Measuring missing heritability: Inferring the contribution of common variants
Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. O...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
National Academy of Sciences (U.S.)
2015
|
| Online Access: | http://hdl.handle.net/1721.1/97248 |
| _version_ | 1811074534259818496 |
|---|---|
| author | Golan, David Rosset, Saharon Lander, Eric Steven |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Golan, David Rosset, Saharon Lander, Eric Steven |
| author_sort | Golan, David |
| collection | MIT |
| description | Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the “missing heritability” is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case–control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation–genotype correlation (PCGC) regression, which generalizes the well-known Haseman–Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure. |
| first_indexed | 2024-09-23T09:51:19Z |
| format | Article |
| id | mit-1721.1/97248 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:51:19Z |
| publishDate | 2015 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/972482022-09-26T14:06:03Z Measuring missing heritability: Inferring the contribution of common variants Golan, David Rosset, Saharon Lander, Eric Steven Massachusetts Institute of Technology. Department of Biology Lander, Eric S. Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the “missing heritability” is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case–control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation–genotype correlation (PCGC) regression, which generalizes the well-known Haseman–Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure. National Institutes of Health (U.S.) (NIH HG003067) Broad Institute of MIT and Harvard 2015-06-09T17:38:19Z 2015-06-09T17:38:19Z 2014-11 2014-06 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/97248 Golan, David, Eric S. Lander, and Saharon Rosset. “Measuring Missing Heritability: Inferring the Contribution of Common Variants.” Proceedings of the National Academy of Sciences 111, no. 49 (November 24, 2014): E5272–E5281. en_US http://dx.doi.org/10.1073/pnas.1419064111 Proceedings of the National Academy of Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) National Academy of Sciences (U.S.) |
| spellingShingle | Golan, David Rosset, Saharon Lander, Eric Steven Measuring missing heritability: Inferring the contribution of common variants |
| title | Measuring missing heritability: Inferring the contribution of common variants |
| title_full | Measuring missing heritability: Inferring the contribution of common variants |
| title_fullStr | Measuring missing heritability: Inferring the contribution of common variants |
| title_full_unstemmed | Measuring missing heritability: Inferring the contribution of common variants |
| title_short | Measuring missing heritability: Inferring the contribution of common variants |
| title_sort | measuring missing heritability inferring the contribution of common variants |
| url | http://hdl.handle.net/1721.1/97248 |
| work_keys_str_mv | AT golandavid measuringmissingheritabilityinferringthecontributionofcommonvariants AT rossetsaharon measuringmissingheritabilityinferringthecontributionofcommonvariants AT landerericsteven measuringmissingheritabilityinferringthecontributionofcommonvariants |