2012Collection of Biostatistics Research ArchiveOpen access

Why odds ratio estimates of GWAS are almost always close to 1.0

Yutaka Yasui

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Abstract

“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, and deduces two specific forms of epistasis/interaction as its cause. The implication is that GWAS may be able to recover “missing heritability” if the two specific forms of epistasis and gene-environmental interaction are fully explored.

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“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, and deduces two specific forms of epistasis/interaction as its cause. The implication is that GWAS may be able to recover “missing heritability” if the two specific forms of epistasis and gene-environmental interaction are fully explored.

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Available abstract

“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, and deduces two specific forms of epistasis/interaction as its cause. The implication is that GWAS may be able to recover “missing heritability” if the two specific forms of epistasis and gene-environmental interaction are fully explored.

Key concepts: Genome-wide association study, Missing heritability problem, Heritability, Genetic association, Epistasis, Odds ratio, Odds, Association (psychology)

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