 chr1 50509555 50509555 1 * 1 50509555 rs116626164
840 chr5 137676482 137676482 1 * 5 137676482 rs11745324
841 chr7 144116260 144116260 1 * 7 144116260 rs117540300
842 chr16 72972675 72972675 1 * 16 72972675 rs12325072
843 chr18 60099280 60099280 1 * 18 60099280 rs1539952
844 chr14 34878306 34878306 1 * 14 34878306 rs1712355
845 chr4 110927114 110927114 1 * 4 110927114 rs17513625
846 chr10 18226070 18226070 1 * 10 18226070 rs1757223
847 chr18 23574060 23574060 1 * 18 23574060 rs1788826
848 chr4 110665822 110665822 1 * 4 110665822 rs1823290
849 chr16 53814649 53814649 1 * 16 53814649 rs1861867
850 chr1 61420374 61420374 1 * 1 61420374 rs1997997
851 chr17 2300159 2300159 1 * 17 2300159 rs216193
852 chr7 92635679 92635679 1 * 7 92635679 rs2282979
853 chr17 78802073 78802073 1 * 17 78802073 rs2306527
854 chr1 16021039 16021039 1 * 1 16021039 rs28579893
855 chr18 33675954 33675954 1 * 18 33675954 rs34728432
856 chr7 74720592 74720592 1 * 7 74720592 rs35005436
857 chr18 7165316 7165316 1 * 18 7165316 rs76345468
858 chr22 46417046 46417046 1 * 22 46417046 rs190258023
859 chr18 7165736 7165736 1 * 18 7165736 rs147545594
860 chr8 63615955 63615955 1 * 8 63615955 rs187251765
861 chr2 225229677 225229677 1 * 2 225229677 rs111641830
862 chr12 119704045 119704045 1 * 12 119704045 rs371848093
863 chr7 6499355 6499355 1 * 7 6499355 rs142659860
864 chr12 75899702 75899702 1 * 12 75899702 rs115146744
865 chr7 6508129 6508129 1 * 7 6508129 rs556723179
866 chr15 75879549 75879549 1 * 15 75879549 rs76806081
867 chr12 75895517 75895517 1 * 12 75895517 rs114782882
868 chr16 50121093 50121093 1 * 16 50121093 rs552214848
869 chr11 824293 824293 1 * 11 824293 rs114512805
870 chr16 87929642 87929642 1 * 16 87929642 rs139731147
871 chr16 24501229 24501229 1 * 16 24501229 rs116598880
872 chr19 32170310 32170310 1 * 19 32170310 rs76302892
873 chr12 89962714 89962714 1 * 12 89962714 rs113516553
874 chr12 89965597 89965597 1 * 12 89965597 rs113983785
875 chr12 89966409 89966409 1 * 12 89966409 rs111371067
876 chr5 166138903 166138903 1 * 5 166138903 rs75729550
877 chr13 73906571 73906571 1 * 13 73906571 rs146264611
878 chr3 189573162 189573162 1 * 3 189573162 rs144563425
879 chr6 16264087 16264087 1 * 6 16264087 rs149649230
880 chr18 7162907 7162907 1 * 18 7162907 rs74972015
881 chr10 77152423 77152423 1 * 10 77152423 rs79087352
882 chr6 36799290 36799290 1 * 6 36799290 rs9470398
883 chr18 7165714 7165714 1 * 18 7165714 rs75262741
884 chr22 46417215 46417215 1 * 22 46417215 rs148416395
885 chr2 225227882 225227882 1 * 2 225227882 rs189536067
886 chr5 170374105 170374105 1 * 5 170374105 rs144322502
887 chr2 225229816 225229816 1 * 2 225229816 rs112372754
888 chr1 18783514 18783514 1 * 1 18783514 rs113459855
889 chr7 6501383 6501383 1 * 7 6501383 rs73059342
890 chr5 166124695 166124695 1 * 5 166124695 rs114101629
891 chr1 226505234 226505234 1 * 1 226505234 rs143554223
892 chr1 226518546 226518546 1 * 1 226518546 rs148467525
893 chr16 13710372 13710372 1 * 16 13710372 rs28523422
894 chr1 18783980 18783980 1 * 1 18783980 rs74056619
895 chr1 18784033 18784033 1 * 1 18784033 rs74056620
896 chr1 18784068 18784068 1 * 1 18784068 rs74056621
897 chr1 18784160 18784160 1 * 1 18784160 rs74056622
898 chr4 114106683 114106683 1 * 4 114106683 rs115982993
899 chr11 118548722 118548722 1 * 11 118548722 rs111657631
900 chr3 189563329 189563329 1 * 3 189563329 rs189566544
901 chr11 121940094 121940094 1 * 11 121940094 rs143694932
902 chr12 89967845 89967845 1 * 12 89967845 rs138517179
903 chr20 59333413 59333413 1 * 20 59333413 rs138005219
904 chr6 15880941 15880941 1 * 6 15880941 rs116116894
905 chr12 84365999 84365999 1 * 12 84365999 rs146219909
906 chr2 4101751 4101751 1 * 2 4101751 rs112901026
907 chr22 49158345 49158345 1 * 22 49158345 rs6009185
908 chr5 31695670 31695670 1 * 5 31695670 rs372344
909 chr7 101886632 101886632 1 * 7 101886632 rs10234809
910 chr7 123038013 123038013 1 * 7 123038013 rs111681691
911 chr12 18903336 18903336 1 * 12 18903336 rs8181669
912 chr12 18903960 18903960 1 * 12 18903960 rs1490716
913 chr16 76851168 76851168 1 * 16 76851168 rs34141129
914 chr10 11818334 11818334 1 * 10 11818334 rs58829444
915 chr16 13889638 13889638 1 * 16 13889638 rs13338660
916 chr16 13894865 13894865 1 * 16 13894865 rs9924452
917 chr16 13894782 13894782 1 * 16 13894782 rs7184192
918 chr1 62981186 62981186 1 * 1 62981186 rs72671743
919 chr21 27242552 27242552 1 * 21 27242552 rs1477717
920 chr16 13892870 13892870 1 * 16 13892870 rs1364363
921 chr5 31694898 31694898 1 * 5 31694898 rs1678921
922 chr16 13891137 13891137 1 * 16 13891137 rs10163219
923 chr16 13892198 13892198 1 * 16 13892198 rs9927170
924 chr3 196256232 196256232 1 * 3 196256232 rs56107869
925 chr3 196256235 196256235 1 * 3 196256235 rs56297497
926 chr8 95439207 95439207 1 * 8 95439207 rs74864598
927 chr8 95439600 95439600 1 * 8 95439600 rs16917667
928 chr19 32103916 32103916 1 * 19 32103916 rs8105292
929 chr1 184396836 184396836 1 * 1 184396836 rs61823501
930 chr10 59510886 59510886 1 * 10 59510886 rs11006544
931 chr12 99694540 99694540 1 * 12 99694540 rs11110004
932 chr4 38234363 38234363 1 * 4 38234363 rs78829380
933 chr12 75900959 75900959 1 * 12 75900959 rs149765481
934 chr6 22177692 22177692 1 * 6 22177692 rs182178320
935 chr7 47398239 47398239 1 * 7 47398239 rs192154334
936 chr19 32137461 32137461 1 * 19 32137461 rs115709306
937 chr19 32143298 32143298 1 * 19 32143298 rs78705027
938 chr19 32152628 32152628 1 * 19 32152628 rs116175387
939 chr2 172769257 172769257 1 * 2 172769257 rs115472750
940 chr16 87973047 87973047 1 * 16 87973047 rs116213227
941 chr18 79553190 79553190 1 * 18 79553190 rs188748322
942 chr16 87967762 87967762 1 * 16 87967762 rs114700275
943 chr16 87971983 87971983 1 * 16 87971983 rs114908471
944 chr5 141806037 141806037 1 * 5 141806037 rs17097649
945 chr5 141809067 141809067 1 * 5 141809067 rs17097676
946 chr5 101495996 101495996 1 * 5 101495996 rs113510721
947 chr5 101496433 101496433 1 * 5 101496433 rs28806579
948 chr1 165408366 165408366 1 * 1 165408366 rs116521297
949 chr1 165408879 165408879 1 * 1 165408879 rs78093250
950 chr16 87949887 87949887 1 * 16 87949887 rs138575291
951 chr5 166127270 166127270 1 * 5 166127270 rs74956835
952 chr13 113804866 113804866 1 * 13 113804866 rs77095672
953 chr13 113804983 113804983 1 * 13 113804983 rs56032548
954 chr16 720886 720886 1 * 16 720886 rs76064792
955 chr4 22625658 22625658 1 * 4 22625658 rs112577387
956 chr4 22630338 22630338 1 * 4 22630338 rs73123536
957 chr14 24328255 24328255 1 * 14 24328255 rs2092866
958 chr21 14119015 14119015 1 * 21 14119015 rs57346421
959 chr21 14120037 14120037 1 * 21 14120037 rs55798126
960 chr21 14124936 14124936 1 * 21 14124936 rs56337324
961 chr21 14126271 14126271 1 * 21 14126271 rs78528733
962 chr21 14131214 14131214 1 * 21 14131214 rs73894141
963 chr21 14131694 14131694 1 * 21 14131694 rs73894142
964 chr22 46422493 46422493 1 * 22 46422493 rs535263906
965 chr6 14453908 14453908 1 * 6 14453908 rs149447933
966 chr5 30425422 30425422 1 * 5 30425422 rs541284506
967 chr5 33083283 33083283 1 * 5 33083283 rs112434206
968 chr5 166089843 166089843 1 * 5 166089843 rs114821210
969 chr1 18784584 18784584 1 * 1 18784584 rs74056623
970 chr16 24584678 24584678 1 * 16 24584678 rs148133894
971 chr3 134436825 134436825 1 * 3 134436825 rs189919070
972 chr1 18790006 18790006 1 * 1 18790006 rs74056624
973 chr16 87925065 87925065 1 * 16 87925065 rs149322277
974 chr1 18783262 18783262 1 * 1 18783262 rs188344082
975 chr22 46423108 46423108 1 * 22 46423108 rs150381023
976 chr22 46429532 46429532 1 * 22 46429532 rs150109621
977 chr6 14411553 14411553 1 * 6 14411553 rs139130723
978 chr6 14420151 14420151 1 * 6 14420151 rs142803096
979 chr18 48509413 48509413 1 * 18 48509413 rs144303414
980 chr1 236396997 236396997 1 * 1 236396997 rs78133413
981 chr1 224153647 224153647 1 * 1 224153647 rs113737900
982 chr5 30869242 30869242 1 * 5 30869242 rs77506079
983 chr12 62675917 62675917 1 * 12 62675917 rs76392993
984 chr6 129963201 129963201 1 * 6 129963201 rs17757727
985 chr21 42637597 42637597 1 * 21 42637597 rs139489372
986 chr5 121264302 121264302 1 * 5 121264302 rs79031501
987 chr5 121265711 121265711 1 * 5 121265711 rs965460
988 chr5 121270537 121270537 1 * 5 121270537 rs114726259
989 chr7 104595243 104595243 1 * 7 104595243 rs143054558
990 chr8 108747025 108747025 1 * 8 108747025 rs62509389
991 chr8 108762441 108762441 1 * 8 108762441 rs62509394
992 chr7 104670969 104670969 1 * 7 104670969 rs190116644
993 chr7 4549001 4549001 1 * 7 4549001 rs11766034
994 chr16 13893862 13893862 1 * 16 13893862 rs6498482
995 chr16 50120769 50120769 1 * 16 50120769 rs79272715
996 chr8 95440624 95440624 1 * 8 95440624 rs1392797
997 chr8 95455121 95455121 1 * 8 95455121 rs78897914
998 chr8 95457559 95457559 1 * 8 95457559 rs16917715
999 chr8 95454820 95454820 1 * 8 95454820 rs116454494
1000 chr16 13895264 13895264 1 * 16 13895264 rs7188980
1001 chr16 78933297 78933297 1 * 16 78933297 rs7198756
1002 chr1 121213648 121213648 1 * 1 121213648 rs587606498
1003 chr22 27571691 27571691 1 * 22 27571691 rs5752592
1004 chr22 27568401 27568401 1 * 22 27568401 rs28580426
1005 chr12 75962050 75962050 1 * 12 75962050 rs7965830
1006 chr13 27021678 27021678 1 * 13 27021678 rs61945053
1007 chr7 6163445 6163445 1 * 7 6163445 rs78314028
1008 chr11 98834502 98834502 1 * 11 98834502 rs12362161
1009 chr14 31849939 31849939 1 * 14 31849939 rs113235453
1010 chr2 23527771 23527771 1 * 2 23527771 rs1709294
1011 chr5 8543925 8543925 1 * 5 8543925 rs1700575
1012 chr6 42088268 42088268 1 * 6 42088268 rs79661299
1013 chr8 3620814 3620814 1 * 8 3620814 rs1600857
1014 chr12 104992867 104992867 1 * 12 104992867 rs4331189
1015 chr12 104959466 104959466 1 * 12 104959466 rs4075503
1016 chr5 2655665 2655665 1 * 5 2655665 rs16870234
1017 chr5 103642776 103642776 1 * 5 103642776 rs75087282
1018 chr12 107654295 107654295 1 * 12 107654295 rs28548659
1019 chr4 11262289 11262289 1 * 4 11262289 rs782760
1020 chr3 32484661 32484661 1 * 3 32484661 rs367841
1021 chr20 52069476 52069476 1 * 20 52069476 rs6013374
1022 chr19 57376380 57376380 1 * 19 57376380 rs189508091
1023 chr12 29535269 29535269 1 * 12 29535269 rs299453
1024 chr12 104806310 104806310 1 * 12 104806310 rs9737956
1025 chr3 175704057 175704057 1 * 3 175704057 rs6773175
1026 chr3 109782466 109782466 1 * 3 109782466 rs664669
1027 chr8 21741725 21741725 1 * 8 21741725 rs112455636
1028 chr10 77684995 77684995 1 * 10 77684995 rs4979906
1029 chr3 32447042 32447042 1 * 3 32447042 rs12638540
1030 chr19 14240762 14240762 1 * 19 14240762 rs4528684
1031 chr4 175937875 175937875 1 * 4 175937875 rs7687921
1032 chr14 90213566 90213566 1 * 14 90213566 rs8017423
1033 chr12 131378358 131378358 1 * 12 131378358 rs7965445
1034 chr15 31537504 31537504 1 * 15 31537504 rs2125623
1035 chr11 12447850 12447850 1 * 11 12447850 rs7120489
1036 chr5 33636489 33636489 1 * 5 33636489 rs6868223
1037 chr1 221378197 221378197 1 * 1 221378197 rs12733856
1038 chr7 112446278 112446278 1 * 7 112446278 rs17159640
1039 chr19 19296909 19296909 1 * 19 19296909 rs10401969
1040 chr19 44919689 44919689 1 * 19 44919689 rs4420638
1041 chr19 44744370 44744370 1 * 19 44744370 rs4803750
1042 chr1 109275684 109275684 1 * 1 109275684 rs629301
1043 chr2 21065449 21065449 1 * 2 21065449 rs562338
1044 chr2 21176344 21176344 1 * 2 21176344 rs478442
1045 chr2 27263727 27263727 1 * 2 27263727 rs6759518
1046 chr2 27412596 27412596 1 * 2 27412596 rs1728918
1047 chr2 27518370 27518370 1 * 2 27518370 rs780094
1048 chr2 215086907 215086907 1 * 2 215086907 rs940274
1049 chr4 102363708 102363708 1 * 4 102363708 rs13114738
1050 chr4 110810780 110810780 1 * 4 110810780 rs6533530
1051 chr4 139829967 139829967 1 * 4 139829967 rs1869717
1052 chr5 75329662 75329662 1 * 5 75329662 rs7703051
1053 chr5 75463358 75463358 1 * 5 75463358 rs4704221
1054 chr5 75584065 75584065 1 * 5 75584065 rs5744680
1055 chr5 75701931 75701931 1 * 5 75701931 rs10057967
1056 chr7 73462836 73462836 1 * 7 73462836 rs2074755
1057 chr7 73637727 73637727 1 * 7 73637727 rs799165
1058 chr11 61803311 61803311 1 * 11 61803311 rs174547
1059 chr11 116778201 116778201 1 * 11 116778201 rs964184
1060 chr12 89656726 89656726 1 * 12 89656726 rs12579302
1061 chr8 20008763 20008763 1 * 8 20008763 rs765547
1062 chr8 125466108 125466108 1 * 8 125466108 rs2980853
1063 chr9 22125504 22125504 1 * 9 22125504 rs1333049
1064 chr20 41147406 41147406 1 * 20 41147406 rs760762
1065 chr20 41322165 41322165 1 * 20 41322165 rs2866611
1066 chr11 117037567 117037567 1 * 11 117037567 rs7115242
1067 chr12 122479003 122479003 1 * 12 122479003 rs12369179
1068 chr15 58435126 58435126 1 * 15 58435126 rs261332
1069 chr16 53786615 53786615 1 * 16 53786615 rs9939609
1070 chr16 56956804 56956804 1 * 16 56956804 rs247617
1071 chr1 109275216 109275216 1 * 1 109275216 rs660240
1072 chr4 110747470 110747470 1 * 4 110747470 rs17042102
1073 chr6 36679903 36679903 1 * 6 36679903 rs4135240
1074 chr6 160591981 160591981 1 * 6 160591981 rs140570886
1075 chr9 22100177 22100177 1 * 9 22100177 rs1556516
1076 chr12 111466567 111466567 1 * 12 111466567 rs4766578
1077 chr9 95006476 95006476 1 * 9 95006476 rs147288039
1078 chr16 72991194 72991194 1 * 16 72991194 rs61208973
1079 chr9 22124141 22124141 1 * 9 22124141 rs7857118
1080 chr16 53769311 53769311 1 * 16 53769311 rs62048402
1081 chr4 110780277 110780277 1 * 4 110780277 rs59788391
1082 chr5 110840429 110840429 1 * 5 110840429 rs9885413
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1082 HF # rtracklayer::export.bed(Short_gwas_gr,"data/Final_four_data/ARR_HF_SNP_local.bed")
#
Dox_prot <- readRDS("data/other_papers/Dox_proteome_paper.RDS")
proto_list <- Dox_prot %>%
group_by(SYMBOL,logFC) %>%
summarize(ENTREZID=paste(unique(ENTREZID),collapse=";"),
Protein=paste(unique(Protein),collapse=";"))Here I am doing the overlapping of the previous ranges of SNPs and the full ATAC peak set. I also later create the data frames from the reheat data, the reheat data using the p<0.005 top genes, the cluster names associated with each peak, and the list of TE/notTE associated with each peak.
ATAC_peaks_gr <- Collapsed_new_peaks %>% GRanges()
Peaks_cutoff <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/") %>% dplyr::select(Peakid)
gwas_short_list <- gwas_peak_check %>% as.data.frame %>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_10k_list <- gwas_peak_check_10k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_20k_list <- gwas_peak_check_20k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_50k_list <- gwas_peak_check_50k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
Reheat_data <- read_excel("data/other_papers/jah36123-sup-0002-tables2.xlsx")
top_reheat <- Reheat_data %>%
dplyr::filter(fisher_pvalue<0.005)
Nine_te_df <- readRDS("data/Final_four_data/Nine_group_TE_df.RDS")
###needed to change TE status to at least 1 bp overlap
match <- Nine_te_df %>%
mutate(TEstatus=if_else(!is.na(per_ol),"TE_peak","not_TE_peak")) %>%
distinct(Peakid,TEstatus,mrc,.keep_all = TRUE) Peaks within 5kb +/- RNA TSS, gwas range +/- 10 kb
To break down what I am doing here: I start with the list of peaks that overlap a gwas SNP that has been expanded by 20kb. I then only add the RNA expressed genes associated with the peaks that are within +/- 5 kb of its TSS. I join the median LFC data frames for ATAC and RNA at 3 and 24 hours, the TEstatus, the reheat status and exclude any SNP-Peak combinations that do not have RNA assigned. (This effectively is filtering out peaks outside of the 10kb TSS range That would make the list drop from 2019 to 298 rows)
gwas_df <-gwas_20k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
summarize(name=unique(name),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Peaks within 5kb +/- RNA TSS, gwas range +/- 25 kb
For comparison, I went ahead and did the same this as above, but used the +/- 25 kb expanded SNP range. This left me with 660 ATAC-SNP_RNA sets.
gwas_df <-
gwas_50k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Peaks within +/-20 kb RNA TSS ,gwas range +/- 10 kb
warning, 702 rows below
gwas_df <-
gwas_20k_list%>%
as.data.frame() %>%
left_join(., peak_40kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
arrange(., Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
gwas short list only
To make life really easy, or the smallest set that was readable, I used only the peaks that were directly overlapping a SNP, but filtered out peaks that were more than +/- 5 kb from an expressed RNA TSS. This gave me 33 ATAC-SNP-RNA rows.
gwas_df_short <-gwas_short_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
na.omit(RNA_median_24_lfc) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
arrange(., Peakid)%>%
left_join(., proto_list, by=c("SYMBOL"="SYMBOL"))
gwas_mat_short <- gwas_df_short %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc,logFC) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat_short <- gwas_df_short %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno_short <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat_short$TEstatus,reheat_status=gwas_name_mat_short$reheat,MRC=gwas_name_mat_short$mrc,direct_overlap=gwas_name_mat_short$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2_short <- gwas_mat_short
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map_short <- ComplexHeatmap::Heatmap(gwas_mat_short,
left_annotation = row_anno_short,
# show_row_names = TRUE,
# width = 10,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat_short), gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_short, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| 5e56c1b | E. Renee Matthews | 2025-01-24 |
mapping genes of interest:
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
# K27_counts <- readRDS("data/Final_four_data/All_Raodahpeaks.RDS")
ATAC_counts <- readRDS("data/Final_four_data/x4_filtered.RDS")
RNA_counts <- readRDS("data/other_papers/cpmcount.RDS") %>%
dplyr::rename_with(.,~gsub(pattern="Da",replacement="DNR",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Do",replacement="DOX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ep",replacement="EPI",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Mi",replacement="MTX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Tr",replacement="TRZ",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ve",replacement="VEH",.)) %>%
rownames_to_column("ENTREZID")df_gene <- data.frame(SYMBOL=c("PSRC1","CDKN1A","CELSR2"))
df_gene <- df_gene %>%
left_join(., (RNA_median_24_lfc %>% dplyr::select(ENTREZID,SYMBOL)), by = c ("SYMBOL"="SYMBOL")) %>%
left_join(., (gwas_df_short %>% dplyr::select(SNPS,Peakid,mrc,SYMBOL)),by = c("SYMBOL"="SYMBOL"))
RNA_counts %>%
dplyr::filter(ENTREZID %in% df_gene$ENTREZID) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
left_join(., df_gene, by =c("ENTREZID"="ENTREZID")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~SYMBOL+Peakid, scales="free_y")+
ggtitle("RNA LFC of expressed gene")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% df_gene$Peakid) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
left_join(., df_gene, by =c("Peakid"="Peakid")) %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~Peakid+SYMBOL,scales="free_y")+
ggtitle(" ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
df_gene_next <- data.frame(SYMBOL=c("RAB44","DINOL"), ENTREZID=c("401258","108783646"))
df_gene_next <- df_gene_next %>%
left_join(., (gwas_df_short %>% dplyr::select(SNPS,Peakid,mrc,SYMBOL)),by = c("SYMBOL"="SYMBOL"))
RNA_counts %>%
dplyr::filter(ENTREZID %in% df_gene_next$ENTREZID) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
# mutate(ENTREZID=as.numeric(ENTREZID)) %>%
left_join(., df_gene_next, by =c("ENTREZID"="ENTREZID")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
# facet_wrap(~SYMBOL+Peakid, scales="free_y")+
ggtitle("RNA LFC of expressed gene")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| 5e56c1b | E. Renee Matthews | 2025-01-24 |
Last figure attempt #2
count_df <- join_overlap_intersect(Collapsed_new_peaks_gr, Short_gwas_gr)
new_gwas_df <- count_df %>%
as.data.frame() %>%
left_join(., Nine_te_df, by=("Peakid"="Peakid")) %>%
left_join(.,(Collapsed_new_peaks %>%
dplyr::select (Peakid, SYMBOL )),by = c ("Peakid"="Peakid")) %>%
dplyr::filter(mrc !="NR") %>%
dplyr::filter(mrc !="not_mrc") %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak")) %>%
mutate(dist_to_SNP=0) %>%
group_by(Peakid, SNPS) %>%
summarize(med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
SYMBOL=unique(SYMBOL),collapse=";",
# AC_24h_lfc=unique(AC_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
GWAS=paste(unique(gwas),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
tidyr::unite(name,Peakid,SNPS,SYMBOL,sep ="_",remove=FALSE) %>%
arrange(., Peakid)
new_gwas_mat <- new_gwas_df%>%
ungroup() %>%
dplyr::select(name,med_3h_lfc, med_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
new_gwas_name_mat <- new_gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,GWAS,dist_to_SNP)
row_anno_gwas <-
rowAnnotation(
TE_status=new_gwas_name_mat$TEstatus,
gwas_status=new_gwas_name_mat$GWAS,
MRC=new_gwas_name_mat$mrc,
direct_overlap=new_gwas_name_mat$dist_to_SNP,
col= list(TE_status=c("TE_peak"="goldenrod",
"not_TE_peak"="lightblue"),
MRC = c("EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_clop"="tan",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
gwas_status=c("AF"="green",
"HF"="orange",
"AF;HF"="purple3"),
direct_overlap=c("0"="red",
"10"="pink",
"20"="tan2",
"50"="grey8")))
simply_map_gwas <- ComplexHeatmap::Heatmap(new_gwas_mat,
left_annotation = row_anno_gwas,
row_names_max_width = max_text_width(rownames(new_gwas_mat),
gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_gwas, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| 5e56c1b | E. Renee Matthews | 2025-01-24 |
# new_gwas_df %>%
# dplyr::filter(GWAS=="AF"|GWAS =="AF;HF") %>%
# distinct(Peakid)
#
# # distinct(Peakid) %>%
# # tally
# Short_gwas_gr %>%
# as.data.frame() %>%
# dplyr::filter(gwas=="AF") %>%
# distinct(SNPS)Knowles_respQTL <- readRDS("data/Knowles_5.RDS")
Knowles_alleQTL <- readRDS("data/Knowles_4.RDS")
# file <- read_parquet(choose.files())
Heart_Left_Ventricle_v10_eGenes <- readRDS("data/other_papers/Heart_Left_Ventricle_v10_eGenes_GTEx.RDS")
gwaslist <- new_gwas_df %>% distinct(SNPS) %>% ungroup()
Heart_Left_Ventricle_v10_eGenes %>% dplyr::filter (rs_id_dbSNP155_GRCh38p13 %in% gwaslist$SNPS)# A tibble: 2 × 32
gene_id gene_name biotype gene_chr gene_start gene_end strand num_var
<chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
1 ENSG00000108175… ZMIZ1 protei… chr10 79068966 79316519 + 8988
2 ENSG00000184207… PGP protei… chr16 2211593 2214840 - 8210
# ℹ 24 more variables: beta_shape1 <dbl>, beta_shape2 <dbl>, true_df <dbl>,
# pval_true_df <dbl>, variant_id <chr>, tss_distance <dbl>, chr <chr>,
# variant_pos <dbl>, ref <chr>, alt <chr>, num_alt_per_site <dbl>,
# rs_id_dbSNP155_GRCh38p13 <chr>, ma_samples <dbl>, ma_count <dbl>, af <dbl>,
# pval_nominal <dbl>, slope <dbl>, slope_se <dbl>, pval_perm <dbl>,
# pval_beta <dbl>, qval <dbl>, pval_nominal_threshold <dbl>, afc <dbl>,
# afc_se <dbl>gwaslist$gene <-c("-","PSRC1","PSRC1","CLCN6","-","PRRX1","PRRX1","-","-","-",
"FUT11;SYNPO2l-AS1","-","ENSG00000254851;ENSG00000280143","-","-","-","TMEM263-DT","-","-","BRICD5;PGP",
"-","-","-","-","CHRNB1","-","-","-","FKBP7","-",
"PLGLB1","-","KCNE1","-","-","-","SCN10A","GMPPB;WDR6;NCKIPSD;NICN1;RBM6;AMT;QRICH1;IHO1","WDR1","-",
"-","-","-","DNAJC18;SPATA24;PROB1;SLC23A1","-","CDKN1A","CAV2;ENSG00000279086","-","-")
gwaslist %>%
dplyr::filter(SNPS %in% Knowles_alleQTL$RSID)# A tibble: 1 × 3
Peakid SNPS gene
<chr> <chr> <chr>
1 chr5.139426247.139426836 rs11242465 DNAJC18;SPATA24;PROB1;SLC23A1Knowles_alleQTL %>%
dplyr::filter(RSID %in% gwaslist$SNPS)# A tibble: 1 × 6
gene chr pos RSID p q
<chr> <chr> <dbl> <chr> <dbl> <dbl>
1 ENSG00000184584 chr5 139426616 rs11242465 0.00155 0.0381# write.csv(gwaslist,"data/other_papers/GWAS_eQTL_genes.csv")nakano, et al
nakano_SNPs <- readRDS("data/other_papers/nakano_SNPs_pull_VEF.RDS")
nakano_SNP_table <-
nakano_SNPs %>%
dplyr::select(1:2) %>%
distinct() %>%
separate_wider_delim(.,Location,delim=":",names=c("chr","position"), cols_remove=FALSE) %>%
separate_wider_delim(.,position,delim="-",names=c("begin","term")) %>%
mutate(chr=paste0("chr",chr))
nakano_SNP_gr <- nakano_SNP_table %>%
mutate("start" = begin, "end"=term) %>%
GRanges()
nakano_SNP_10k_gr <- nakano_SNP_table %>%
mutate(begin=as.numeric(begin),term=as.numeric(term)) %>%
mutate(start=begin-5000, end=term+5000) %>%
GRanges()
nakano_SNP_20k_gr <- nakano_SNP_table %>%
mutate(begin=as.numeric(begin),term=as.numeric(term)) %>%
mutate(start=begin-10000, end=term+10000) %>%
GRanges()
nakano_SNP_gr_check <- join_overlap_intersect(Collapsed_new_peaks_gr,nakano_SNP_gr) %>%
as.data.frame()
nakano_SNP_gr_10k_check <- join_overlap_intersect(Collapsed_new_peaks_gr,nakano_SNP_10k_gr) %>%
as.data.frame()
nakano_SNP_gr_20k_check <- join_overlap_intersect(Collapsed_new_peaks_gr,nakano_SNP_20k_gr) %>%
as.data.frame()nakano_SNP_gr_check <- join_overlap_intersect(Collapsed_new_peaks_gr,nakano_SNP_gr) %>%
as.data.frame()
nakano_df <-nakano_SNP_gr_20k_check%>%
as.data.frame() %>%
dplyr::select(Peakid, X.Uploaded_variation) %>%
dplyr::rename("SNPS"=X.Uploaded_variation) %>%
left_join(., Nine_te_df, by=("Peakid"="Peakid")) %>%
dplyr::select(Peakid, SNPS,mrc,TEstatus) %>%
# left_join(., (Collapsed_new_peaks %>%
# dplyr::select(Peakid,NCBI_gene,SYMBOL)), by=c("Peakid"="Peakid"))
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
dplyr::filter(mrc !="NR") %>%
dplyr::filter(mrc !="not_mrc") %>%
mutate(dist_to_SNP=case_when(
Peakid %in% nakano_SNP_gr_check$Peakid &SNPS %in% nakano_SNP_gr_check$X.Uploaded_variation~ 0,
Peakid %in% nakano_SNP_gr_10k_check$Peakid &SNPS %in% nakano_SNP_gr_10k_check$X.Uploaded_variation~ 10,
Peakid %in% nakano_SNP_gr_20k_check$Peakid &SNPS %in% nakano_SNP_gr_20k_check$X.Uploaded_variation~ 20)) %>%
tidyr::unite(name,Peakid,SNPS, sep = "_", remove = FALSE) %>%
group_by(Peakid) %>%
summarize(name=unique(name),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
# RNA_3h_lfc=unique(RNA_3h_lfc),
# RNA_24h_lfc=unique(RNA_24h_lfc),
# repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
# SYMBOL=paste(unique(SYMBOL),collapse=";"),
# reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
arrange(., Peakid)
new_nakano_mat <- nakano_df%>%
ungroup() %>%
dplyr::select(name,med_3h_lfc, med_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
new_nakano_name_mat <- nakano_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,dist_to_SNP)
row_anno_nakano <-
rowAnnotation(
TE_status=new_nakano_name_mat$TEstatus,
# gwas_status=new_nakano__name_mat$GWAS,
MRC=new_nakano_name_mat$mrc,
direct_overlap=new_nakano_name_mat$dist_to_SNP,
col= list(TE_status=c("TE_peak"="goldenrod",
"not_TE_peak"="lightblue"),
MRC = c("EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_clop"="tan",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
# gwas_status=c("AF"="green",
# "HF"="orange",
# "AF;HF"="purple3"),
direct_overlap=c("0"="red",
"10"="pink",
"20"="tan2",
"50"="grey8")))
col_fun <- circlize::colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
simply_map_nakano <- ComplexHeatmap::Heatmap(new_nakano_mat,
col= col_fun,
left_annotation = row_anno_nakano,
row_names_max_width = max_text_width(rownames(new_nakano_mat),
gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_nakano, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| 7ea74a3 | E. Renee Matthews | 2025-02-10 |
nakano_gene <- data.frame(SYMBOL=c("FSCN2","FAAP100"))
nakano_gene <- nakano_gene %>%
left_join(., (RNA_median_24_lfc %>% dplyr::select(ENTREZID,SYMBOL)), by = c ("SYMBOL"="SYMBOL"))
# left_join(., (gwas_df_short %>% dplyr::select(SNPS,Peakid,mrc,SYMBOL)),by = c("SYMBOL"="SYMBOL"))
RNA_counts %>%
dplyr::filter(ENTREZID %in% nakano_gene$ENTREZID) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
left_join(., nakano_gene, by =c("ENTREZID"="ENTREZID")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~SYMBOL, scales="free_y")+
ggtitle("RNA log 2 cpm of expressed gene")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| 7ea74a3 | E. Renee Matthews | 2025-02-10 |
nakano_atac <- nakano_df %>% dplyr::select(Peakid)
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% nakano_atac$Peakid) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
left_join(., nakano_atac, by =c("Peakid"="Peakid")) %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~Peakid,scales="free_y")+
ggtitle(" ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| 7ea74a3 | E. Renee Matthews | 2025-02-10 |
checking for H3K27 ac overlap
overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS")
new_gwas_df %>%
dplyr::filter(Peakid %in% overlap_df_ggplot$peakid)# A tibble: 19 × 12
# Groups: Peakid [16]
name Peakid SNPS med_3h_lfc med_24h_lfc SYMBOL collapse repClass TEstatus
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
1 chr1.1… chr1.… rs88… -0.874 -0.983 CASZ1 ; LINE TE_peak
2 chr1.1… chr1.… rs17… -0.798 -1.21 MTHFR ; LINE:SI… TE_peak
3 chr1.2… chr1.… rs12… 0.142 -1.07 ACTN2 ; NA not_TE_…
4 chr10.… chr10… rs17… -0.123 -0.857 ZMIZ1 ; NA not_TE_…
5 chr11.… chr11… rs18… -0.404 -0.852 HTATI… ; SINE TE_peak
6 chr11.… chr11… rs26… -0.404 -0.852 HTATI… ; SINE TE_peak
7 chr11.… chr11… rs47… -0.404 -0.852 HTATI… ; SINE TE_peak
8 chr15.… chr15… rs71… -1.14 -2.94 HCN4 ; NA not_TE_…
9 chr16.… chr16… rs77… -0.156 -0.807 PGP ; Other:L… TE_peak
10 chr16.… chr16… rs76… 0.0382 -0.555 ANTKMT ; Other TE_peak
11 chr22.… chr22… rs14… -0.175 -0.950 CELSR1 ; SINE TE_peak
12 chr22.… chr22… rs19… -0.175 -0.950 CELSR1 ; SINE TE_peak
13 chr3.1… chr3.… rs56… -0.636 -1.39 LSM3 ; SINE:LI… TE_peak
14 chr3.3… chr3.… rs68… -0.0239 -0.989 SCN5A ; SINE TE_peak
15 chr3.4… chr3.… rs76… -0.917 -0.537 KLHDC… ; Other TE_peak
16 chr4.1… chr4.… rs22… -0.0675 1.05 PITX2 ; NA not_TE_…
17 chr6.3… chr6.… rs31… 0.624 0.833 CDKN1A ; NA not_TE_…
18 chr7.1… chr7.… rs22… -0.591 -1.11 KCNH2 ; NA not_TE_…
19 chr8.1… chr8.… rs35… -0.257 -0.612 GATA4 ; LTR:SIN… TE_peak
# ℹ 3 more variables: GWAS <chr>, mrc <chr>, dist_to_SNP <dbl>overlap_df_ggplot %>%
dplyr::filter(peakid %in% new_gwas_df$Peakid) peakid Geneid AC_3h_lfc AC_24h_lfc
1 chr1.10736684.10737808 chr1.10736800.10738525 -1.07130364 -0.3161770
2 chr1.11792123.11792871 chr1.11788673.11793441 -0.99402173 -0.7606922
3 chr1.236688731.236689166 chr1.236688392.236691210 -0.65889582 -1.4066351
4 chr10.79139073.79139940 chr10.79136506.79140349 -0.68866808 -0.9499010
5 chr11.19988504.19989024 chr11.19987440.19988841 -0.02974555 -0.5303745
6 chr15.73374622.73375198 chr15.73374063.73376621 -0.07183763 -0.6055360
7 chr16.719886.721785 chr16.719959.721831 0.10173815 -0.5844286
8 chr16.2214289.2215681 chr16.2214045.2215689 -0.16667020 -0.7756399
9 chr22.46417011.46418228 chr22.46417239.46418071 -0.76670700 -0.4177514
10 chr3.14232390.14233136 chr3.14232161.14233176 0.18102248 -0.4684514
11 chr3.38725644.38726351 chr3.38723566.38726669 -0.48988241 -0.7003138
12 chr3.49173142.49174143 chr3.49170739.49175549 -0.50498900 -0.2912939
13 chr4.110793293.110793943 chr4.110793293.110794147 0.07621543 0.6552841
14 chr6.36678380.36679788 chr6.36674959.36687167 0.53591918 1.4631670
15 chr7.150954727.150956459 chr7.150953391.150957009 -0.94929588 -1.0307303
16 chr8.11641900.11643102 chr8.11640911.11645051 -0.12144589 -0.5033708
med_3h_lfc med_24h_lfc
1 -0.87377970 -0.9827495
2 -0.79827638 -1.2100494
3 0.14168149 -1.0686028
4 -0.12270258 -0.8573865
5 -0.40393132 -0.8518911
6 -1.14277387 -2.9394196
7 0.03824147 -0.5545150
8 -0.15641641 -0.8074414
9 -0.17506976 -0.9497148
10 -0.63602797 -1.3939025
11 -0.02385810 -0.9889708
12 -0.91700064 -0.5367741
13 -0.06749937 1.0534724
14 0.62408785 0.8327855
15 -0.59108390 -1.1085155
16 -0.25736705 -0.6120516# Park_df %>%
# dplyr::filter(Peakid %in% overlap_df_ggplot$peakid)
# overlap_df_ggplot %>%
# dplyr::filter(peakid %in% Park_df$Peakid)
new_gwas_df %>%
ungroup() %>%
dplyr::filter(mrc=="EAR_open"|mrc=="ESR_open"|mrc=="LR_open") %>%
distinct(SNPS)# A tibble: 21 × 1
SNPS
<chr>
1 rs629301
2 rs660240
3 rs10824026
4 rs7115242
5 rs11841562
6 rs11642015
7 rs7197197
8 rs9930504
9 rs2071502
10 rs3803802
# ℹ 11 more rows
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] circlize_0.4.16
[2] arrow_18.1.0.1
[3] readxl_1.4.3
[4] smplot2_0.2.5
[5] cowplot_1.1.3
[6] ComplexHeatmap_2.22.0
[7] ggrepel_0.9.6
[8] plyranges_1.26.0
[9] ggsignif_0.6.4
[10] genomation_1.38.0
[11] edgeR_4.4.1
[12] limma_3.62.2
[13] ggpubr_0.6.0
[14] BiocParallel_1.40.0
[15] ggVennDiagram_1.5.2
[16] scales_1.3.0
[17] VennDiagram_1.7.3
[18] futile.logger_1.4.3
[19] gridExtra_2.3
[20] ggfortify_0.4.17
[21] rtracklayer_1.66.0
[22] org.Hs.eg.db_3.20.0
[23] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[24] GenomicFeatures_1.58.0
[25] AnnotationDbi_1.68.0
[26] Biobase_2.66.0
[27] GenomicRanges_1.58.0
[28] GenomeInfoDb_1.42.1
[29] IRanges_2.40.1
[30] S4Vectors_0.44.0
[31] BiocGenerics_0.52.0
[32] RColorBrewer_1.1-3
[33] broom_1.0.7
[34] kableExtra_1.4.0
[35] lubridate_1.9.4
[36] forcats_1.0.0
[37] stringr_1.5.1
[38] dplyr_1.1.4
[39] purrr_1.0.2
[40] readr_2.1.5
[41] tidyr_1.3.1
[42] tibble_3.2.1
[43] ggplot2_3.5.1
[44] tidyverse_2.0.0
[45] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.1 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.5
[13] lattice_0.22-6 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-2
[17] sass_0.4.9 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] plotrix_3.8-4 httpuv_1.6.15
[23] DBI_1.2.3 abind_1.4-8
[25] zlibbioc_1.52.0 RCurl_1.98-1.16
[27] nnet_7.3-20 git2r_0.35.0
[29] GenomeInfoDbData_1.2.13 svglite_2.1.3
[31] codetools_0.2-20 DelayedArray_0.32.0
[33] xml2_1.3.6 tidyselect_1.2.1
[35] shape_1.4.6.1 farver_2.1.2
[37] UCSC.utils_1.2.0 base64enc_0.1-3
[39] matrixStats_1.5.0 GenomicAlignments_1.42.0
[41] jsonlite_1.8.9 GetoptLong_1.0.5
[43] Formula_1.2-5 iterators_1.0.14
[45] systemfonts_1.2.1 foreach_1.5.2
[47] tools_4.4.2 Rcpp_1.0.14
[49] glue_1.8.0 SparseArray_1.6.1
[51] xfun_0.50 MatrixGenerics_1.18.1
[53] withr_3.0.2 formatR_1.14
[55] fastmap_1.2.0 callr_3.7.6
[57] digest_0.6.37 timechange_0.3.0
[59] R6_2.5.1 seqPattern_1.38.0
[61] colorspace_2.1-1 RSQLite_2.3.9
[63] utf8_1.2.4 generics_0.1.3
[65] data.table_1.16.4 htmlwidgets_1.6.4
[67] httr_1.4.7 S4Arrays_1.6.0
[69] whisker_0.4.1 pkgconfig_2.0.3
[71] gtable_0.3.6 blob_1.2.4
[73] impute_1.80.0 XVector_0.46.0
[75] htmltools_0.5.8.1 carData_3.0-5
[77] pwr_1.3-0 clue_0.3-66
[79] png_0.1-8 knitr_1.49
[81] lambda.r_1.2.4 rstudioapi_0.17.1
[83] tzdb_0.4.0 reshape2_1.4.4
[85] rjson_0.2.23 checkmate_2.3.2
[87] curl_6.2.0 zoo_1.8-12
[89] cachem_1.1.0 GlobalOptions_0.1.2
[91] KernSmooth_2.23-26 parallel_4.4.2
[93] foreign_0.8-88 restfulr_0.0.15
[95] pillar_1.10.1 vctrs_0.6.5
[97] promises_1.3.2 car_3.1-3
[99] cluster_2.1.8 htmlTable_2.4.3
[101] evaluate_1.0.3 magick_2.8.5
[103] cli_3.6.3 locfit_1.5-9.10
[105] compiler_4.4.2 futile.options_1.0.1
[107] Rsamtools_2.22.0 rlang_1.1.5
[109] crayon_1.5.3 labeling_0.4.3
[111] ps_1.8.1 getPass_0.2-4
[113] plyr_1.8.9 fs_1.6.5
[115] stringi_1.8.4 viridisLite_0.4.2
[117] gridBase_0.4-7 assertthat_0.2.1
[119] munsell_0.5.1 Biostrings_2.74.1
[121] Matrix_1.7-2 BSgenome_1.74.0
[123] patchwork_1.3.0 hms_1.1.3
[125] bit64_4.6.0-1 KEGGREST_1.46.0
[127] statmod_1.5.0 SummarizedExperiment_1.36.0
[129] memoise_2.0.1 bslib_0.8.0
[131] bit_4.5.0.1