孟德尔随机化--初筛

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本节内容我们主要讲解如何进行孟德尔随机化暴露与结局的快速初筛。

主要列举以下几个方法：

基于我自己写的一个小的R包：friendly2MR;

基于epigraphdb数据库开发epigraphdb包；

基于epigraphdb在线网站；

实用教程：

基于我自己写的一个小的R包：friendly2MR：

安装和加载：

if (!requireNamespace("remotes", quietly = TRUE))install.packages("remotes")
remotes::install_github("xiechengyong123/friendly2MR")
library(friendly2MR)

使用：

# 测试多个暴露对一个结局的阳性结果 ----------------------------------------------------------------------
#rm(list = ls())
library(TwoSampleMR)
library(friendly2MR)
# List available GWASs
ao <- available_outcomes(access_token = NULL)

ao1=ao[grep("ukb",ao$id),]
#IEU GWAS 数据ID
exposure <-ao1$id
exposure=exposure[1:20]

#调用函数，生成结果
mr_mul2one=find_anyexposur_outcome(exposure,"ieu-a-7",write=T)

# 测试一个暴露对多个结局的阳性结果 ----------------------------------------------------------------------
# rm(list = ls())
library(TwoSampleMR)
library(friendly2MR)
# List available GWASs
# ao <- available_outcomes(access_token =NULL)

ao1=ao[grep("ukb",ao$id),]
#IEU GWAS 数据ID
outcome <-ao1$id
outcome=outcome[1:20]
mr_one2mul=find_exposur_anyoutcome("ieu-a-7",outcome,write=T)

结果：

mr_mul2one：

id.exposure	id.outcome	conclusion
ukb-b-1489	ieu-a-7	初筛结果阳性
ukb-b-8727	ieu-a-7	初筛结果无阳性
ukb-a-583	ieu-a-7	需要重新设定LD阈值，否则无法进行MR分析
ukb-b-12466	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-767_EAS	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-1707_p1_MID	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-1883_AFR	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-b-5326	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-d-22608_2	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-d-5610_3	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-2100_AFR	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-20019_CSA	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-b-20382	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-4825_EAS	ieu-a-7	初筛结果无阳性
ukb-e-24015_AFR	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-e-104900_AFR	ieu-a-7	筛选暴露P值太小，无法进行MR分析
ukb-b-19277	ieu-a-7	初筛结果阳性
ukb-b-17772	ieu-a-7	初筛结果阳性
ukb-d-22660_107	ieu-a-7	初筛结果无阳性
ukb-b-5863	ieu-a-7	筛选暴露P值太小，无法进行MR分析

mr_one2mul：

id.exposure	id.outcome	conclusion
ieu-a-7	ukb-b-1489	初筛结果阳性
ieu-a-7	ukb-b-8727	初筛结果无阳性
ieu-a-7	ukb-a-583	初筛结果无阳性
ieu-a-7	ukb-b-12466	初筛结果无阳性
ieu-a-7	ukb-e-767_EAS	初筛结果无阳性
ieu-a-7	ukb-e-1707_p1_MID	需要重新设定LD阈值，否则无法进行MR分析
ieu-a-7	ukb-e-1883_AFR	初筛结果无阳性
ieu-a-7	ukb-b-5326	初筛结果无阳性
ieu-a-7	ukb-d-22608_2	初筛结果无阳性
ieu-a-7	ukb-d-5610_3	初筛结果无阳性
ieu-a-7	ukb-e-2100_AFR	初筛结果阳性
ieu-a-7	ukb-e-20019_CSA	初筛结果无阳性
ieu-a-7	ukb-b-20382	初筛结果无阳性
ieu-a-7	ukb-e-4825_EAS	初筛结果无阳性
ieu-a-7	ukb-e-24015_AFR	初筛结果无阳性
ieu-a-7	ukb-e-104900_AFR	初筛结果无阳性
ieu-a-7	ukb-b-19277	初筛结果无阳性
ieu-a-7	ukb-b-17772	初筛结果无阳性
ieu-a-7	ukb-d-22660_107	初筛结果无阳性
ieu-a-7	ukb-b-5863	初筛结果无阳性

基于epigraphdb数据库开发epigraphdb包：

安装以及加载：

if (!requireNamespace("epigraphdb", quietly = TRUE))install.packages("epigraphdb")
library("epigraphdb")

封装函数调用：

# 筛选多个暴露和多个结局中的显著结果 -------------------------------------------------------
fast_screen=function(exposure = NULL, 
                     outcome = NULL, 
                     pval_threshold = 1e-05, 
                     write=T,
                     file="fast_screen.csv"){
  start=Sys.time()
  cat("##########################开始筛查######################################\n")
  #设置mr_fast_result_all
  mr_fast_result_all<-data.frame()
  #首先生成IEU的性状信息：
  if(!file.exists("IEU_ao.RData")){
    ao <- available_outcomes()
    write.csv(ao,file = "IEU_ao.csv")
    save(ao,file = "IEU_ao.RData")
  }
  load(file = "IEU_ao.RData")

  exposure_length=length(exposure)
  outcome_length=length(outcome)

  for (i in 1:exposure_length) {
    for (j in 1:outcome_length) {
      exposure_id = exposure[i]
      outcome_id = outcome[j]
      # #判断暴露id和性状是否在数据库中
      # if (sum((exposure_id %in% ao$trait),(exposure_id %in% ao$id))>0) {
      # }else{stop("\n##########################该暴露不在数据库中######################################\n")}
      # # #判断结局id和性状是否在数据库中
      # if (sum((outcome_id %in% ao$trait),(outcome_id %in% ao$id))>0) {
      # }else{stop("\n##########################该结局不在数据库中######################################\n")}
      #首先判断是ieu号还是性状名称：
      if (!is.null(exposure_id)) {
        if (exposure_id %in% ao$id) {
          exposure_trait <- ao[ao$id == exposure_id, ]$trait
        } else{
          exposure_trait = exposure_id
        }
      } else{
        exposure_trait = exposure_id
      }

      if (!is.null(outcome)) {
        if (outcome_id %in% ao$id) {
          outcome_trait <- ao[ao$id == outcome_id, ]$trait
        } else{
          outcome_trait = outcome_id
        }
      } else{
        outcome_trait = outcome_id
      }

      mr_fast_result = epigraphdb::mr(
        exposure_trait = exposure_trait,
        pval_threshold = pval_threshold,
        outcome_trait = outcome_trait,
        mode = "table"
      )

      #根据ieu号进行筛选：
      # mr_fast_result1<-data.frame()
      if (!is.null(exposure_id)) {
        if (exposure_id %in% ao$id) {
          mr_fast_result <-
            mr_fast_result[mr_fast_result$exposure.id == exposure_id, ]
        }else{mr_fast_result=mr_fast_result}
      }
      # mr_fast_result1 <- unique(rbind(mr_fast_result1, mr_fast_result1))

      # mr_fast_result2<-data.frame()
      if (!is.null(outcome_id)) {
        if (outcome_id %in% ao$id) {
          mr_fast_result <- mr_fast_result[mr_fast_result$outcome.id == outcome_id, ]
        }else{mr_fast_result=mr_fast_result}
      }
      # 多个结果合并
      # mr_fast_result2 <- unique(rbind(mr_fast_result2,mr_fast_result2))
      mr_fast_result_all<-unique(rbind(mr_fast_result,mr_fast_result_all))

    } 
  }
  # mr_fast_result_all<-unique(rbind(mr_fast_result_all,mr_fast_result_all))

  if (write) {
    write.csv(mr_fast_result_all,file=file,row.names = F)
  }
  end=Sys.time()
  print(end-start)
  cat("##########################筛查结束######################################\n")

  return(mr_fast_result_all)
}

#筛选一个暴露和所有结局中的显著结果
mv_mr_result=fast_screen(exposure="Body mass index")
mv_mr_result1=fast_screen(exposure="ieu-a-2")

#筛选所有暴露和一个结局中的显著结果
mv_mr_result2=fast_screen(outcome="Coronary heart disease")
mv_mr_result3=fast_screen(outcome="ieu-a-7")

#筛选一个暴露和一个结局中的显著结果
mv_mr_result4=fast_screen(exposure="Body mass index",outcome="Coronary heart disease")
mv_mr_result5=fast_screen(exposure="ieu-a-2",outcome="ieu-a-7")
mv_mr_result6=fast_screen(exposure="Body mass index",outcome="ieu-a-7")
mv_mr_result7=fast_screen(exposure="ieu-a-2",outcome="Coronary heart disease")

#筛选多个暴露和所有结局
mv_mr_result8=fast_screen(exposure=c("Body mass index","Coronary heart disease"))

mv_mr_result9=fast_screen(exposure=c("Coronary heart disease","ieu-a-2"))

mv_mr_result10=fast_screen(exposure=c("Body mass index","ieu-a-7"))

mv_mr_result11=fast_screen(exposure=c("ieu-a-2","ieu-a-7"))

#筛选所有暴露和多个结局
mv_mr_result12=fast_screen(outcome=c("Body mass index","Coronary heart disease"))

mv_mr_result3=fast_screen(outcome=c("ieu-a-2","Coronary heart disease"))

mv_mr_result14=fast_screen(outcome=c("Body mass index","ieu-a-7"))

mv_mr_result15=fast_screen(outcome=c("ieu-a-2","ieu-a-7"))

#筛选多个暴露和多个结局
mv_mr_result16=fast_screen(exposure=c("Body mass index","Coronary heart disease"),outcome=c("Body mass index","Coronary heart disease"))

mv_mr_result17=fast_screen(outcome=c("ieu-a-2","Coronary heart disease"))

mv_mr_result18=fast_screen(outcome=c("Body mass index","ieu-a-7"))

mv_mr_result19=fast_screen(outcome=c("ieu-a-2","ieu-a-7"))

mv_mr_result20=fast_screen(exposure=c("ieu-a-2","ieu-a-7"),outcome=c("ieu-a-2","ieu-a-7"))
mv_mr_result21=fast_screen(exposure="ieu-a-2",outcome="ieu-a-7")
mv_mr_result22=fast_screen(exposure="ieu-a-7",outcome="ieu-a-2")

结果：

mv_mr_result：

exposure.id	exposure.trait	outcome.id	outcome.trait	mr.b	mr.se	mr.pval	mr.method	mr.selection	mr.moescore
ieu-a-974	Body mass index	ebi-a-GCST005062	Fibrinogen levels	0.193038233	0.002236082	0	FE IVW	DF	1
ebi-a-GCST006368	Body mass index	ukb-b-20188	Arm fat percentage (left)	0.533277383	0.010444881	0	FE IVW	DF + HF	0.93
ieu-a-2	Body mass index	ukb-b-4650	Comparative body size at age 10	0.439223556	0.009889465	0	FE IVW	Tophits	0.9
ieu-a-2	Body mass index	ukb-b-2303	Body mass index (BMI)	0.673901055	0.017841589	0	FE IVW	DF + HF	0.92
ieu-a-2	Body mass index	ukb-b-16446	Basal metabolic rate	0.44864951	0.011831593	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ukb-a-282	Arm fat percentage (right)	0.527720537	0.012506983	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ieu-a-94	Body mass index	1.008870831	0.02685817	0	FE IVW	HF	0.86
ieu-a-2	Body mass index	ieu-a-48	Hip circumference	0.828391733	0.014868948	0	FE IVW	Tophits	0.91
ieu-a-2	Body mass index	ukb-b-9405	Waist circumference	0.644965247	0.012623943	0	FE IVW	Tophits	0.94
ieu-a-2	Body mass index	ukb-b-9093	Arm predicted mass (left)	0.398396568	0.008409717	0	FE IVW	Tophits	0.92
ieu-a-785	Body mass index	ieu-a-85	Extreme body mass index	1.717111367	0.001634409	0	FE IVW	DF + HF	0.82
ebi-a-GCST004904	Body mass index	ukb-b-2303	Body mass index (BMI)	0.59491911	0.015287241	0	FE IVW	Tophits	0.81
ieu-a-835	Body mass index	ukb-b-18096	Leg fat mass (right)	0.607879093	0.013882094	0	FE IVW	DF + HF	0.94
ieu-a-835	Body mass index	ukb-b-12854	Arm fat percentage (right)	0.525880654	0.013035386	0	FE IVW	DF + HF	0.96
ieu-a-835	Body mass index	ieu-a-93	Overweight	1.671102672	0.034075749	0	FE IVW	Tophits	0.84
ieu-a-835	Body mass index	ieu-a-61	Waist circumference	0.823566324	0.019441318	0	FE IVW	Tophits	0.94
ieu-a-835	Body mass index	ieu-a-60	Waist circumference	0.819575103	0.019483688	0	FE IVW	Tophits	0.9
ieu-a-2	Body mass index	ieu-a-65	Waist circumference	0.731622271	0.020027625	3.62E-292	FE IVW	DF + HF	0.88
ebi-a-GCST004904	Body mass index	ukb-b-16099	Leg fat-free mass (left)	0.338792555	0.009287316	2.38E-291	FE IVW	Tophits	0.83
ieu-a-835	Body mass index	ieu-a-50	Hip circumference	0.836276005	0.022981123	6.20E-290	FE IVW	HF	0.89
ieu-a-2	Body mass index	ukb-b-15590	Hip circumference	0.651710135	0.017978537	1.02E-287	FE IVW	DF + HF	0.91
ebi-a-GCST006368	Body mass index	ieu-a-94	Body mass index	0.964264501	0.02711376	5.11E-277	FE IVW	Tophits	0.84
ebi-a-GCST006368	Body mass index	ukb-b-7212	Leg fat mass (left)	0.617988587	0.017559991	2.56E-271	Weighted median	HF	0.89
ieu-a-95	Body mass index	ieu-a-974	Body mass index	0.980908389	0.027924883	2.64E-270	FE IVW	HF	0.74
ieu-a-835	Body mass index	ukb-b-15590	Hip circumference	0.648765312	0.018483694	6.90E-270	FE IVW	DF + HF	0.93
ieu-a-835	Body mass index	ieu-a-64	Waist circumference	0.739561429	0.021168464	2.04E-267	FE IVW	DF + HF	0.89
ieu-a-2	Body mass index	ukb-a-277	Leg predicted mass (right)	0.443018641	0.012685733	3.38E-267	FE IVW	DF + HF	0.91
ieu-a-785	Body mass index	ieu-a-52	Hip circumference	0.76669586	0.022013173	8.87E-266	FE IVW	DF + HF	0.92
ieu-a-835	Body mass index	ieu-a-94	Body mass index	1.002287387	0.028871062	4.52E-264	FE IVW	HF	0.82

mv_mr_result1：

exposure.id	exposure.trait	outcome.id	outcome.trait	mr.b	mr.se	mr.pval	mr.method	mr.selection	mr.moescore
ieu-a-2	Body mass index	ukb-b-4650	Comparative body size at age 10	0.439223556	0.009889465	0	FE IVW	Tophits	0.9
ieu-a-2	Body mass index	ukb-b-2303	Body mass index (BMI)	0.673901055	0.017841589	0	FE IVW	DF + HF	0.92
ieu-a-2	Body mass index	ukb-b-16446	Basal metabolic rate	0.44864951	0.011831593	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ukb-a-282	Arm fat percentage (right)	0.527720537	0.012506983	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ieu-a-94	Body mass index	1.008870831	0.02685817	0	FE IVW	HF	0.86
ieu-a-2	Body mass index	ieu-a-48	Hip circumference	0.828391733	0.014868948	0	FE IVW	Tophits	0.91
ieu-a-2	Body mass index	ukb-b-9405	Waist circumference	0.644965247	0.012623943	0	FE IVW	Tophits	0.94
ieu-a-2	Body mass index	ukb-b-9093	Arm predicted mass (left)	0.398396568	0.008409717	0	FE IVW	Tophits	0.92
ieu-a-2	Body mass index	ieu-a-65	Waist circumference	0.731622271	0.020027625	3.62E-292	FE IVW	DF + HF	0.88
ieu-a-2	Body mass index	ukb-b-15590	Hip circumference	0.651710135	0.017978537	1.02E-287	FE IVW	DF + HF	0.91
ieu-a-2	Body mass index	ukb-a-277	Leg predicted mass (right)	0.443018641	0.012685733	3.38E-267	FE IVW	DF + HF	0.91
ieu-a-2	Body mass index	ukb-b-7212	Leg fat mass (left)	0.648456311	0.018929275	3.45E-257	Simple median	Tophits	0.93
ieu-a-2	Body mass index	ieu-a-52	Hip circumference	0.797228817	0.023501272	3.07E-252	FE IVW	Tophits	0.85
ieu-a-2	Body mass index	ieu-a-53	Hip circumference	0.81089765	0.024143591	2.65E-247	FE IVW	Tophits	0.85
ieu-a-2	Body mass index	ukb-b-18096	Leg fat mass (right)	0.637960977	0.019111543	2.59E-244	Simple median	Tophits	0.92
ieu-a-2	Body mass index	ukb-b-6704	Arm fat mass (right)	0.81387157	0.024648715	4.36E-239	Simple median	Tophits	0.9
ieu-a-2	Body mass index	ieu-a-107	Weight	0.965895656	0.029396065	8.76E-237	FE IVW	Tophits	0.86
ieu-a-2	Body mass index	ukb-b-19393	Whole body fat mass	0.763723593	0.023467402	2.54E-232	Simple median	Tophits	0.91
ieu-a-2	Body mass index	ukb-b-8338	Arm fat mass (left)	0.814308642	0.025767648	3.47E-219	Simple median	Tophits	0.91
ieu-a-2	Body mass index	ukb-a-248	Body mass index (BMI)	0.841602649	0.026890568	5.08E-215	Simple median	HF	0.92
ieu-a-2	Body mass index	ukb-a-275	Leg fat mass (right)	0.640040167	0.020840402	4.00E-207	Weighted median	HF	0.93
ieu-a-2	Body mass index	ukb-b-18377	Leg fat percentage (left)	0.428676596	0.014325895	9.82E-197	Simple median	HF	0.92

mv_mr_result2:

exposure.id	exposure.trait	outcome.id	outcome.trait	mr.b	mr.se	mr.pval	mr.method	mr.selection	mr.moescore
ubm-a-496	IDP dMRI TBSS ICVF Superior cerebellar peduncle R	ebi-a-GCST000998	Coronary heart disease	0.136105468	0.000331893	0	FE IVW	DF	1
ubm-a-2677	volume Right-Cerebellum-Cortex	ebi-a-GCST000998	Coronary heart disease	-0.22925404	0.005693358	0	FE IVW	DF	1
ubm-a-496	IDP dMRI TBSS ICVF Superior cerebellar peduncle R	ieu-a-8	Coronary heart disease	0.136105468	0.000331893	0	FE IVW	DF	1
ubm-a-2677	volume Right-Cerebellum-Cortex	ieu-a-8	Coronary heart disease	-0.22925404	0.005693358	0	FE IVW	DF	1
ukb-a-309	Other serious medical condition/disability diagnosed by doctor	ieu-a-7	Coronary heart disease	2.760557121	0.059659725	0	FE IVW	DF	1
prot-a-67	Alcohol dehydrogenase [NADP(+)]	ieu-a-7	Coronary heart disease	-0.018451731	1.50E-05	0	FE IVW	DF	1
prot-a-399	C-C motif chemokine 23	ieu-a-7	Coronary heart disease	0.018490732	0.00048839	0	FE IVW	DF	1
prot-a-2427	Prostaglandin F2 receptor negative regulator	ieu-a-7	Coronary heart disease	-0.017530907	0.000307085	0	FE IVW	DF	1
prot-a-1792	Leucine-rich repeat neuronal protein 1	ieu-a-7	Coronary heart disease	0.07904299	0.002053008	0	FE IVW	DF	1
prot-a-1666	Kallikrein-7	ieu-a-7	Coronary heart disease	-0.034176952	0.00030915	0	FE IVW	DF	1
prot-a-1587	Inter-alpha-trypsin inhibitor heavy chain H5	ieu-a-7	Coronary heart disease	-0.058270284	0.001067473	0	FE IVW	DF	1
ukb-d-I9_IHD	Ischaemic heart disease, wide definition	ieu-a-9	Coronary heart disease	11.44308868	0.184143218	0	FE IVW	DF	1
ukb-a-12	Nap during day	ieu-a-9	Coronary heart disease	2.437416506	0.008056364	0	FE IVW	DF	1
ieu-a-796	Urate	ieu-a-9	Coronary heart disease	0.148498726	0.001844479	0	FE IVW	DF	1
ukb-b-3278	Length of menstrual cycle	ieu-a-6	Coronary heart disease	0.344276965	0.004943106	0	FE IVW	DF	1
ukb-b-1806	Number of symbol digit matches attempted	ieu-a-6	Coronary heart disease	-0.708949098	0.006804179	0	FE IVW	DF	1
ukb-a-23	Number of operations self-reported	ieu-a-6	Coronary heart disease	1.153492785	0.02236149	0	FE IVW	DF	1
ebi-a-GCST005038	Allergic disease (asthma, hay fever or eczema)	ieu-a-9	Coronary heart disease	-0.144630627	0.001863039	0	FE IVW	DF	1
prot-a-2215	Programmed cell death 1 ligand 2	ieu-a-6	Coronary heart disease	-0.030704826	0.00082524	5.23E-303	FE IVW	DF	1
met-c-918	Phosphatidylcholine and other cholines	ebi-a-GCST000998	Coronary heart disease	0.049792917	0.001370729	6.32E-289	FE IVW	DF	1
met-c-918	Phosphatidylcholine and other cholines	ieu-a-8	Coronary heart disease	0.049792917	0.001370729	6.32E-289	FE IVW	DF	1
prot-a-2039	Neurofascin	ieu-a-8	Coronary heart disease	0.010041464	0.000277356	5.21E-287	FE IVW	DF	1

mv_mr_result3:

exposure.id	exposure.trait	outcome.id	outcome.trait	mr.b	mr.se	mr.pval	mr.method	mr.selection	mr.moescore
ukb-a-309	Other serious medical condition/disability diagnosed by doctor	ieu-a-7	Coronary heart disease	2.760557121	0.059659725	0	FE IVW	DF	1
prot-a-67	Alcohol dehydrogenase [NADP(+)]	ieu-a-7	Coronary heart disease	-0.018451731	1.50E-05	0	FE IVW	DF	1
prot-a-399	C-C motif chemokine 23	ieu-a-7	Coronary heart disease	0.018490732	0.00048839	0	FE IVW	DF	1
prot-a-2427	Prostaglandin F2 receptor negative regulator	ieu-a-7	Coronary heart disease	-0.017530907	0.000307085	0	FE IVW	DF	1
prot-a-1792	Leucine-rich repeat neuronal protein 1	ieu-a-7	Coronary heart disease	0.07904299	0.002053008	0	FE IVW	DF	1
prot-a-1666	Kallikrein-7	ieu-a-7	Coronary heart disease	-0.034176952	0.00030915	0	FE IVW	DF	1
prot-a-1587	Inter-alpha-trypsin inhibitor heavy chain H5	ieu-a-7	Coronary heart disease	-0.058270284	0.001067473	0	FE IVW	DF	1
prot-a-2470	Sulfhydryl oxidase 2	ieu-a-7	Coronary heart disease	0.069634599	0.001998623	5.77E-266	FE IVW	DF	1
ukb-b-18189	Reason for glasses/contact lenses: For long-sightedness, i.e. for distance and near, but particularly for near tasks like reading (called 'hypermetropia')	ieu-a-7	Coronary heart disease	2.379792789	0.080957625	6.26E-190	FE IVW	DF	1
prot-a-280	Uncharacterized protein C10orf35	ieu-a-7	Coronary heart disease	-0.125030158	0.00453479	2.46E-167	FE IVW	DF	1
ukb-a-210	Illnesses of mother: Alzheimer's disease/dementia	ieu-a-7	Coronary heart disease	1.931390023	0.077693322	2.06E-136	FE IVW	DF	1
ebi-a-GCST005920	Paternal history of Alzheimer's disease	ieu-a-7	Coronary heart disease	0.171597385	0.007441708	1.20E-117	FE IVW	DF	1
prot-a-857	Dipeptidase 2	ieu-a-7	Coronary heart disease	0.073361658	0.003188102	3.61E-117	FE IVW	DF	1
prot-a-1688	Keratinocyte differentiation-associated protein	ieu-a-7	Coronary heart disease	0.049755495	0.00230633	3.19E-103	FE IVW	DF	1
ebi-a-GCST005194	Coronary artery disease	ieu-a-7	Coronary heart disease	0.954774586	0.017136978	4.32E-93	Simple mean	HF	0.87
ukb-b-10912	Non-cancer illness code, self-reported: high cholesterol	ieu-a-7	Coronary heart disease	3.796891592	0.1862581	2.27E-92	FE IVW	HF	0.96
ukb-b-7872	Mouth/teeth dental problems: Bleeding gums	ieu-a-7	Coronary heart disease	1.769264657	0.089973244	4.36E-86	FE IVW	DF	1
ukb-d-C3_PRIMARY_LYMPHOID_HEMATOPOIETIC	Primary_lymphoid and hematopoietic malignant neoplasms	ieu-a-7	Coronary heart disease	13.78520614	0.709314219	3.94E-84	FE IVW	DF	1
ukb-b-14177	Vascular/heart problems diagnosed by doctor: High blood pressure	ieu-a-7	Coronary heart disease	1.705308306	0.092300392	3.24E-76	FE IVW	Tophits	0.87
ukb-b-18953	Operative procedures - secondary OPCS: T86.2 Sampling of axillary lymph nodes	ieu-a-7	Coronary heart disease	1.122459119	0.060846997	5.49E-76	FE IVW	DF	1
prot-a-420	CD177 antigen	ieu-a-7	Coronary heart disease	0.01990002	0.001147197	2.09E-67	FE IVW	DF	1

......后续有兴趣大家可以自己试试。

基于epigraphdb在线网站：

网站页面：

1679537219483.png

Network 3D图：

1679537113992.png

效果相当炸裂

列表：

exposure.id(Click to sort Ascending)	exposure.trait(Click to sort Ascending)	outcome.id(Click to sort Ascending)	outcome.trait(Click to sort Ascending)	mr.b(Click to sort Ascending)	mr.se(Click to sort Ascending)	mr.pval(Click to sort Ascending)	mr.method(Click to sort Ascending)	mr.selection(Click to sort Ascending)	mr.moescore(Click to sort Ascending)
ieu-a-974	Body mass index	ebi-a-GCST005062	Fibrinogen levels	0.193	0.0022	0	FE IVW	DF	1
ebi-a-GCST006368	Body mass index	ukb-b-20188	Arm fat percentage (left)	0.5333	0.0104	0	FE IVW	DF + HF	0.93
ieu-a-2	Body mass index	ukb-b-4650	Comparative body size at age 10	0.4392	0.0099	0	FE IVW	Tophits	0.9
ieu-a-2	Body mass index	ukb-b-2303	Body mass index (BMI)	0.6739	0.0178	0	FE IVW	DF + HF	0.92
ieu-a-2	Body mass index	ukb-b-16446	Basal metabolic rate	0.4486	0.0118	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ukb-a-282	Arm fat percentage (right)	0.5277	0.0125	0	FE IVW	DF + HF	0.94
ieu-a-2	Body mass index	ieu-a-94	Body mass index	1.0089	0.0269	0	FE IVW	HF	0.86
ieu-a-2	Body mass index	ieu-a-48	Hip circumference	0.8284	0.0149	0	FE IVW	Tophits	0.91
ieu-a-2	Body mass index	ukb-b-9405	Waist circumference	0.645	0.0126	0	FE IVW	Tophits	0.94
ieu-a-2	Body mass index	ukb-b-9093	Arm predicted mass (left)	0.3984	0.0084	0	FE IVW	Tophits	0.92

与R代码跑出的结果一致

代码爬取：

他还非常贴心的列出了如何获取相关结果的R和python代码，相当nice！

1679537900519.png

注意：

请注意，epigraphdb在线数据库和R包筛选出的结果，都是使用自动算法产生的初步结果，所以应该始终通过使用进一步的方法进行分析来验证。大家有兴趣的话，可以去多多探索。。。

使用教程

b站链接（【孟德尔随机化--初筛2】 https://www.bilibili.com/video/BV1bm4y1k7Ca/?share_source=copy_web&vd_source=db46041789fa1bbf5497ce06b73f53d8）