{"id":61427,"date":"2024-10-30T15:36:05","date_gmt":"2024-10-30T07:36:05","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61427"},"modified":"2024-10-30T15:36:05","modified_gmt":"2024-10-30T07:36:05","slug":"%e7%ae%80%e5%8c%96gwas%e6%b5%81%e7%a8%8b%ef%bc%81snprelate%e5%8c%85%ef%bc%8c%e9%81%97%e4%bc%a0%e4%bf%a1%e6%81%af%e7%9a%84%e7%b2%be%e5%87%86%e7%bf%bb%e8%af%91%e5%ae%98%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61427","title":{"rendered":"\u7b80\u5316GWAS\u6d41\u7a0b\uff01SNPRelate\u5305\uff0c\u9057\u4f20\u4fe1\u606f\u7684\u7cbe\u51c6\u7ffb\u8bd1\u5b98\uff01"},"content":{"rendered":"\n

\u4eb2\u7231\u7684\u79d1\u7814\u5c0f\u4f19\u4f34\u4eec\uff0c\u5927\u5bb6\u597d\u5440\uff01\u6211\u662f\u4f60\u4eec\u7684\u5c0f\u5e08\u59b9\u3002\u4eca\u5929\uff0c\u6211\u8981\u7ed9\u5927\u5bb6\u5e26\u6765\u4e00\u4e2a\u8d85\u7ea7\u7ed9\u529b\u7684\u5de5\u5177\u4ecb\u7ecd\u2014\u2014SNPRelate\uff0c\u4e00\u4e2a\u5728\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684R\u5305\u3002<\/p>\n\n\n\n

\u4f60\u662f\u5426\u5728\u9762\u5bf9\u5e9e\u5927\u7684\u57fa\u56e0\u7ec4\u6570\u636e\u65f6\u611f\u5230\u65e0\u4ece\u4e0b\u624b\uff1f\u6216\u8005\u5728\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4\u5173\u8054\u7814\u7a76\uff08GWAS\uff09\u65f6\uff0c\u88ab\u590d\u6742\u7684\u8ba1\u7b97\u5206\u6790\u641e\u5f97\u7126\u5934\u70c2\u989d\uff1f\u522b\u62c5\u5fc3\uff0c\u4eca\u5929\u5c0f\u5e08\u59b9\u5c31\u5e26\u4f60\u4e00\u8d77\u63a2\u7d22SNPRelate\u7684\u5965\u79d8\uff0c\u8ba9\u4f60\u7684\u7814\u7a76\u5de5\u4f5c\u4e8b\u534a\u529f\u500d\uff01<\/p>\n\n\n\n

\u9996\u5148\uff0c\u6211\u4eec\u5f97\u77e5\u9053\uff0cGWAS\u5728\u5e2e\u52a9\u6211\u4eec\u63ed\u793a\u75be\u75c5\u548c\u6027\u72b6\u7684\u9057\u4f20\u57fa\u7840\u65b9\u9762\u53d1\u6325\u7740\u5de8\u5927\u4f5c\u7528\u3002\u4f46\u662f\uff0c\u968f\u4e4b\u800c\u6765\u7684\u8ba1\u7b97\u6311\u6218\u4e5f\u4e0d\u5bb9\u5c0f\u89d1\u3002\u5e78\u8fd0\u7684\u662f\uff0c\u6211\u4eec\u6709SNPRelate\u548c\u5b83\u7684\u597d\u4f19\u4f34gdsfmt\uff0c\u8fd9\u4e24\u4e2a\u9ad8\u6027\u80fd\u8ba1\u7b97R\u5305\uff0c\u5b83\u4eec\u80fd\u5728\u591a\u6838\u5bf9\u79f0\u591a\u5904\u7406\uff08SMP\uff09\u8ba1\u7b97\u673a\u67b6\u6784\u4e0a\uff0c\u52a0\u901fPCA\u548c\u57fa\u4e8e\u8840\u7f18\u540c\u6e90\uff08IBD\uff09\u7684\u4eb2\u7f18\u5173\u7cfb\u5206\u6790\u3002<\/p>\n\n\n\n

\u5728\u8fd9\u4e2a\u7f8e\u5999\u7684\u79d1\u7814\u65c5\u7a0b\u4e2d\uff0c\u5c0f\u5e08\u59b9\u5c06\u624b\u628a\u624b\u6559\u4f60\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u4ece\u5b89\u88c5\u5230\u6570\u636e\u51c6\u5907\uff0c\u518d\u5230\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0c\u6bcf\u4e00\u6b65\u90fd\u4f1a\u6709\u8be6\u7ec6\u7684\u6307\u5bfc\u3002\u65e0\u8bba\u4f60\u662f\u751f\u7269\u4fe1\u606f\u5b66\u7684\u65b0\u624b\uff0c\u8fd8\u662f\u5e0c\u671b\u63d0\u9ad8\u5206\u6790\u6548\u7387\u7684\u8d44\u6df1\u7814\u7a76\u8005\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u90fd\u4f1a\u5728\u8fd9\u6b21\u5b66\u4e60\u4e2d\u6709\u6240\u6536\u83b7\uff01\u8981\u6ce8\u610f\u7684\u662f<\/strong>\uff0c\u8fd9\u4e2a<\/strong>R\u5305\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\uff0c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668<\/strong>\u54e6<\/strong>\uff0c\u6b22\u8fce\u8054\u7cfb<\/strong>\u5c0f\u5e08\u59b9<\/strong>\u79df\u8d41\u6027\u4ef7\u6bd4\u9ad8\u7684\u670d\u52a1\u5668<\/strong>~<\/strong><\/p>\n\n\n\n

\u90a3\u4e48\uff0c\u8fd8\u7b49\u4ec0\u4e48\u5462\uff1f\u5feb\u8ddf\u5c0f\u5e08\u59b9\u4e00\u8d77\uff0c\u8e0f\u4e0a\u8fd9\u573a\u57fa\u56e0\u5965\u79d8\u63a2\u7d22\u4e4b\u65c5\u5427\uff01\u5982\u679c\u4f60\u5728\u64cd\u4f5c\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898\uff0c\u522b\u72b9\u8c6b\uff0c\u8bb0\u5f97\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6~<\/strong><\/strong><\/p>\n\n\n\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5f00\u59cb\u5b89\u88c5SNPRelate\u5305\u548c\u53e6\u5916\u9700\u8981\u7528\u5230\u7684\u5305\uff1a<\/p>\n\n\n\n

if (!requireNamespace(“BiocManager”, quietly=TRUE))<\/p>\n\n\n\n

    install.packages(“BiocManager”)<\/p>\n\n\n\n

BiocManager::install(“gdsfmt”)<\/p>\n\n\n\n

BiocManager::install(“SNPRelate”)<\/p>\n\n\n\n

\u5b89\u88c5\u597d\u4e86\u4e4b\u540e\uff0c\u8bb0\u5f97\u52a0\u8f7d\u8fd9\u4e9b\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\n

library(gdsfmt)<\/p>\n\n\n\n

library(SNPRelate)<\/p>\n\n\n\n

\u597d\u5566\uff0c\u5927\u5bb6\u51c6\u5907\u597d\u4e86\u5417\uff1f\u8ba9\u6211\u4eec\u73b0\u5728\u5c31\u542f\u7a0b\uff0c\u4e00\u8d77\u8e0f\u4e0a\u8fd9\u573a\u6fc0\u52a8\u4eba\u5fc3\u7684\u6570\u636e\u5206\u6790\u4e4b\u65c5\u5427\uff01<\/p>\n\n\n\n

\u4e00\u3001<\/strong> <\/strong>\u6570\u636e\u51c6\u5907<\/strong><\/strong><\/p>\n\n\n\n

    \n
  1.  SNPRelate\u652f\u6301\u7684\u6570\u636e\u683c\u5f0f<\/strong><\/strong><\/li>\n<\/ol>\n\n\n\n

    \u5728SNPRelate\u4e2d\uff0c\u6211\u4eec\u4e3b\u8981\u4f7f\u7528\u4e00\u79cd\u9ad8\u6548\u7684\u57fa\u56e0\u7ec4\u6570\u636e\u7ed3\u6784\uff08GDS\uff09\u6587\u4ef6\u683c\u5f0f\u6765\u5b58\u50a8\u5168\u57fa\u56e0\u7ec4\u5173\u8054\u7814\u7a76\uff08GWAS\uff09\u4e2d\u7684\u6570\u636e\u3002\u8fd9\u79cd\u683c\u5f0f\u7279\u522b\u9002\u5408\u4e8e\u5b58\u50a8\u5927\u91cf\u7684SNP\uff08\u5355\u6838\u82f7\u9178\u591a\u6001\u6027\uff09\u57fa\u56e0\u578b\u6570\u636e\u3002<\/p>\n\n\n\n

    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528snpgdsOpen\u51fd\u6570\u6765\u6253\u5f00\u4e00\u4e2aGDS\u6587\u4ef6\u3002\u8fd9\u4e2a\u51fd\u6570\u4f1a\u8fd4\u56de\u4e00\u4e2a\u6587\u4ef6\u5bf9\u8c61\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528\u8fd9\u4e2a\u5bf9\u8c61\u6765\u8bfb\u53d6\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u3002GDS\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u53d8\u91cf\u5305\u62ec\u6837\u672cID\uff08sample.id\uff09\u3001SNP ID\uff08snp.id\uff09\u3001SNP\u4f4d\u7f6e\uff08snp.position\uff09\u3001\u67d3\u8272\u4f53\u4fe1\u606f\uff08snp.chromosome\uff09\u3001SNP\u7b49\u4f4d\u57fa\u56e0\uff08snp.allele\uff09\u548c\u57fa\u56e0\u578b\u6570\u636e\uff08genotype\uff09\u3002<\/p>\n\n\n\n

      \n
    • sample.id\uff1a\u6bcf\u4e2a\u6837\u672c\u7684\u552f\u4e00\u6807\u8bc6\u7b26\u3002<\/li>\n\n\n\n
    • snp.id\uff1a\u6bcf\u4e2aSNP\u7684\u552f\u4e00\u6807\u8bc6\u7b26\u3002<\/li>\n\n\n\n
    • snp.position\uff1a\u6bcf\u4e2aSNP\u5728\u67d3\u8272\u4f53\u4e0a\u7684\u57fa\u7840\u4f4d\u7f6e\u3002<\/li>\n\n\n\n
    • snp.chromosome\uff1a\u6bcf\u4e2a\u67d3\u8272\u4f53\u7684\u6574\u6570\u6216\u5b57\u7b26\u6620\u5c04\u3002<\/li>\n\n\n\n
    • snp.allele\uff1aSNP\u7684\u7b49\u4f4d\u57fa\u56e0\u4fe1\u606f\u3002<\/li>\n\n\n\n
    • genotype\uff1aSNP\u57fa\u56e0\u578b\u77e9\u9635\u3002<\/li>\n<\/ul>\n\n\n\n

      # \u6253\u5f00\u4e00\u4e2aGDS\u6587\u4ef6<\/p>\n\n\n\n

      (genofile <- snpgdsOpen(snpgdsExampleFileName()))<\/p>\n\n\n\n

      # (genofile <- snpgdsOpen(‘hapmap_geno.gds’))  # \u4e5f\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u5c0f\u5e08\u59b9\u63d0\u4f9b\u7684\u6587\u4ef6\u54e6<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      \u6211\u4eec\u53ef\u4ee5\u67e5\u770bGDS\u6587\u4ef6\u4e2d\u5b58\u50a8\u4e86\u54ea\u4e9b\u6570\u636e\u3002snpgdsSummary\u51fd\u6570\u53ef\u4ee5\u63d0\u4f9b\u6587\u4ef6\u4e2d\u57fa\u56e0\u578b\u7684\u6982\u8981\u4fe1\u606f\u3002<\/p>\n\n\n\n

      snpgdsSummary(genofile)<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528read.gdsn\u51fd\u6570\u6765\u83b7\u53d6GDS\u6587\u4ef6\u4e2d\u7684\u7279\u5b9a\u6570\u636e\u3002\u4f8b\u5982\uff0c\u83b7\u53d6\u524d5\u4e2aSNP\u7684ID\uff1a<\/p>\n\n\n\n

      # \u83b7\u53d6\u524d5\u4e2aSNP\u7684ID<\/p>\n\n\n\n

      head(read.gdsn(index.gdsn(genofile, “snp.id”)))<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      \u6216\u8005\u83b7\u53d6\u6837\u672c\u6ce8\u91ca\u4fe1\u606f\uff0c\u6bd4\u5982\u4eba\u53e3\u7fa4\u4f53\u4fe1\u606f\uff1a<\/p>\n\n\n\n

      # \u8bfb\u53d6\u4eba\u53e3\u7fa4\u4f53\u4fe1\u606f<\/p>\n\n\n\n

      pop <- read.gdsn(index.gdsn(genofile, path=”sample.annot\/pop.group”))<\/p>\n\n\n\n

      table(pop)<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      \u5b8c\u6210\u6570\u636e\u8bfb\u53d6\u540e\uff0c\u4e00\u5b9a\u8981\u8bb0\u5f97\u5173\u95edGDS\u6587\u4ef6\u4ee5\u91ca\u653e\u8d44\u6e90\u54e6\uff01<\/p>\n\n\n\n

      # \u5173\u95edGDS\u6587\u4ef6<\/p>\n\n\n\n

      snpgdsClose(genofile)<\/p>\n\n\n\n

        \n
      •  \u521b\u5efaGDS\u6587\u4ef6<\/strong><\/li>\n<\/ul>\n\n\n\n

        snpgdsCreateGeno()\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u521b\u5efaGDS\u6587\u4ef6\u3002\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u4e00\u4e2aSNP\u57fa\u56e0\u578b\u77e9\u9635\u3002\u8f93\u5165\u57fa\u56e0\u578b\u77e9\u9635\u4e2d\u53ef\u80fd\u5b58\u50a8\u7684\u503c\u67090\u30011\u30012\u548c\u5176\u4ed6\u503c\u3002\u201c0\u201d\u8868\u793a\u4e24\u4e2aB\u7b49\u4f4d\u57fa\u56e0\uff0c\u201c1\u201d\u8868\u793a\u4e00\u4e2aA\u7b49\u4f4d\u57fa\u56e0\u548c\u4e00\u4e2aB\u7b49\u4f4d\u57fa\u56e0\uff0c\u201c2\u201d\u8868\u793a\u4e24\u4e2aA\u7b49\u4f4d\u57fa\u56e0\uff0c\u5176\u4ed6\u503c\u8868\u793a\u7f3a\u5931\u57fa\u56e0\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n\n\n\n

        # \u52a0\u8f7d\u6570\u636e<\/p>\n\n\n\n

        data(hapmap_geno)<\/p>\n\n\n\n

        # \u521b\u5efa\u4e00\u4e2agds\u6587\u4ef6<\/p>\n\n\n\n

        snpgdsCreateGeno(“test.gds”, genmat = hapmap_geno$genotype,<\/p>\n\n\n\n

            sample.id = hapmap_geno$sample.id, snp.id = hapmap_geno$snp.id,<\/p>\n\n\n\n

            snp.chromosome = hapmap_geno$snp.chromosome,<\/p>\n\n\n\n

            snp.position = hapmap_geno$snp.position,<\/p>\n\n\n\n

            snp.allele = hapmap_geno$snp.allele, snpfirstdim=TRUE)<\/p>\n\n\n\n

        # \u6253\u5f00GDS\u6587\u4ef6<\/p>\n\n\n\n

        (genofile <- snpgdsOpen(“test.gds”))<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        # \u5173\u95edGDS\u6587\u4ef6<\/p>\n\n\n\n

        snpgdsClose(genofile)<\/p>\n\n\n\n

        \u9664\u4e86\u4e0a\u9762\u8fd9\u79cd\u65b9\u6cd5\uff0cgdsfmt\u5305\u4e5f\u53ef\u4ee5\u7528\u6765\u521b\u5efaGDS\u6587\u4ef6\uff0c\u51fd\u6570createfn.gds(), add.gdsn(), put.attr.gdsn(), write.gdsn() \u548c index.gdsn() \u90fd\u5728gdsfmt\u5305\u4e2d\u5b9a\u4e49\u3002\u8fd9\u90e8\u5206\u5177\u4f53\u793a\u4f8b\u4ee3\u7801\u53ef\u4ee5\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6~<\/p>\n\n\n\n

          \n
        •  \u4ece<\/strong>\u5176\u4ed6<\/strong>\u6587\u4ef6\u8f6c\u6362\u683c\u5f0f<\/strong><\/li>\n<\/ul>\n\n\n\n

          SNPRelate\u5305\u63d0\u4f9b\u4e86snpgdsPED2GDS()\u548csnpgdsBED2GDS()\u51fd\u6570\uff0c\u7528\u4e8e\u5c06PLINK\u6587\u672c\/\u4e8c\u8fdb\u5236\u6587\u4ef6\u8f6c\u6362\u4e3aGDS\u6587\u4ef6\uff1a<\/p>\n\n\n\n

          # PLINK BED\u6587\u4ef6\uff0c\u4f7f\u7528SNPRelate\u5305\u4e2d\u7684\u4f8b\u5b50<\/p>\n\n\n\n

          bed.fn <- system.file(“extdata”, “plinkhapmap.bed.gz”, package=”SNPRelate”)<\/p>\n\n\n\n

          fam.fn <- system.file(“extdata”, “plinkhapmap.fam.gz”, package=”SNPRelate”)<\/p>\n\n\n\n

          bim.fn <- system.file(“extdata”, “plinkhapmap.bim.gz”, package=”SNPRelate”)<\/p>\n\n\n\n

          \u6216\u8005\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u4f60\u81ea\u5df1\u7684PLINK\u6587\u4ef6\uff1a<\/p>\n\n\n\n

          bed.fn <- “C:\/your_folder\/your_plink_file.bed”<\/p>\n\n\n\n

          fam.fn <- “C:\/your_folder\/your_plink_file.fam”<\/p>\n\n\n\n

          bim.fn <- “C:\/your_folder\/your_plink_file.bim”<\/p>\n\n\n\n

          \u6700\u540e\uff0c\u4f7f\u7528snpgdsBED2GDS\u8fdb\u884c\u4e00\u6b65\u8f6c\u6362\uff1a<\/p>\n\n\n\n

          # \u8f6c\u6362<\/p>\n\n\n\n

          snpgdsBED2GDS(bed.fn, fam.fn, bim.fn, “test.gds”)<\/p>\n\n\n\n

          \"\"<\/figure>\n\n\n\n

          snpgdsSummary(“test.gds”)<\/p>\n\n\n\n

          \"\"<\/figure>\n\n\n\n

          \u53e6\u5916\uff0c\u5bf9\u4e8eVCF\u6587\u4ef6\u7684\u8f6c\u6362\uff0c\u9664\u4e86\u53ef\u4ee5\u7528SNPRelate\u5305\u63d0\u4f9b\u7684snpgdsVCF2GDS()\u51fd\u6570\uff0c\u8fd8\u80fd\u7528SeqArray\u5305\u91cc\u7684seqVCF2GDS()\u51fd\u6570\u6765\u641e\u5b9a\uff0c\u800c\u4e14\u5b83\u8fd8\u80fd\u4e00\u6b21\u6027\u5904\u7406\u597d\u51e0\u4e2aVCF\u6587\u4ef6\u3002\u8fd9\u4e2a\u51fd\u6570\u4f1a\u9ed8\u8ba4\u628a\u6570\u636e\u538b\u7f29\u8d77\u6765\uff0c\u7701\u7a7a\u95f4\u53c8\u9ad8\u6548\u3002\u5982\u679c\u4f60\u5728\u7814\u7a76\u5168\u57fa\u56e0\u7ec4\u6d4b\u5e8f\u6570\u636e\uff0c\u90a3SeqArray\u5305\u7edd\u5bf9\u662f\u4f60\u7684\u597d\u5e2e\u624b\u3002\u60f3\u8981\u4e86\u89e3\u66f4\u591a\u7ec6\u8282\uff0c\u6b22\u8fce\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6~<\/p>\n\n\n\n

          \u4e8c<\/strong>\u3001<\/strong> <\/strong>\u6570\u636e\u5206\u6790<\/strong><\/strong><\/p>\n\n\n\n

          \u63a5\u4e0b\u6765\uff0c\u8ddf\u7740\u5c0f\u5e08\u59b9\u4e00\u8d77\u63a2\u7d22\u5982\u4f55\u5229\u7528\u8fd9\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u5305\u62ec\u57fa\u4e8eLD\u7684SNP\u4fee\u526a\u3001\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u3001Fst<\/sub>\u4f30\u8ba1\u548c\u4eb2\u7f18\u5173\u7cfb\u5206\u6790\u3002\u51c6\u5907\u597d\u4e86\u5417\uff1f\u8ba9\u6211\u4eec\u5f00\u59cb\u5427\uff01<\/p>\n\n\n\n

            \n
          1. \u57fa\u4e8eLD\u7684SNP\u4fee\u526a<\/strong><\/strong><\/li>\n<\/ol>\n\n\n\n

            LD\uff0c\u6216\u79f0\u8fde\u9501\u4e0d\u5e73\u8861\uff0c\u63cf\u8ff0\u7684\u662f\u67d0\u4e9b\u57fa\u56e0\u578b\u5728\u4e00\u8d77\u51fa\u73b0\u9891\u7387\u8f83\u9ad8\u7684\u73b0\u8c61\u3002\u5f53\u6211\u4eec\u51c6\u5907\u8fdb\u884c\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u4e4b\u524d\uff0c\u901a\u5e38\u8981\u5148\u5bf9SNPs\u8fdb\u884cLD\u4fee\u526a\u3002\u7ecf\u8fc7\u4fee\u526a\u540e\uff0c\u8fd9\u4e9bSNP\u4e4b\u95f4\u5927\u81f4\u5904\u4e8e\u8fde\u9501\u5e73\u8861\uff0c\u53ef\u4ee5\u907f\u514d\u5728\u4e3b\u6210\u5206\u5206\u6790\u548c\u4eb2\u7f18\u5173\u7cfb\u5206\u6790\u4e2dSNP\u7c07\u7684\u5f3a\u5f71\u54cd\u3002<\/p>\n\n\n\n

            set.seed(1000)<\/p>\n\n\n\n

            # \u6253\u5f00GDS\u6587\u4ef6<\/p>\n\n\n\n

            genofile <- snpgdsOpen(snpgdsExampleFileName())<\/p>\n\n\n\n

            # \u53ef\u4ee5\u5c1d\u8bd5\u4e0d\u540c\u7684LD\u9608\u503c\u8fdb\u884c\u5206\u6790<\/p>\n\n\n\n

            snpset <- snpgdsLDpruning(genofile, ld.threshold=0.2)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n
            \"\"<\/figure>\n\n\n\n

            str(snpset)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n

            names(snpset)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n

            # \u83b7\u53d6\u6240\u6709\u9009\u62e9\u7684 snp id<\/p>\n\n\n\n

            snpset.id <- unlist(unname(snpset))<\/p>\n\n\n\n

            head(snpset.id)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n
              \n
            • \u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09<\/strong><\/strong><\/li>\n<\/ul>\n\n\n\n

              SNPRelate\u4e2d\u7684PCA\u51fd\u6570\u5305\u62ec\u4ece\u57fa\u56e0\u578b\u8ba1\u7b97\u9057\u4f20\u534f\u65b9\u5dee\u77e9\u9635\u3001\u8ba1\u7b97\u6837\u672c\u8f7d\u8377\u4e0e\u6bcf\u4e2aSNP\u57fa\u56e0\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u3001\u8ba1\u7b97SNP\u7279\u5f81\u5411\u91cf\uff08\u8f7d\u8377\uff09\uff0c\u4ee5\u53ca\u4ece\u6307\u5b9a\u7684SNP\u7279\u5f81\u5411\u91cf\u4f30\u8ba1\u65b0\u6570\u636e\u96c6\u7684\u6837\u672c\u8f7d\u8377\u3002<\/p>\n\n\n\n

              # \u8fd0\u884cPCA<\/p>\n\n\n\n

              pca <- snpgdsPCA(genofile, snp.id=snpset.id, num.thread=2)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u524d\u51e0\u4e2a\u4e3b\u6210\u5206\u6240\u5360\u7684\u53d8\u5f02\u767e\u5206\u6bd4\u3002\u53ef\u4ee5\u660e\u663e\u770b\u51fa\uff0c\u524d\u4e24\u4e2a\u7279\u5f81\u5411\u91cf\u5728\u603b\u4f53\u53d8\u5f02\u4e2d\u5360\u636e\u4e86\u6700\u5927\u7684\u6bd4\u4f8b\uff0c\u5c3d\u7ba1\u603b\u53d8\u5f02\u4ecd\u7136\u4e0d\u8db3\u56db\u5206\u4e4b\u4e00\u3002<\/p>\n\n\n\n

              # \u53d8\u5f02\u6bd4\u4f8b\uff08%\uff09<\/p>\n\n\n\n

              pc.percent <- pca$varprop * 100<\/p>\n\n\n\n

              head(round(pc.percent, 2))<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u5982\u679c\u6ca1\u6709\u5148\u524d\u7684\u4eba\u53e3\u4fe1\u606f\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\uff1a<\/p>\n\n\n\n

              # \u521b\u5efa\u6570\u636e\u6846<\/p>\n\n\n\n

              tab <- data.frame(sample.id = pca$sample.id,<\/p>\n\n\n\n

                  EV1 = pca$eigenvect[,1],    # \u7b2c\u4e00\u4e2a\u7279\u5f81\u5411\u91cf<\/p>\n\n\n\n

                  EV2 = pca$eigenvect[,2],    # \u7b2c\u4e8c\u4e2a\u7279\u5f81\u5411\u91cf<\/p>\n\n\n\n

                  stringsAsFactors = FALSE)<\/p>\n\n\n\n

              head(tab)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u7136\u540e\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u8fd9\u4e24\u4e2a\u7279\u5f81\u5411\u91cf\u7684\u6563\u70b9\u56fe\uff1a<\/p>\n\n\n\n

              # \u7ed8\u56fe<\/p>\n\n\n\n

              plot(tab$EV2, tab$EV1, xlab=”eigenvector 2″, ylab=”eigenvector 1″)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u5982\u679c\u6709\u4eba\u53e3\u4fe1\u606f\uff0c\u6211\u4eec\u53ef\u4ee5\u8fd9\u6837\u5904\u7406\uff1a<\/p>\n\n\n\n

              # \u83b7\u53d6\u6837\u672cID<\/p>\n\n\n\n

              sample.id <- read.gdsn(index.gdsn(genofile, “sample.id”))<\/p>\n\n\n\n

              # \u83b7\u53d6\u4eba\u53e3\u4fe1\u606f<\/p>\n\n\n\n

              pop_code <- read.gdsn(index.gdsn(genofile, “sample.annot\/pop.group”))<\/p>\n\n\n\n

              # \u5047\u8bbe\u6837\u672cID\u7684\u987a\u5e8f\u4e0e\u4eba\u53e3\u4ee3\u7801\u76f8\u540c<\/p>\n\n\n\n

              head(cbind(sample.id, pop_code))<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4eba\u53e3\u4fe1\u606f\u7684\u6570\u636e\u6846\uff1a<\/p>\n\n\n\n

              # \u521b\u5efa\u6570\u636e\u6846<\/p>\n\n\n\n

              tab <- data.frame(sample.id = pca$sample.id,<\/p>\n\n\n\n

                  pop = factor(pop_code)[match(pca$sample.id, sample.id)],<\/p>\n\n\n\n

                  EV1 = pca$eigenvect[,1],    # \u7b2c\u4e00\u4e2a\u7279\u5f81\u5411\u91cf<\/p>\n\n\n\n

                  EV2 = pca$eigenvect[,2],    # \u7b2c\u4e8c\u4e2a\u7279\u5f81\u5411\u91cf<\/p>\n\n\n\n

                  stringsAsFactors = FALSE)<\/p>\n\n\n\n

              head(tab)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u4e0d\u540c\u4eba\u53e3\u7fa4\u4f53\u7684\u6563\u70b9\u56fe\uff1a<\/p>\n\n\n\n

              # \u7ed8\u56fe<\/p>\n\n\n\n

              plot(tab$EV2, tab$EV1, col=as.integer(tab$pop), xlab=”eigenvector 2″, ylab=”eigenvector 1″)<\/p>\n\n\n\n

              legend(“bottomright”, legend=levels(tab$pop), pch=”o”, col=1:nlevels(tab$pop))<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u7ed8\u5236\u524d\u56db\u4e2a\u4e3b\u6210\u5206\u7684\u6210\u5bf9\u56fe\uff1a<\/p>\n\n\n\n

              lbls <- paste(“PC”, 1:4, “\\n”, format(pc.percent[1:4], digits=2), “%”, sep=””)<\/p>\n\n\n\n

              pairs(pca$eigenvect[,1:4], col=tab$pop, labels=lbls)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u5bf9\u4e8e\u524d\u51e0\u4e2a\u4e3b\u6210\u5206\u7684\u5e73\u884c\u5750\u6807\u56fe\uff1a<\/p>\n\n\n\n

              library(MASS)<\/p>\n\n\n\n

              datpop <- factor(pop_code)[match(pca$sample.id, sample.id)]<\/p>\n\n\n\n

              parcoord(pca$eigenvect[,1:16], col=datpop)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              \u8981\u8ba1\u7b97\u7279\u5f81\u5411\u91cf\u4e0eSNP\u57fa\u56e0\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff1a<\/p>\n\n\n\n

              # \u83b7\u53d6\u67d3\u8272\u4f53\u7d22\u5f15<\/p>\n\n\n\n

              chr <- read.gdsn(index.gdsn(genofile, “snp.chromosome”))<\/p>\n\n\n\n

              CORR <- snpgdsPCACorr(pca, genofile, eig.which=1:4)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              savepar <- par(mfrow=c(2,1), mai=c(0.45, 0.55, 0.1, 0.25))<\/p>\n\n\n\n

              for (i in 1:2)<\/p>\n\n\n\n

              {plot(abs(CORR$snpcorr[i,]), ylim=c(0,1), xlab=””, ylab=paste(“PC”, i),<\/p>\n\n\n\n

                      col=chr, pch=”+”)}<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              par(savepar)<\/p>\n\n\n\n

                \n
              • Fst\u4f30\u8ba1<\/strong><\/strong><\/li>\n<\/ul>\n\n\n\n

                Fst\uff0c\u5373\u56fa\u5b9a\u6307\u6570\uff0c\u662f\u8861\u91cf\u79cd\u7fa4\u95f4\u9057\u4f20\u5206\u5316\u7684\u4e00\u4e2a\u7ecf\u5178\u6307\u6807\u3002\u901a\u8fc7Fst\u4f30\u8ba1\uff0c\u6211\u4eec\u53ef\u4ee5\u4e86\u89e3\u4e0d\u540c\u7fa4\u4f53\u95f4\u7684\u9057\u4f20\u5dee\u5f02\u3002\u5728\u62e5\u6709\u4e24\u4e2a\u6216\u66f4\u591a\u79cd\u7fa4\u7684\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Weir\u548cCockerham\uff081984\uff09\u7684\u65b9\u6cd5\u6765\u4f30\u8ba1Fst\u3002<\/p>\n\n\n\n

                # \u83b7\u53d6\u6837\u672cID<\/p>\n\n\n\n

                sample.id <- read.gdsn(index.gdsn(genofile, “sample.id”))<\/p>\n\n\n\n

                # \u83b7\u53d6\u79cd\u7fa4\u4fe1\u606f<\/p>\n\n\n\n

                pop_code <- read.gdsn(index.gdsn(genofile, “sample.annot\/pop.group”))<\/p>\n\n\n\n

                # \u9009\u62e9\u4e24\u4e2a\u79cd\u7fa4\uff1aHCB\u548cJPT<\/p>\n\n\n\n

                flag <- pop_code %in% c(“HCB”, “JPT”)<\/p>\n\n\n\n

                samp.sel <- sample.id[flag]<\/p>\n\n\n\n

                pop.sel <- pop_code[flag]<\/p>\n\n\n\n

                v <- snpgdsFst(genofile, sample.id=samp.sel, population=as.factor(pop.sel), method=”W&C84″)<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                v$Fst        # Weir\u548cCockerham\u52a0\u6743Fst\u4f30\u8ba1<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                v$MeanFst    # Weir\u548cCockerham\u5e73\u5747Fst\u4f30\u8ba1<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                summary(v$FstSNP)<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                \u5982\u679c\u6211\u4eec\u60f3\u8981\u5bf9\u591a\u4e2a\u79cd\u7fa4\u8fdb\u884cFst\u4f30\u8ba1\uff0c\u6bd4\u5982CEU\u3001HCB\u3001JPT\u548cYRI\uff0c\u6211\u4eec\u9700\u8981\u5148\u6392\u9664\u540e\u4ee3\u3002<\/p>\n\n\n\n

                # \u6392\u9664\u540e\u4ee3<\/p>\n\n\n\n

                father <- read.gdsn(index.gdsn(genofile, “sample.annot\/father.id”))<\/p>\n\n\n\n

                mother <- read.gdsn(index.gdsn(genofile, “sample.annot\/mother.id”))<\/p>\n\n\n\n

                flag <- (father==””) & (mother==””)<\/p>\n\n\n\n

                samp.sel <- sample.id[flag]<\/p>\n\n\n\n

                pop.sel <- pop_code[flag]<\/p>\n\n\n\n

                v <- snpgdsFst(genofile, sample.id=samp.sel, population=as.factor(pop.sel), method=”W&C84″)<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                v$Fst        # Weir\u548cCockerham\u52a0\u6743Fst\u4f30\u8ba1<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                v$MeanFst    # Weir\u548cCockerham\u5e73\u5747Fst\u4f30\u8ba1<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n

                summary(v$FstSNP)<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n
                  \n
                • \u4eb2\u7f18\u5173\u7cfb\u5206\u6790<\/strong><\/strong><\/li>\n<\/ul>\n\n\n\n

                  \u5728\u4eb2\u7f18\u5173\u7cfb\u5206\u6790\u4e2d\uff0cSNPRelate\u4e2d\u53ef\u4ee5\u4f7f\u7528\u77e9\u4f30\u8ba1\uff08Method of Moments\uff0cMoM\uff09\u65b9\u6cd5\u6216\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\uff08MLE\uff09\u6765\u8fdb\u884c\u57fa\u4e8e\u8840\u7f18\u540c\u6e90\uff08IBD\uff09\u7684\u4eb2\u7f18\u5173\u7cfb\u5206\u6790\u3002\u5bf9\u4e8e\u8fd9\u4e24\u79cd\u65b9\u6cd5\uff0c\u5efa\u8bae\u4f7f\u7528\u8fde\u9501\u4e0d\u5e73\u8861\u4fee\u526a\u540e\u7684SNP\u96c6\u5408\u54e6~<\/p>\n\n\n\n

                  \uff081\uff09<\/strong>\u4f7f\u7528PLINK\u65b9\u6cd5\u7684\u77e9\u4f30\u8ba1\uff08MoM\uff09\u4f30\u7b97IBD<\/strong><\/strong><\/p>\n\n\n\n

                  # \u83b7\u53d6\u6837\u672cID<\/p>\n\n\n\n

                  sample.id <- read.gdsn(index.gdsn(genofile, “sample.id”))<\/p>\n\n\n\n

                  # \u7b5b\u9009YRI\u4eba\u7fa4\u7684\u6837\u672cID<\/p>\n\n\n\n

                  YRI.id <- sample.id[pop_code == “YRI”]<\/p>\n\n\n\n

                  # \u4f30\u7b97IBD\u7cfb\u6570<\/p>\n\n\n\n

                  ibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id, snp.id=snpset.id,<\/p>\n\n\n\n

                      maf=0.05, missing.rate=0.05, num.thread=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  # \u6784\u5efa\u6570\u636e\u6846<\/p>\n\n\n\n

                  ibd.coeff <- snpgdsIBDSelection(ibd)<\/p>\n\n\n\n

                  head(ibd.coeff)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  # \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n\n\n\n

                  plot(ibd.coeff$k0, ibd.coeff$k1, xlim=c(0,1), ylim=c(0,1),<\/p>\n\n\n\n

                      xlab=”k0″, ylab=”k1″, main=”YRI\u6837\u672c (MoM)”)<\/p>\n\n\n\n

                  lines(c(0,1), c(1,0), col=”red”, lty=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u56fe\u91cc\u7684\u6bcf\u4e2a\u70b9\u90fd\u4ee3\u8868\u4e00\u5bf9\u4e2a\u4f53\uff0c\u6a2a\u8f74k0\u8868\u793a\u4ed6\u4eec\u6ca1\u5171\u4eab\u57fa\u56e0\u7684\u6982\u7387\uff0c\u7eb5\u8f74k1\u8868\u793a\u5171\u4eab\u4e86\u4e00\u4e2a\u57fa\u56e0\u7684\u6982\u7387\u3002\u5982\u679c\u4e24\u4e2a\u4e2a\u4f53\u6ca1\u5565\u4eb2\u7f18\u5173\u7cfb\uff0c\u70b9\u4f1a\u9760\u5728\u56fe\u7684\u53f3\u4e0b\u89d2\uff1b\u5982\u679c\u662f\u4eb2\u5144\u5f1f\uff0c\u70b9\u4f1a\u9760\u5728\u5de6\u4e0a\u89d2\u3002\u90a3\u6761\u7ea2\u8272\u7684\u865a\u7ebf\u8868\u793ak0\u548ck1\u76f8\u7b49\uff0c\u4e5f\u5c31\u662f\u5171\u4eab\u548c\u4e0d\u5171\u4eab\u57fa\u56e0\u7684\u6982\u7387\u5dee\u4e0d\u591a\u3002\u5982\u679c\u70b9\u79bb\u8fd9\u6761\u7ebf\u8fdc\uff0c\u5c24\u5176\u662f\u8fdc\u79bb\u53f3\u4e0b\u89d2\uff0c\u90a3\u53ef\u80fd\u8bf4\u660e\u4e2a\u4f53\u95f4\u6709\u8f83\u8fd1\u7684\u4eb2\u7f18\u5173\u7cfb\u54e6\uff01<\/p>\n\n\n\n

                  \uff08<\/strong>2<\/strong>\uff09<\/strong>\u4f7f\u7528\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\uff08MLE\uff09\u4f30\u7b97IBD\u7cfb\u6570<\/strong><\/p>\n\n\n\n

                  \u9664\u4e86\u524d\u9762\u63d0\u5230\u7684PLINK\u65b9\u6cd5\u7684\u77e9\u4f30\u8ba1\uff08MoM\uff09\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\uff08MLE\uff09\u6765\u66f4\u7cbe\u786e\u5730\u8fdb\u884c\u8fd9\u4e00\u4f30\u7b97\u3002\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\uff08MLE\uff09\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u5bfb\u627e\u80fd\u591f\u4f7f\u89c2\u6d4b\u6570\u636e\u51fa\u73b0\u6982\u7387\uff08\u4f3c\u7136\u51fd\u6570\uff09\u6700\u5927\u5316\u7684\u53c2\u6570\u503c\u6765\u8fdb\u884c\u53c2\u6570\u4f30\u8ba1\u3002\u5728\u9057\u4f20\u5b66\u4e2d\uff0cMLE\u88ab\u5e7f\u6cdb\u7528\u4e8e\u4f30\u7b97\u9057\u4f20\u53c2\u6570\uff0c\u5305\u62ecIBD\u3002<\/p>\n\n\n\n

                  \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4ece\u5927\u91cf\u7684\u5355\u6838\u82f7\u9178\u591a\u6001\u6027\uff08SNP\uff09\u4e2d\u968f\u673a\u9009\u62e9\u4e00\u90e8\u5206\u6765\u8fdb\u884c\u5206\u6790\u3002\u8fd9\u6837\u505a\u53ef\u4ee5\u51cf\u5c11\u8ba1\u7b97\u91cf\uff0c\u540c\u65f6\u4ecd\u7136\u80fd\u591f\u83b7\u5f97\u4ee3\u8868\u6027\u7684\u7ed3\u679c\u3002\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u968f\u673a\u9009\u62e9\u4e861500\u4e2aSNP\u3002<\/p>\n\n\n\n

                  # \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u53ef\u91cd\u590d\u6027<\/p>\n\n\n\n

                  set.seed(100)<\/p>\n\n\n\n

                  # \u4ece\u9009\u5b9a\u7684SNP\u96c6\u5408\u4e2d\u968f\u673a\u9009\u62e91500\u4e2aSNP<\/p>\n\n\n\n

                  snp.id <- sample(snpset.id, 1500)<\/p>\n\n\n\n

                  \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528snpgdsIBDMLE\u51fd\u6570\u6765\u8fdb\u884cIBD\u7684\u4f30\u7b97\u3002\u8fd9\u4e2a\u51fd\u6570\u63a5\u53d7\u57fa\u56e0\u6587\u4ef6\u3001\u6837\u672cID\u3001SNP ID\u7b49\u53c2\u6570\uff0c\u5e76\u8fd4\u56de\u6bcf\u5bf9\u4e2a\u4f53\u4e4b\u95f4\u7684IBD\u4f30\u8ba1\u503c\u3002<\/p>\n\n\n\n

                  # \u4f7f\u7528MLE\u65b9\u6cd5\u4f30\u7b97IBD<\/p>\n\n\n\n

                  ibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id, snp.id=snp.id,<\/p>\n\n\n\n

                      maf=0.05, missing.rate=0.05, num.thread=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u6307\u5b9a\u4e86\u6700\u5c0f\u7b49\u4f4d\u57fa\u56e0\u9891\u7387\uff08MAF\uff09\u4e3a0.05\uff0c\u7f3a\u5931\u7387\u4e3a0.05\uff0c\u5e76\u4f7f\u75282\u4e2a\u7ebf\u7a0b\u6765\u52a0\u901f\u8ba1\u7b97\u3002\u51fd\u6570\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5305\u542bIBD\u4f30\u8ba1\u503c\u7684\u5bf9\u8c61\u3002<\/p>\n\n\n\n

                  \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528snpgdsIBDSelection\u51fd\u6570\u6765\u63d0\u53d6IBD\u7cfb\u6570\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5728\u4e00\u4e2a\u6570\u636e\u6846\u4e2d\u3002<\/p>\n\n\n\n

                  # \u63d0\u53d6IBD\u7cfb\u6570<\/p>\n\n\n\n

                  ibd.coeff <- snpgdsIBDSelection(ibd)<\/p>\n\n\n\n

                  # \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n\n\n\n

                  plot(ibd.coeff$k0, ibd.coeff$k1, xlim=c(0,1), ylim=c(0,1),<\/p>\n\n\n\n

                      xlab=”k0″, ylab=”k1″, main=”YRI\u6837\u672c (MLE)”)<\/p>\n\n\n\n

                  lines(c(0,1), c(1,0), col=”red”, lty=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \uff08<\/strong>3<\/strong>\uff09<\/strong>\u4f7f\u7528KING\u65b9\u6cd5\u7684\u77e9\u4f30\u8ba1\u63a8\u65ad\u4eb2\u7f18\u5173\u7cfb<\/strong><\/strong><\/p>\n\n\n\n

                  \u5728\u9057\u4f20\u5b66\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u63a8\u65ad\u4e2a\u4f53\u4e4b\u95f4\u7684\u4eb2\u7f18\u5173\u7cfb\uff0c\u5c24\u5176\u662f\u5728\u5b58\u5728\u4eba\u7fa4\u5206\u5c42\u7684\u60c5\u51b5\u4e0b\u3002KING\u65b9\u6cd5\u662f\u4e00\u79cd\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u6548\u7684\u5de5\u5177\uff0c\u5b83\u53ef\u4ee5\u5728\u5bb6\u65cf\u5185\u548c\u5bb6\u65cf\u95f4\u63a8\u65ad\u4eb2\u7f18\u5173\u7cfb\uff0c\u5e76\u4e14\u5bf9\u4eba\u7fa4\u5206\u5c42\u5177\u6709\u9c81\u68d2\u6027\u3002<\/p>\n\n\n\n

                  \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u83b7\u53d6\u5bb6\u65cf\u4fe1\u606f\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6GDS\u6587\u4ef6\u4e2d\u7684\u5bb6\u65cfID\u6765\u5b9e\u73b0\u3002\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4e86YRI\u6837\u672c\u7684\u5bb6\u65cfID\u3002<\/p>\n\n\n\n

                  # \u8bfb\u53d6\u5bb6\u65cfID<\/p>\n\n\n\n

                  family.id <- read.gdsn(index.gdsn(genofile, “sample.annot\/family.id”))<\/p>\n\n\n\n

                  # \u6839\u636eYRI\u6837\u672c\u7684ID\u7b5b\u9009\u5bf9\u5e94\u7684\u5bb6\u65cfID<\/p>\n\n\n\n

                  family.id <- family.id[match(YRI.id, sample.id)]<\/p>\n\n\n\n

                  # \u67e5\u770b\u5bb6\u65cfID\u7684\u5206\u5e03\u60c5\u51b5<\/p>\n\n\n\n

                  table(family.id)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528snpgdsIBDKING\u51fd\u6570\u6765\u8fdb\u884c\u4eb2\u7f18\u5173\u7cfb\u7684\u63a8\u65ad\u3002\u8fd9\u4e2a\u51fd\u6570\u63a5\u53d7\u57fa\u56e0\u6587\u4ef6\u3001\u6837\u672cID\u3001\u5bb6\u65cfID\u7b49\u53c2\u6570\uff0c\u5e76\u8fd4\u56de\u6bcf\u5bf9\u4e2a\u4f53\u4e4b\u95f4\u7684\u4eb2\u7f18\u5173\u7cfb\u4f30\u8ba1\u503c\u3002<\/p>\n\n\n\n

                  # \u4f7f\u7528KING\u65b9\u6cd5\u63a8\u65ad\u4eb2\u7f18\u5173\u7cfb<\/p>\n\n\n\n

                  ibd.robust <- snpgdsIBDKING(genofile, sample.id=YRI.id,<\/p>\n\n\n\n

                      family.id=family.id, num.thread=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528snpgdsIBDSelection\u51fd\u6570\u6765\u63d0\u53d6\u4eb2\u7f18\u5173\u7cfb\u4f30\u8ba1\u503c\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5728\u4e00\u4e2a\u6570\u636e\u6846\u4e2d\u3002<\/p>\n\n\n\n

                  # \u63d0\u53d6\u4eb2\u7f18\u5173\u7cfb\u4f30\u8ba1\u503c<\/p>\n\n\n\n

                  dat <- snpgdsIBDSelection(ibd.robust)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  # \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n\n\n\n

                  plot(dat$IBS0, dat$kinship, xlab=”Proportion of Zero IBS”,<\/p>\n\n\n\n

                      ylab=”Estimated Kinship Coefficient (KING-robust)”)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \uff08<\/strong>4<\/strong>\uff09<\/strong>\u72b6\u6001\u540c\u6e90<\/strong>\uff08IBS\uff09\u5206\u6790<\/strong><\/p>\n\n\n\n

                  IBS\u5206\u6790\u662f\u4e00\u79cd\u8861\u91cf\u4e2a\u4f53\u95f4\u57fa\u56e0\u578b\u76f8\u4f3c\u5ea6\u7684\u65b9\u6cd5\uff0c\u5b83\u4e0d\u8003\u8651\u9057\u4f20\u5dee\u5f02\u7684\u6765\u6e90\uff0c\u53ea\u5173\u6ce8\u57fa\u56e0\u578b\u662f\u5426\u76f8\u540c\u3002\u5bf9\u4e8en\u4e2a\u7814\u7a76\u4e2a\u4f53\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528snpgdsIBS()\u51fd\u6570\u6765\u521b\u5efa\u4e00\u4e2an\u00d7n\u7684\u77e9\u9635\uff0c\u5176\u4e2d\u5305\u542b\u4e86\u5168\u57fa\u56e0\u7ec4\u5e73\u5747IBS\u914d\u5bf9\u8eab\u4efd\u3002<\/p>\n\n\n\n

                  # \u8fdb\u884cIBS\u5206\u6790<\/p>\n\n\n\n

                  ibs <- snpgdsIBS(genofile, num.thread=2)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u8fd9\u6bb5\u4ee3\u7801\u4f1a\u6392\u9664\u975e\u5e38\u67d3\u8272\u4f53\u4e0a\u7684SNP\uff0c\u5e76\u8ba1\u7b97\u6240\u6709\u9009\u5b9a\u57fa\u56e0\u578b\uff080,1,2\uff09\u7684\u603b\u548c\u3002\u5206\u6790\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u70ed\u56fe\u6765\u76f4\u89c2\u5730\u5c55\u793a\u7ed3\u679c\uff1a<\/p>\n\n\n\n

                  # \u5c06\u540c\u4e00\u4eba\u7fa4\u4e2d\u7684\u4e2a\u4f53\u805a\u96c6\u5728\u4e00\u8d77<\/p>\n\n\n\n

                  pop.idx <- order(pop_code)<\/p>\n\n\n\n

                  # \u5c55\u793a\u70ed\u56fe<\/p>\n\n\n\n

                  image(ibs$ibs[pop.idx, pop.idx], col=terrain.colors(16))<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u591a\u7ef4\u5c3a\u5ea6\u5206\u6790\uff08MDS\uff09\u6765\u5206\u6790\u5168\u57fa\u56e0\u7ec4IBS\u914d\u5bf9\u8ddd\u79bb\u7684n\u00d7n\u77e9\u9635\uff1a<\/p>\n\n\n\n

                  # \u8fdb\u884cMDS\u5206\u6790<\/p>\n\n\n\n

                  loc <- cmdscale(1 – ibs$ibs, k = 2)<\/p>\n\n\n\n

                  x <- loc[, 1]; y <- loc[, 2]<\/p>\n\n\n\n

                  race <- as.factor(pop_code)<\/p>\n\n\n\n

                  # \u7ed8\u5236MDS\u56fe<\/p>\n\n\n\n

                  plot(x, y, col=race, xlab = “”, ylab = “”,<\/p>\n\n\n\n

                      main = “Multidimensional Scaling Analysis (IBS)”)<\/p>\n\n\n\n

                  legend(“topleft”, legend=levels(race), pch=”o”, text.col=1:nlevels(race))<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u8fdb\u884c\u805a\u7c7b\u5206\u6790\uff0c\u4ee5\u786e\u5b9a\u4e2a\u4f53\u7684\u7fa4\u4f53\uff0c\u5e76\u901a\u8fc7\u7f6e\u6362\u5206\u6570\u81ea\u52a8\u786e\u5b9a\u7fa4\u4f53\uff1a<\/p>\n\n\n\n

                  # \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/p>\n\n\n\n

                  set.seed(100)<\/p>\n\n\n\n

                  # \u8fdb\u884c\u805a\u7c7b\u5206\u6790<\/p>\n\n\n\n

                  ibs.hc <- snpgdsHCluster(snpgdsIBS(genofile, num.thread=2))<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u6811\u72b6\u56fe\u6765\u786e\u5b9a\u4e2a\u4f53\u7684\u7fa4\u4f53\uff1a<\/p>\n\n\n\n

                  # \u81ea\u52a8\u786e\u5b9a\u4e2a\u4f53\u7684\u7fa4\u4f53<\/p>\n\n\n\n

                  rv <- snpgdsCutTree(ibs.hc)<\/p>\n\n\n\n

                  # \u7ed8\u5236\u6811\u72b6\u56fe<\/p>\n\n\n\n

                  plot(rv$dendrogram, leaflab=”none”, main=”HapMap Phase II”)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u6211\u4eec\u8fd8\u53ef\u4ee5\u57fa\u4e8e\u5df2\u77e5\u7684\u4eba\u53e3\u4fe1\u606f\u6765\u786e\u5b9a\u4e2a\u4f53\u7684\u7fa4\u4f53\uff1a<\/p>\n\n\n\n

                  # \u6839\u636e\u4eba\u53e3\u4fe1\u606f\u786e\u5b9a\u4e2a\u4f53\u7684\u7fa4\u4f53<\/p>\n\n\n\n

                  rv2 <- snpgdsCutTree(ibs.hc, samp.group=as.factor(pop_code))<\/p>\n\n\n\n

                  # \u7ed8\u5236\u6811\u72b6\u56fe<\/p>\n\n\n\n

                  plot(rv2$dendrogram, leaflab=”none”, main=”HapMap Phase II”)<\/p>\n\n\n\n

                  legend(“topright”, legend=levels(race), col=1:nlevels(race), pch=19, ncol=4)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n

                  \u6700\u540e\uff0c\u522b\u5fd8\u4e86\u5173\u95edGDS\u6587\u4ef6\uff1a<\/p>\n\n\n\n

                  snpgdsClose(genofile)<\/p>\n\n\n\n

                  \u901a\u8fc7\u8fd9\u4e9b\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u4e2a\u4f53\u4e4b\u95f4\u7684\u9057\u4f20\u5173\u7cfb\uff0c\u8fd9\u5bf9\u4e8e\u9057\u4f20\u5b66\u7814\u7a76\u548c\u4eba\u7fa4\u9057\u4f20\u7ed3\u6784\u5206\u6790\u90fd\u662f\u975e\u5e38\u6709\u5e2e\u52a9\u7684\u3002\u5982\u679c\u4f60\u5bf9\u8fd9\u4e2a\u8fc7\u7a0b\u8fd8\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u6216\u8005\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u5e2e\u52a9\uff0c\u968f\u65f6\u53ef\u4ee5\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6\uff01<\/p>\n\n\n\n

                  \u65e0\u8bba\u4f60\u662f\u5728\u4f18\u5316\u4ee3\u7801\uff0c\u8fd8\u662f\u5728\u4e91\u7aef\u8fdb\u884c\u4fbf\u6377\u7684\u5206\u6790\uff0c\u4e91\u751f\u4fe1\u795e\u5668\u90fd\u80fd\u4e3a\u4f60\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\u3002\u6b22\u8fce\u8bd5\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u795e\u5668\uff0c\u53ea\u9700\u4e00\u952e\u4e0a\u4f20\u6570\u636e\uff0c\u60f3\u8981\u7684\u56fe\u5c31\u80fd\u8f7b\u677eget~<\/strong><\/strong><\/p>\n\n\n\n

                  \u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1a<\/strong>http:\/\/www.biocloudservice.com\/home.html<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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