{"action":"opened","issue":{"url":"https://api.github.com/repos/genetics-statistics/GEMMA/issues/153","repository_url":"https://api.github.com/repos/genetics-statistics/GEMMA","labels_url":"https://api.github.com/repos/genetics-statistics/GEMMA/issues/153/labels{/name}","comments_url":"https://api.github.com/repos/genetics-statistics/GEMMA/issues/153/comments","events_url":"https://api.github.com/repos/genetics-statistics/GEMMA/issues/153/events","html_url":"https://github.com/genetics-statistics/GEMMA/issues/153","id":330368675,"node_id":"MDU6SXNzdWUzMzAzNjg2NzU=","number":153,"title":"Explained heritablity by identified significant SNPs associating with phenotype","user":{"login":"wyzhangMPI","id":28892768,"node_id":"MDQ6VXNlcjI4ODkyNzY4","avatar_url":"https://avatars2.githubusercontent.com/u/28892768?v=4","gravatar_id":"","url":"https://api.github.com/users/wyzhangMPI","html_url":"https://github.com/wyzhangMPI","followers_url":"https://api.github.com/users/wyzhangMPI/followers","following_url":"https://api.github.com/users/wyzhangMPI/following{/other_user}","gists_url":"https://api.github.com/users/wyzhangMPI/gists{/gist_id}","starred_url":"https://api.github.com/users/wyzhangMPI/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wyzhangMPI/subscriptions","organizations_url":"https://api.github.com/users/wyzhangMPI/orgs","repos_url":"https://api.github.com/users/wyzhangMPI/repos","events_url":"https://api.github.com/users/wyzhangMPI/events{/privacy}","received_events_url":"https://api.github.com/users/wyzhangMPI/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-07T17:34:36Z","updated_at":"2018-06-07T17:34:36Z","closed_at":null,"author_association":"NONE","body":"Hi, \r\n\r\nWe are currently using GEMMA to estimate the narrow sense heritability (chip heritablity) of a behavior trait in Drosophia melanogaster. The input dataset includes the phenotype data and genetic variants information of around 200 individuals (or inbred lines) from DGRP strains. \r\n\r\nFor the calculation of the heritablity, we used:\r\n\r\n>gemma -bfile dgrp2_high_coverage_height -miss 0.2 -maf 0.05 -gk 1 -o dgrp2_high_coverage.RM\r\n\r\n> gemma -bfile dgrp2_high_coverage_height -k ./output/dgrp2_high_coverage.RM.cXX.txt -miss 0.2 -maf 0.05 -lmm 4 -o dgrp2_high_coverage_lmm.height\r\n\r\nReading Files ...\r\n## number of total individuals = 198\r\n## number of analyzed individuals = 198\r\n## number of covariates = 1\r\n## number of phenotypes = 1\r\n## number of total SNPs = 1903028\r\n## number of analyzed SNPs = 1903028\r\nStart Eigen-Decomposition...\r\npve estimate =0.456917\r\nse(pve) =0.196208\r\n\r\n\r\nThe relatedness matrix \"dgrp2_high_coverage.RM.cXX.tx\" was generated with all the genetic variants in the genome. Based on the above analysis, the chip heritablity should be around 0.46 (0.20 SE).\r\n\r\nThrough genome-wide association analysis, we got around 30 significant SNPs associating with our phenotype. We wanted to test how much the identified significant SNPs can explain the phenotype variance (Heritablity explained by a subset of SNPs). Then we first generated a new relatedness matrix with only this subset of SNPs, and redid the process similarly as before:\r\n\r\n> gemma -bfile dgrp2_high_coverage_height -snps Height_Sig_SNP_Plink -miss 0.2 -maf 0.05 -gk 1 -o dgrp2_high_coverage_Sig_SNP.RM\r\n\r\n> gemma -bfile dgrp2_high_coverage_height -snps Height_Sig_SNP_Plink -k ./output/dgrp2_high_coverage_Sig_SNP.RM.cXX.txt -miss 0.2 -maf 0.05 -lmm 4 -o dgrp2_high_coverage_Sig_SNP_lmm.height\r\n\r\nReading Files ...\r\n## number of total individuals = 198\r\n## number of analyzed individuals = 198\r\n## number of covariates = 1\r\n## number of phenotypes = 1\r\n## number of total SNPs = 1903028\r\n## number of analyzed SNPs = 28\r\nStart Eigen-Decomposition...\r\npve estimate =0.495412\r\nse(pve) =0.0838081\r\n\r\n\r\nIn this way, this subset of SNPs can explain 49.54% of the phenotype variance, even higher than the total chip heritablity. If this is true, meaning the identified significant SNPs can explain all the chip heritablity, we would be assue the rest SNPs (not in the above list) would explain almost nothing. However, with all the rest SNP, we can get:\r\n>gemma -bfile dgrp2_high_coverage_height -snps Other.SNP-miss 0.2 -maf 0.05 -gk 1 -o dgrp2_high_coverage_Other_SNP.RM\r\n\r\n>gemma -bfile dgrp2_high_coverage_height -snps Other.SNP -k ./output/dgrp2_high_coverage_Other_SNP.RM.cXX.txt -miss 0.2 -maf 0.05 -lmm 4 -o dgrp2_high_coverage_Other_SNP_lmm.height\r\n \r\nReading Files ...\r\n## number of total individuals = 198\r\n## number of analyzed individuals = 198\r\n## number of covariates = 1\r\n## number of phenotypes = 1\r\n## number of total SNPs = 1903028\r\n## number of analyzed SNPs = 1903000\r\nStart Eigen-Decomposition...\r\npve estimate =0.4563\r\nse(pve) =0.196231\r\n\r\nAs you can see, the rest SNPs can explain as same as the total chip heritablity. In this way, we would say the above calculation on the explained heritability from significant SNPs was wrong. Could you help to take a look at what is the problem here?\r\n\r\nThanks and have a nice day!\r\n\r\nBest,\r\nWenyu"}}
{"actor":{"display_login":"wyzhangMPI"}}