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2023-10-14-【学习】孟德尔随机化.md

2023-10-14-【学习】孟德尔随机化.md 17 KB
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title: 【学习】孟德尔随机化 urlname: Mendelian-Randomization date: 2023-10-14 17:18:54 index_img: https://api.limour.top/randomImg?d=2023-10-14 17:18:54

tags: ['SNP', 'MR']

MR定义

孟德尔随机化是一种基于全基因组测序数据(GWAS数据)，利用单核首酸多态性(SNPs)作为工具变量(IV)，用于揭示因果关系的新型流行病学方法，相较于队列研究等观察性研究，暴露在出生前便已确定，较少受到反向因果及混杂因素的影响，因而能够有效减少偏倚。 RCT与MR的比较 MR的核心是运用遗传学数据作为桥梁，来探索某一暴露和某一结局之间的因果关联。与RCT将参与者随机分配到试验组或对照组类似，MR研究基于影响危险因素的一个或多个等位基因，对参与基因进行"随机化"(自然的随机化)，以确定这些遗传变异的携带者与非携带者相比，是否具有不同的疾病发生风险，因此，孟德尔随机化可以被认为类似于"自然的随机对照试验"。MR的相关术语

理论假设

  1. the variant is associated with the exposure
  2. the variant is not associated with the outcome via a confounding pathway
  3. the variant does not affect the outcome directly, only possibly indirectly via the exposure

孟德尔随机化框架的有向无环图表示

  1. 关联性假设：变异与暴露有关
  2. 独立性假设：变异与结果之间没有通过混杂途径相关
  3. 排他性假设：变异不直接影响结果，只可能通过暴露途径间接影响
  • 关联性假设：p值，F统计量，R^2
  • 排他性假设：与结局的相关性计算时，p值要大于0.05

  • MR-Egger回归相比线性回归可以弱化对排他性假设的要求

    适用范围

  • 不确定先有鸡还是先有蛋，比如，到底是抑郁导致肺癌还是肺癌导致了抑郁？

  • 暴露因素难以测量，或者花费昂贵。例如，水溶性维生素等生物标志物的检测金标准可能成本太高，大样本无法承受，或者空腹血糖的测量需要隔夜空腹，可能不现实。

  • 暴露与结局数据来自同一人群，且不存在或存在少量可接受范围内的样本重叠

    配置环境

  • 基础编程环境

  • GitHub 下载加速

  • 可能需要用到的加速服务

    conda create -n MR -c conda-forge r-devtools -y
conda activate MR
conda install -c conda-forge r-irkernel -y
Rscript -e "IRkernel::installspec(name='MR', displayname='MR')"
# Rscript -e "usethis::edit_r_environ()" # 设置 GITHUB_PAT
# nano ~/.Renviron # MRCIEU 真是超喜欢GITHUB，要访问一万次 api.github.com
conda install -c conda-forge r-rmarkdown -y
conda install -c conda-forge r-meta -y
wget https://github.com/MRCIEU/TwoSampleMR/archive/refs/heads/master.zip -O TwoSampleMR.zip
Rscript -e "devtools::install_local('TwoSampleMR.zip')"
wget https://github.com/MRCIEU/MRInstruments/archive/refs/heads/master.zip -O MRInstruments.zip
Rscript -e "devtools::install_local('MRInstruments.zip')"
conda install -c conda-forge r-susier -y
conda install -c bioconda bioconductor-variantannotation -y
wget https://github.com/MRCIEU/gwasglue/archive/refs/heads/master.zip -O gwasglue.zip
Rscript -e "devtools::install_local('gwasglue.zip')"
# wget https://github.com/MRCIEU/genetics.binaRies/archive/refs/heads/master.zip -O genetics.binaRies.zip
# Rscript -e "devtools::install_local('genetics.binaRies.zip')"
conda install -c bioconda plink -y
# whereis plink # /opt/conda/envs/MR/bin/plink
Rscript -e 'install.packages("MendelianRandomization")'

    数据来源

  • 精神病学基因组：PGC

  • 社会科学遗传学：SSGAC

  • 大脑健康和疾病：CTG

  • MRCIEU汇总数据库：IEU

  • GWAS研究目录：NHGRI-EBI

  • 自己分析出数据

  • 更多相关网站

    一些参考数据

    wget http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz
tar -zxvf 1kg.v3.tgz
# mkdir EUR && mv EUR.* EUR

    示例结局数据

  • 浏览器下载 ADHD2022_iPSYCH_deCODE_PGC.meta.gz

  • 上传到服务器

    # zcat ADHD2022_iPSYCH_deCODE_PGC.meta.gz | head
CHR SNP BP A1 A2 FRQ_A_38691 FRQ_U_186843 INFO OR SE P Direction Nca Nco
8 rs62513865 101592213 C T 0.925 0.937 0.981 0.99631 0.0175 0.8325 +---+++0-++-+ 38691 186843
8 rs79643588 106973048 G A 0.91 0.917 1 1.00411 0.0159 0.7967 ++--++-+-+-++ 38691 186843
8 rs17396518 108690829 T G 0.561 0.577 0.998 0.99611 0.0096 0.6876 --++-++??-+-- 37367 184388
8 rs983166 108681675 A C 0.57 0.586 0.996 0.99491 0.0096 0.5956 --++-++++-+-- 38691 186843
8 rs28842593 103044620 T C 0.839 0.836 0.982 0.98314 0.0135 0.2081 ----++0+??--+ 37504 184525
8 rs7014597 104152280 G C 0.824 0.824 0.997 0.99950 0.0122 0.9679 +-++-+++++--- 38691 186843
8 rs3134156 100479917 T C 0.841 0.833 0.997 0.98866 0.0128 0.3762 -+----+--++-- 38691 186843
8 rs6980591 103144592 A C 0.783 0.79 1 1.01106 0.0108 0.3075 ++-++---+++++ 38691 186843
8 rs72670434 108166508 A T 0.642 0.623 0.983 1.00672 0.0103 0.5171 +++-+++--+++- 38691 186843

    CHR Chromosome (hg19)
SNP Marker name
BP Base pair location (hg19)
A1 Reference allele for OR (may or may not be minor allele)
A2 Alternative allele
FRQ_A_38691 allele frequency of A1 in 38,691 ADHD cases
FRQ_U_186843 allele frequency of A1 in 38,691 controls
INFO Imputation information score (the reported imputation INFO score is a weighted average across the
cohorts contributing to the meta-analysis for that variant)
OR Odds ratio for the effect of the A1 allele
SE Standard error of the log(OR)
P P-value for association test in the meta-analysis
Direction direction of effect in the included cohorts
Nca number of cases with variant information
Nco number of controls with variant information

    其中SNP,Effect allele,Beta(OR),SE,P这五列是必须的。遇到没有提供EAF的数据，可以匹配千人基因组数据的EAF，get_eaf_from_1000G。

    示例暴露数据

    wget -c https://gwas.mrcieu.ac.uk/files/ieu-a-2/ieu-a-2.vcf.gz

    VCF_dat = VariantAnnotation::readVcf('~/upload/GWAS/IEU/ieu-a-2.vcf.gz')
exp_dat = gwasglue::gwasvcf_to_TwoSampleMR(vcf = VCF_dat)
saveRDS(file = 'ieu-a-2.exp_dat', exp_dat)
exp_dat = subset(exp_dat, pval.exposure < 5e-08) # 关联性假设
# 去除连锁不平衡
# exp_dat = TwoSampleMR::clump_data(dat = exp_dat, clump_kb = 10000, clump_r2 = 0.001) # MRCIEU太喜欢用cloud api了
fix_ld_clump_local = function (dat, tempfile, clump_kb, clump_r2, clump_p, bfile, plink_bin) {
shell <- ifelse(Sys.info()["sysname"] == "Windows", "cmd", 
    "sh")
write.table(data.frame(SNP = dat[["rsid"]], P = dat[["pval"]]), 
    file = tempfile, row.names = F, col.names = T, quote = F)
fun2 <- paste0(shQuote(plink_bin, type = shell), " --bfile ", 
    shQuote(bfile, type = shell), " --clump ", shQuote(tempfile, 
        type = shell), " --clump-p1 ", clump_p, " --clump-r2 ", 
    clump_r2, " --clump-kb ", clump_kb, " --out ", shQuote(tempfile, 
        type = shell))
print(fun2)
system(fun2)
res <- read.table(paste(tempfile, ".clumped", sep = ""), header = T)
unlink(paste(tempfile, "*", sep = ""))
y <- subset(dat, !dat[["rsid"]] %in% res[["SNP"]])
if (nrow(y) > 0) {
    message("Removing ", length(y[["rsid"]]), " of ", nrow(dat), 
        " variants due to LD with other variants or absence from LD reference panel")
}
return(subset(dat, dat[["rsid"]] %in% res[["SNP"]]))
}
fuck = fix_ld_clump_local(
dat = dplyr::tibble(rsid=exp_dat$SNP, pval=exp_dat$pval.exposure),
tempfile = file.path(getwd(),'tmp.ld_clump.exp_dat'),
clump_kb = 10000, clump_r2 = 0.001, clump_p = 1,
# pop = "EUR", # Super-population. Options are "EUR", "SAS", "EAS", "AFR", "AMR"
plink_bin = '/opt/conda/envs/MR/bin/plink', # 千万别用什么 genetics.binaRies::get_plink_binary()，他们自己编译的文件有问题
bfile = "/home/jovyan/upload/GWAS/ld/EUR" # 前缀，不是文件夹也不是文件
)
exp_dat_clumped = exp_dat[exp_dat$SNP %in% fuck$rsid,]
saveRDS(file = 'ieu-a-2.exp_gwas', exp_dat_clumped)

    获取暴露数据

    自己的数据

    df_gwas <- data.frame(
SNP = c("rs1", "rs2"),
beta = c(1, 2),
se = c(1, 2),
effect_allele = c("A", "T")
)
head(df_gwas)
exp_dat <- TwoSampleMR::format_data(df_gwas, type = "exposure")

    gwas_catalog

    df_gwas <-
subset(MRInstruments::gwas_catalog,
     grepl("Speliotes", Author) &
       Phenotype == "Body mass index")
head(df_gwas)
exp_dat <- TwoSampleMR::format_data(df_gwas)

    metab_qtls

    df_gwas <-
subset(MRInstruments::metab_qtls,
    phenotype == "Ala"
)
head(df_gwas)
exp_dat <- TwoSampleMR::format_metab_qtls(df_gwas)

    proteomic_qtls

    df_gwas <-
subset(MRInstruments::proteomic_qtls,
    analyte == "ApoH"
)
head(df_gwas)
exp_dat <- TwoSampleMR::format_proteomic_qtls(df_gwas)

    某个基因

    df_gwas <-
subset(MRInstruments::gtex_eqtl,
    gene_name == "IRAK1BP1" & tissue == "Adipose Subcutaneous"
)
head(df_gwas)
exp_dat <- TwoSampleMR::format_gtex_eqtl(df_gwas)

    某个性状的某个甲基化位点相关QTL

    df_gwas <-
subset(MRInstruments::aries_mqtl,
    cpg == "cg25212131" & age == "Birth"
)
head(df_gwas)
exp_dat <- TwoSampleMR::format_aries_mqtl(df_gwas)

    IEU的ID

    exp_gwas <- TwoSampleMR::extract_instruments(outcomes = 'ieu-a-2')
head(exp_gwas)
saveRDS(file = 'ieu-a-2.exp_gwas', exp_gwas) # 和自己从VCF开始经过clump得到的差不多

    UK_Biobank

    hyperten_tophits <- ieugwasr::tophits(id="ukb-b-12493", clump=0)
hyperten_gwas <- dplyr::rename(hyperten_tophits, c(
"SNP"="rsid",
"effect_allele.exposure"="ea",
"other_allele.exposure"="nea",
"beta.exposure"="beta",
"se.exposure"="se",
"eaf.exposure"="eaf",
"pval.exposure"="p",
"N"="n"))
fuck = fix_ld_clump_local(
dat = dplyr::tibble(rsid=hyperten_gwas$SNP, pval=hyperten_gwas$pval.exposure),
tempfile = file.path(getwd(),'tmp.ld_clump.exp_dat'),
clump_kb = 10000, clump_r2 = 0.001, clump_p = 1,
# pop = "EUR", # Super-population. Options are "EUR", "SAS", "EAS", "AFR", "AMR"
plink_bin = '/opt/conda/envs/MR/bin/plink', # 千万别用什么 genetics.binaRies::get_plink_binary()，他们自己编译的文件有问题
bfile = "/home/jovyan/upload/GWAS/ld/EUR" # 前缀，不是文件夹也不是文件
)
exp_dat_clumped = hyperten_gwas[hyperten_gwas$SNP %in% fuck$rsid,]
MR_calc_r2_F(
beta = exp_dat_clumped$beta.exposure, # Vector of Log odds ratio. beta = log(OR)
eaf = exp_dat_clumped$eaf.exposure, # Vector of allele frequencies.
N = exp_dat_clumped$N, # Array of sample sizes
se = exp_dat_clumped$se.exposure # Vector of SE.
) # 取 F>10 的

    计算统计效力

    # 分类变量
tmp_r2 =TwoSampleMR::get_r_from_lor(
lor = exp_dat_clumped$beta.exposure, # Vector of Log odds ratio. beta = log(OR)
af = exp_dat_clumped$eaf.exposure, # Vector of allele frequencies.
ncase = exp_dat_clumped$ncase.exposure, # Vector of Number of cases. 
ncontrol = exp_dat_clumped$ncontrol.exposure, # Vector of Number of controls. 
prevalence = 1, # Vector of Disease prevalence in the population.
)
# 连续变量
tmp_r2 =TwoSampleMR::get_r_from_pn(
p = exp_dat_clumped$pval.exposure, # Array of pvals
n = exp_dat_clumped$samplesize.exposure # Array of sample sizes
)

    MR_calc_r2_F = function(beta, eaf, N, se){
# https://doi.org/10.1038/s41467-020-14389-8
# https://doi.org/10.1371/journal.pone.0120758
r2 = (2 * (beta^2) * eaf * (1 - eaf)) /
        (2 * (beta^2) * eaf * (1 - eaf) +
        2 * N * eaf * (1 - eaf) * se^2)
F = r2 * (N - 2) / (1 - r2)
print(mean(F))
return(dplyr::tibble(r2=r2, F=F))
}
MR_calc_r2_F(
beta = exp_dat_clumped$beta.exposure, # Vector of Log odds ratio. beta = log(OR)
eaf = exp_dat_clumped$eaf.exposure, # Vector of allele frequencies.
N = exp_dat_clumped$samplesize.exposure, # Array of sample sizes
se = exp_dat_clumped$se.exposure # Vector of SE.
) # 取 F>10 的

    获取结局数据

    IEU

    out_gwas = TwoSampleMR::extract_outcome_data(snps = exp_gwas$SNP, outcomes = 'ieu-a-7')

    UK_Biobank

    anxiety_hyperten_liberal <- TwoSampleMR::extract_outcome_data(snps = exp_dat_clumped$SNP, outcomes = "ukb-b-11311")

    PGC的示例

    df_gwas = read.table(gzfile('~/upload/GWAS/PGC/ADHD2022_iPSYCH_deCODE_PGC.meta.gz'), header = T)
head(df_gwas)
df_gwas = df_gwas[df_gwas$SNP %in% exp_gwas$SNP,]
out_gwas = data.frame(
SNP = df_gwas$SNP,
chr = as.character(df_gwas$CHR),
pos = df_gwas$BP,
beta.outcome = log(df_gwas$OR),
se.outcome = df_gwas$SE,
samplesize.outcome = df_gwas$Nca + df_gwas$Nco,
pval.outcome = df_gwas$P,
eaf.outcome = with(df_gwas, (FRQ_A_38691*Nca+FRQ_U_186843*Nco)/(Nca+Nco)),
effect_allele.outcome = df_gwas$A1,
other_allele.outcome = df_gwas$A2,
outcome = 'ADHD',
id.outcome = 'ADHD2022_iPSYCH_deCODE_PGC'    
)
out_gwas = subset(out_gwas, pval.outcome > 5e-08) # 排他性假设

附加 代理SNP

一部分暴露的SNPs在结局中找不到，可以找和这部分SNPs连锁不平衡的SNPs来代替。相关网站：snipa

Harmonization

  • 将Exposure-SNP及Outcome-SNP等位基因方向协同
  • 根据EAF大小，剔除不能判断方向的回文SNP

  • 剔除incompatible SNP

    dat <- TwoSampleMR::harmonise_data(
exposure_dat = exp_gwas, 
outcome_dat = out_gwas
)

    附加 一键报告

    TwoSampleMR::mr_report(dat, output_type = "md")

    MR分析

    回归分析

    TwoSampleMR::mr_method_list() # 查看mr支持的MR分析方法
mr_regression = TwoSampleMR::mr(dat, method_list = c('mr_ivw', 'mr_egger_regression', 'mr_weighted_median'))
mr_regression_or = TwoSampleMR::generate_odds_ratios(mr_res = mr_regression) # 分类变量
{pdf(file = 'MR.BMIvsADHD.plot.pdf', width = 6, height = 6); # 导出 PDF 开始
print(TwoSampleMR::mr_scatter_plot(mr_results = mr_regression, dat = dat)); # 返回的是一个ggplot2对象
dev.off()} # 导出 PDF 结束

    mr_scatter_plot

    异质性检测

  • 有异质性用随机效应模型ivw，无异质性用固定效应模型（也可以用随机效应模型，两者结果一致）

  • 异质性可能带来多效性，如果没有多效性，则可以说异质性没有带来多效性

    TwoSampleMR::mr_heterogeneity(dat) # ivw的 Q_pval < 0.05 则说明有异质性
heterogeneity_presso = TwoSampleMR::run_mr_presso(dat, NbDistribution = 3000) # NbDistribution越高分辨率越高，找不到离群的SNP时需要提高
heterogeneity_presso[[1]]$`MR-PRESSO results`$`Global Test`$Pvalue # < 0.05 说明有异质性
heterogeneity_presso[[1]]$`MR-PRESSO results`$`Distortion Test`$`Outliers Indices` # 显示离群的SNP，将其剔除后重新分析

    水平多效性

  • P < 0.05 说明不满足独立性假设，建议放弃继续做这个课题

  • P < 0.05 拒绝了截距为0的假设，说明SNP效应为0时依然有影响(截距存在)，有其他因素在起作用

    TwoSampleMR::mr_pleiotropy_test(dat)

    敏感性分析

  • Leave-one-out analysis

  • 所有结果都不应该存在跨过0的情况，否则说明结果不稳定，不再能说明因果关系

    mr_loo <- TwoSampleMR::mr_leaveoneout(dat)
{pdf(file = 'MR.BMIvsADHD.leaveoneout.plot.pdf', width = 6, height = 6); # 导出 PDF 开始
print(TwoSampleMR::mr_leaveoneout_plot(leaveoneout_results = mr_loo)); # 返回的是一个ggplot2对象
dev.off()} # 导出 PDF 结束

    单SNP分析

  • 对每个暴露-结果组合进行多次分析，每次使用不同的单 SNP 进行分析

    mr_res_single <- TwoSampleMR::mr_singlesnp(dat)
TwoSampleMR::mr_funnel_plot(mr_res_single)
TwoSampleMR::mr_forest_plot(mr_res_single)

    方向性检测

    TwoSampleMR::directionality_test(dat) # TRUE表示确实是暴露导致了结果

    附加 稳健回归

    dat2 <- TwoSampleMR::dat_to_MRInput(dat)
mr_ivw_robust <- MendelianRandomization::mr_ivw(dat2[[1]], model= "default", # “random”指的就是随机效应模型，“fixed”指的是固定效应模型
                                         robust = TRUE, penalized = TRUE,correl = FALSE, # 参数penalized代表下调异常值的权重
                                         weights ="simple", psi = 0,distribution = "normal",alpha = 0.05)

    附加 绘制森林图

  • 美化森林图

附加 计算Power