MR单因素分析

写在前面：在执行本地数据进行分析的时候，均需要按照标准数据准备流程进行预处理，参考：MR标准格式 例如此处用到的本地暴露和本地结局，用以下代码完成标准准备：

# 1.数据下载
# IEU数据下载：https://gwas.mrcieu.ac.uk/files/ieu-a-300/ieu-a-300.vcf.gz
# IEU数据下载：https://gwas.mrcieu.ac.uk/files/ieu-a-301/ieu-a-301.vcf.gz
# IEU数据下载：https://gwas.mrcieu.ac.uk/files/ieu-a-302/ieu-a-302.vcf.gz
# IEU数据下载：https://gwas.mrcieu.ac.uk/files/ieu-a-7/ieu-a-7.vcf.gz
# IEU数据下载：https://gwas.mrcieu.ac.uk/files/ieu-a-8/ieu-a-8.vcf.gz
# bfile数据下载: http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz

# 2.预处理本地数据
prepare_ieu(file_path = "ieu-a-300.vcf.gz",out_path="./")
prepare_ieu(file_path = "ieu-a-301.vcf.gz",out_path="./")
prepare_ieu(file_path = "ieu-a-302.vcf.gz",out_path="./")
prepare_ieu(file_path = "ieu-a-7.vcf.gz",out_path="./")
prepare_ieu(file_path = "ieu-a-8.vcf.gz",out_path="./")

1. 基础MR

1.1 本地暴露-本地结局 

# 1.提取暴露数据
exposure_local <- mr_extract_exposure_local(
  local_exp_path = c("./ieu-a-300.txt","./ieu-a-301.txt","./ieu-a-302.txt"),
  bfile = "./1kg.v3/EUR"
)

# 2. 提取结局数据
# snps来自提取的暴露数据SNP
outcome_local <- mr_extract_outcome_local(
  snps=exposure_local$SNP,
  local_out_path = c("./ieu-a-7.txt","./ieu-a-8.txt"),
  bfile = "./1kg.v3/EUR"
)

# 3. 本地暴露-本地结局MR分析
mr_analysis_uv(
    exposure = exposure_local,
    outcome = outcome_local
)

1.2 在线暴露-在线结局 

# 1. 提取IEU线上暴露数据
exposure_online <- mr_extract_exposure_online(
  ieu_exp_id = c("ieu-a-300","ieu-a-301","ieu-a-302")
)

# 2. 提取IEU线上结局数据
outcome_online <- mr_extract_outcome_online(
  snps = exposure_online$SNP,
  ieu_out_id = c("ieu-a-7","ieu-a-8")
)

# 3. 线上暴露-线上结局MR分析
mr_analysis_uv(
    exposure = exposure_online,
    outcome = outcome_online
)

1.3 在线暴露-本地结局 

# 1. 提取IEU线上暴露数据
exposure_online <- mr_extract_exposure_online(
  ieu_exp_id = c("ieu-a-300","ieu-a-301","ieu-a-302")
)

# 2. 提取本地结局数据
outcome_local <- mr_extract_outcome_local(
  snps=exposure_online$SNP,
  local_out_path = c("./ieu-a-7.txt","./ieu-a-8.txt"),
  bfile = "./1kg.v3/EUR"
)

# 3. 线上暴露-线上结局MR分析
mr_analysis_uv(
    exposure = exposure_online,
    outcome = outcome_local
)

1.3 本地暴露-在线结局 

# 1.提取暴露数据
exposure_local <- mr_extract_exposure_local(
  local_exp_path = c("./ieu-a-300.txt","./ieu-a-301.txt","./ieu-a-302.txt"),
  bfile = "./1kg.v3/EUR"
)


# 2. 提取在线结局数据
outcome_online <- mr_extract_outcome_online(
  snps = exposure_local$SNP,
  ieu_out_id = c("ieu-a-7","ieu-a-8")
)


# 3. 线上暴露-线上结局MR分析
mr_analysis_uv(
    exposure = exposure_local,
    outcome = outcome_online
)

2. 一键MR

2.1 在线暴露-在线结局

备注：基于IEU数据在线分析

# 加载包
library(DrugTargetMR)

# 调用函数
# 单个暴露GWAS，单个结局GWAS
mr_online(
  ieu_exp_id = c("ieu-a-300"),
  ieu_out_id = c("ieu-a-7")
)

# 多个暴露GWAS，多个结局GWAS
mr_online(
  ieu_exp_id = c("ieu-a-300", "ieu-a-301", "ieu-a-302"),
  ieu_out_id = c("ieu-a-7", "ieu-a-8")
)

2.2 本地暴露-本地结局

2.2.1 一步执行 

# 加载包
library(DrugTargetMR)

# 调用函数
mr_local(
  local_exp_path = c(
    "./data_prepare/ieu-a-300.txt",
    "./data_prepare/ieu-a-301.txt",
    "./data_prepare/ieu-a-302.txt"
  ),
  local_out_path = c(
    "./data_prepare/ieu-a-7.txt",
    "./data_prepare/ieu-a-8.txt"
  ),
  out_path = "./result",
  # 指定bfile文件，需要解压之后的文件
  bfile = "./data/1kg.v3/EUR" 
)

2.2.2 分步执行 

# 加载包
library(DrugTargetMR)

# 调用函数
# 执行本地clump
mr_clump(
  local_exp_path = c(
    "./data_prepare/ieu-a-300.txt",
    "./data_prepare/ieu-a-301.txt",
    "./data_prepare/ieu-a-302.txt"
  ),
  bfile = "./data/1kg.v3/EUR" ,
  out_path = "./data_prepare"
)

# 执行MR分析
mr_local_clumped(
  local_exp_path = "./data_prepare/5e-08-clumped-10000kb-0.001r2.csv",
  local_out_path = c(
    "./data_prepare/ieu-a-7.txt",
    "./data_prepare/ieu-a-8.txt"
  ),
  out_path = "./result",
  # 指定bfile文件，需要解压之后的文件
  bfile = "./data/1kg.v3/EUR"
)

3. 分析结果

mr分析过程中得到的两个文件，可直接作为投稿文章的附表。

  • table_s1_harmonise_data.csv（harmonise之后的暴露和结局SNP数据）
  • table_s2_mr_results.csv（MR分析的结果，包括异质性分析和多效性检验的结果）。