Quantifying Missing Heritability at Known GWAS Loci
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we...
| Main Authors: | , , , , , , , , , , , , |
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| 格式: | 文件 |
| 语言: | en_US |
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Public Library of Science
2014
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| 在线阅读: | http://hdl.handle.net/1721.1/86014 |
| _version_ | 1826208138742726656 |
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| author | Gusev, Alexander Bhatia, Gaurav Zaitlen, Noah Vilhjalmsson, Bjarni J. Diogo, Dorothee Stahl, Eli A. Gregersen, Peter K. Worthington, Jane Klareskog, Lars Raychaudhuri, Soumya Plenge, Robert M. Pasaniuc, Bogdan Price, Alkes L. |
| author2 | Whitaker College of Health Sciences and Technology |
| author_facet | Whitaker College of Health Sciences and Technology Gusev, Alexander Bhatia, Gaurav Zaitlen, Noah Vilhjalmsson, Bjarni J. Diogo, Dorothee Stahl, Eli A. Gregersen, Peter K. Worthington, Jane Klareskog, Lars Raychaudhuri, Soumya Plenge, Robert M. Pasaniuc, Bogdan Price, Alkes L. |
| author_sort | Gusev, Alexander |
| collection | MIT |
| description | Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P < 1.0 X 10[superscript -16]) and more CD heritability than known CD SNPs (P = 6.1 X 10[superscript -9]), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture. |
| first_indexed | 2024-09-23T14:01:06Z |
| format | Article |
| id | mit-1721.1/86014 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:01:06Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/860142022-10-01T18:35:11Z Quantifying Missing Heritability at Known GWAS Loci Gusev, Alexander Bhatia, Gaurav Zaitlen, Noah Vilhjalmsson, Bjarni J. Diogo, Dorothee Stahl, Eli A. Gregersen, Peter K. Worthington, Jane Klareskog, Lars Raychaudhuri, Soumya Plenge, Robert M. Pasaniuc, Bogdan Price, Alkes L. Whitaker College of Health Sciences and Technology Bhatia, Gaurav Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P < 1.0 X 10[superscript -16]) and more CD heritability than known CD SNPs (P = 6.1 X 10[superscript -9]), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture. National Institutes of Health (U.S.) (Grant R03HG006731) National Institutes of Health (U.S.) (Fellowship F32GM106584) 2014-04-04T13:45:14Z 2014-04-04T13:45:14Z 2013-12 2013-04 Article http://purl.org/eprint/type/JournalArticle 1553-7404 http://hdl.handle.net/1721.1/86014 Gusev, Alexander, Gaurav Bhatia, Noah Zaitlen, Bjarni J. Vilhjalmsson, Dorothee Diogo, Eli A. Stahl, Peter K. Gregersen, et al. “Quantifying Missing Heritability at Known GWAS Loci.” Edited by Peter M. Visscher. PLoS Genet 9, no. 12 (December 26, 2013): e1003993. en_US http://dx.doi.org/10.1371/journal.pgen.1003993 PLoS Genetics Publisher with Creative Commons License http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLoS |
| spellingShingle | Gusev, Alexander Bhatia, Gaurav Zaitlen, Noah Vilhjalmsson, Bjarni J. Diogo, Dorothee Stahl, Eli A. Gregersen, Peter K. Worthington, Jane Klareskog, Lars Raychaudhuri, Soumya Plenge, Robert M. Pasaniuc, Bogdan Price, Alkes L. Quantifying Missing Heritability at Known GWAS Loci |
| title | Quantifying Missing Heritability at Known GWAS Loci |
| title_full | Quantifying Missing Heritability at Known GWAS Loci |
| title_fullStr | Quantifying Missing Heritability at Known GWAS Loci |
| title_full_unstemmed | Quantifying Missing Heritability at Known GWAS Loci |
| title_short | Quantifying Missing Heritability at Known GWAS Loci |
| title_sort | quantifying missing heritability at known gwas loci |
| url | http://hdl.handle.net/1721.1/86014 |
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