Measuring missing heritability: Inferring the contribution of common variants

David Golan, Eric S. Lander*, Saharon Rosset

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

187 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)E5272-E5281
JournalProceedings of the National Academy of Sciences of the United States of America
Volume111
Issue number49
DOIs
StatePublished - 9 Dec 2014

Funding

FundersFunder number
National Institutes of HealthHG003067
Wellcome Trust
National Human Genome Research InstituteU54HG003067

    Keywords

    • Genome-wide association studies
    • Heritability estimation
    • Statistical genetics

    Fingerprint

    Dive into the research topics of 'Measuring missing heritability: Inferring the contribution of common variants'. Together they form a unique fingerprint.

    Cite this