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load("/Users/nicholeyang/Desktop/gene_level_fine_mapping/data/train_add_hic.RData")

data summary

head(dat_add_hic)

  gene_name      snp_loc38             variant_id UTR5 UTR3 exon intron
1      A1BG chr19:57866502 chr19_57866502_T_C_b38    0    0    0      0
2      A1BG chr19:58059544 chr19_58059544_T_G_b38    0    0    0      0
3      A1BG chr19:58170494 chr19_58170494_G_T_b38    0    0    0      0
4      A1BG chr19:58228973 chr19_58228973_T_G_b38    0    0    0      0
5      A1BG chr19:58330182 chr19_58330182_C_T_b38    0    0    0      0
6      A1BG chr19:58359927 chr19_58359927_G_A_b38    0    0    0      0
  upstream tss_dist_to_snp y Mon Mac0 Mac1 Mac2 Neu MK EP Ery FoeT nCD4 tCD4
1        0          481132 0   0    0    0    0   0  0  0   0    0    0    0
2        0          288090 0   0    0    0    0   0  0  0   0    0    0    0
3        0          177140 0   0    0    0    0   0  0  0   0    0    0    0
4        0          118661 0   0    0    0    0   0  0  0   0    0    0    0
5        0           17452 1   0    0    0    0   0  0  0   0    0    0    0
6        0            6428 1   0    0    0    0   0  0  0   0    0    0    0
  aCD4 naCD4 nCD8 tCD8 nB tB
1    0     0    0    0  0  0
2    0     0    0    0  0  0
3    0     0    0    0  0  0
4    0     0    0    0  0  0
5    0     0    0    0  0  0
6    0     0    0    0  0  0

hist(dat_add_hic$tss_dist_to_snp, xlab = 'distance between tss and snp', main = 'Histogram on all data')

Version	Author	Date
5e78e6f	yunqiyang0215	2020-08-05

hist(dat_add_hic$tss_dist_to_snp[dat_add_hic$y==1], breaks = 20,  xlab = 'positive', main = '')

Version	Author	Date
d0ba55a	yunqiyang0215	2020-08-06

hist(dat_add_hic$tss_dist_to_snp[dat_add_hic$y==0], breaks = 20, xlab = 'negative', main = '')

Version	Author	Date
d0ba55a	yunqiyang0215	2020-08-06

range(dat_add_hic$tss_dist_to_snp)

[1]       0 2176373

range(dat_add_hic$tss_dist_to_snp[dat_add_hic$y == 1])

[1]      0 946399

unify the HiC feature

hic = apply(dat_add_hic[, c(11:27)], 1, sum)
dat_add_hic$hic = ifelse(hic>0, 1, 0)

sub_dat = dat_add_hic[dat_add_hic$y == 1, ]
prop_hic_pos = sum(sub_dat$hic == 1)/dim(sub_dat)[1]

sub_dat = dat_add_hic[dat_add_hic$y == 0, ]
prop_hic_neg = sum(sub_dat$hic == 1)/dim(sub_dat)[1]

prop_hic_pos

[1] 0.07263733

prop_hic_neg

[1] 0.1394212

by distance hic signature summary

dist_range = c(0, 1e4, 5e4, 1e5, 3e6)
prop_pos = rep(NA, length(dist_range)-1)
prop_neg = rep(NA, length(dist_range)-1)


for (i in 1:(length(dist_range)-1)){
  
  sub_dat = dat_add_hic[dat_add_hic$tss_dist_to_snp %in% seq(dist_range[i], dist_range[i+1], by = 1), ]
  
  dat_pos = sub_dat[sub_dat$y == 1, ]
  dat_neg = sub_dat[sub_dat$y == 0, ]
  
  prop_pos[i] = sum(dat_pos$hic == 1)/dim(dat_pos)[1]
  prop_neg[i] = sum(dat_neg$hic == 1)/dim(dat_neg)[1]
}

plot(prop_pos, type = 'b', ylim = c(0, 0.3), ylab = 'proportions')
points(prop_neg, type = 'b', col = 'red')
legend('topright', legend =  c('positive', 'negative'), col = c('black', 'red'), lty = 1)

cat_tss_dist_pos = cut(dat_add_hic[dat_add_hic$y==1, ]$tss_dist_to_snp, breaks = dist_range)
cat_tss_dist_neg = cut(dat_add_hic[dat_add_hic$y==0, ]$tss_dist_to_snp, breaks = dist_range)

table(cat_tss_dist_pos)

cat_tss_dist_pos
    (0,1e+04] (1e+04,5e+04] (5e+04,1e+05] (1e+05,3e+06] 
         2558           902           230           146

table(cat_tss_dist_neg)

cat_tss_dist_neg
    (0,1e+04] (1e+04,5e+04] (5e+04,1e+05] (1e+05,3e+06] 
         1263          5833          7014         48505

regress unified HiC feature

fit1 = glm(y ~ hic, data = dat_add_hic, family = "binomial")
fit2 = glm(y ~ hic + tss_dist_to_snp + hic*tss_dist_to_snp, data =dat_add_hic, family = "binomial")

Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

fit3 = glm(y ~ hic , data =dat_add_hic[dat_add_hic$tss_dist_to_snp> 5e4, ], family = "binomial")

summary(fit1)


Call:
glm(formula = y ~ hic, family = "binomial", data = dat_add_hic)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.3578  -0.3578  -0.3578  -0.3578   2.6362  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.71655    0.01730 -157.02   <2e-16 ***
hic         -0.72676    0.06323  -11.49   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 29356  on 66456  degrees of freedom
Residual deviance: 29195  on 66455  degrees of freedom
AIC: 29199

Number of Fisher Scoring iterations: 6

summary(fit2)


Call:
glm(formula = y ~ hic + tss_dist_to_snp + hic * tss_dist_to_snp, 
    family = "binomial", data = dat_add_hic)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.2091  -0.1315  -0.0094  -0.0004   8.4904  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          7.427e-02  2.851e-02   2.605  0.00918 ** 
hic                 -1.032e+00  1.025e-01 -10.065  < 2e-16 ***
tss_dist_to_snp     -4.239e-05  8.096e-07 -52.362  < 2e-16 ***
hic:tss_dist_to_snp  1.563e-05  1.803e-06   8.664  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 29356  on 66456  degrees of freedom
Residual deviance: 15875  on 66453  degrees of freedom
AIC: 15883

Number of Fisher Scoring iterations: 10

summary(fit3)


Call:
glm(formula = y ~ hic, family = "binomial", data = dat_add_hic[dat_add_hic$tss_dist_to_snp > 
    50000, ])

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1363  -0.1125  -0.1125  -0.1125   3.1828  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -5.05877    0.05763 -87.779  < 2e-16 ***
hic          0.38376    0.13095   2.931  0.00338 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4502.8  on 55893  degrees of freedom
AIC: 4506.8

Number of Fisher Scoring iterations: 8

plot the proportion of features in the positive/negative set

for (i in 1:(length(dist_range)-1)){
  
  par(mfrow = c(1,2))
  
  sub_dat = dat_add_hic[dat_add_hic$tss_dist_to_snp %in% seq(dist_range[i], dist_range[i+1], by = 1), ]

  # proportion of features in the positive set
  tot_count = apply(sub_dat[sub_dat$y==1, c(11:27)], 2, function(x) sum(x == 1))
  barplot(tot_count/dim(sub_dat[sub_dat$y==1, ])[1], las = 2, main = paste('positive set', dist_range[i], dist_range[i+1], sep = ':'), cex.names=.7)

  # proportion of features in the negative set
  tot_count = apply(sub_dat[sub_dat$y==0, c(11:27)], 2, function(x) sum(x == 1))
  barplot(tot_count/dim(sub_dat[sub_dat$y==0, ])[1], las = 2, 
          main = paste('negative set', dist_range[i], dist_range[i+1], sep = ':'), cex.names=.7)
  
}

one variable logistic regression by range

sub_dat = dat_add_hic[dat_add_hic$tss_dist_to_snp > 5e4, ]
for (i  in 11: 27){
  fit = glm(sub_dat$y ~ sub_dat[, i],  family = "binomial")
  print(summary(fit))
}


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1452  -0.1145  -0.1145  -0.1145   3.1718  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.02350    0.05377 -93.422   <2e-16 ***
sub_dat[, i]  0.47604    0.19744   2.411   0.0159 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4505.6  on 55893  degrees of freedom
AIC: 4509.6

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1499  -0.1146  -0.1146  -0.1146   3.1716  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.02300    0.05354 -93.814  < 2e-16 ***
sub_dat[, i]  0.53932    0.20813   2.591  0.00956 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4504.9  on 55893  degrees of freedom
AIC: 4508.9

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1671  -0.1137  -0.1137  -0.1137   3.1766  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.03887    0.05393 -93.426  < 2e-16 ***
sub_dat[, i]  0.77478    0.19160   4.044 5.26e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4497.4  on 55893  degrees of freedom
AIC: 4501.4

Number of Fisher Scoring iterations: 8


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1753  -0.1132  -0.1132  -0.1132   3.1793  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.04761    0.05417 -93.184  < 2e-16 ***
sub_dat[, i]  0.87977    0.18359   4.792 1.65e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4492.4  on 55893  degrees of freedom
AIC: 4496.4

Number of Fisher Scoring iterations: 8


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1193  -0.1160  -0.1160  -0.1160   3.1638  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99795    0.05325  -93.87   <2e-16 ***
sub_dat[, i]  0.05631    0.22535    0.25    0.803    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.6  on 55893  degrees of freedom
AIC: 4514.6

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1381  -0.1152  -0.1152  -0.1152   3.1682  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.01210    0.05324 -94.138   <2e-16 ***
sub_dat[, i]  0.36451    0.22563   1.616    0.106    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4508.3  on 55893  degrees of freedom
AIC: 4512.3

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1240  -0.1159  -0.1159  -0.1159   3.1645  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.00030    0.05287 -94.570   <2e-16 ***
sub_dat[, i]  0.13566    0.25646   0.529    0.597    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.4  on 55893  degrees of freedom
AIC: 4514.4

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1163  -0.1163  -0.1163  -0.1163   3.1710  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99363    0.05295 -94.310   <2e-16 ***
sub_dat[, i] -0.02754    0.24899  -0.111    0.912    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.7  on 55893  degrees of freedom
AIC: 4514.7

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1171  -0.1171  -0.1171  -0.1171   3.2712  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.97973    0.05267 -94.552   <2e-16 ***
sub_dat[, i] -0.36591    0.28295  -1.293    0.196    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4508.8  on 55893  degrees of freedom
AIC: 4512.8

Number of Fisher Scoring iterations: 8


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1164  -0.1164  -0.1164  -0.1164   3.1772  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99182    0.05340 -93.484   <2e-16 ***
sub_dat[, i] -0.04908    0.21586  -0.227     0.82    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.6  on 55893  degrees of freedom
AIC: 4514.6

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-0.119  -0.116  -0.116  -0.116   3.164  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99734    0.05310  -94.12   <2e-16 ***
sub_dat[, i]  0.04971    0.23625    0.21    0.833    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.7  on 55893  degrees of freedom
AIC: 4514.7

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1162  -0.1162  -0.1162  -0.1162   3.1659  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99430    0.05325 -93.796   <2e-16 ***
sub_dat[, i] -0.01061    0.22530  -0.047    0.962    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.7  on 55893  degrees of freedom
AIC: 4514.7

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1259  -0.1156  -0.1156  -0.1156   3.1660  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.00501    0.05347 -93.604   <2e-16 ***
sub_dat[, i]  0.17171    0.21179   0.811    0.417    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.1  on 55893  degrees of freedom
AIC: 4514.1

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1287  -0.1154  -0.1154  -0.1154   3.1669  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -5.00805    0.05355 -93.528   <2e-16 ***
sub_dat[, i]  0.21889    0.20784   1.053    0.292    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4509.7  on 55893  degrees of freedom
AIC: 4513.7

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1162  -0.1162  -0.1162  -0.1162   3.1631  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.994824   0.053321 -93.674   <2e-16 ***
sub_dat[, i] -0.001159   0.220436  -0.005    0.996    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.7  on 55893  degrees of freedom
AIC: 4514.7

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1167  -0.1167  -0.1167  -0.1167   3.2257  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -4.9855     0.0528 -94.416   <2e-16 ***
sub_dat[, i]  -0.2115     0.2641  -0.801    0.423    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.0  on 55893  degrees of freedom
AIC: 4514

Number of Fisher Scoring iterations: 7


Call:
glm(formula = sub_dat$y ~ sub_dat[, i], family = "binomial")

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1163  -0.1163  -0.1163  -0.1163   3.1703  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.99368    0.05302 -94.180   <2e-16 ***
sub_dat[, i] -0.02507    0.24231  -0.103    0.918    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4510.7  on 55894  degrees of freedom
Residual deviance: 4510.7  on 55893  degrees of freedom
AIC: 4514.7

Number of Fisher Scoring iterations: 7

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4      rprojroot_1.3-2 digest_0.6.25   later_1.0.0    
 [5] R6_2.4.1        backports_1.1.5 git2r_0.26.1    magrittr_1.5   
 [9] evaluate_0.14   highr_0.8       stringi_1.4.6   rlang_0.4.5    
[13] fs_1.3.2        promises_1.1.0  whisker_0.4     rmarkdown_2.1  
[17] tools_3.6.3     stringr_1.4.0   glue_1.3.2      httpuv_1.5.2   
[21] xfun_0.12       yaml_2.2.1      compiler_3.6.3  htmltools_0.4.0
[25] knitr_1.28

model hic

Yunqi Yang