Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples

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Genome-wide association studies (GWAS) have identified thousands of genetic variants for hundreds of traits and diseases. However, the genetic variants discovered from GWAS only explained a small fraction of the heritability, resulting in the question of “missing heritability”. We have recently developed approaches (called GREML) to estimate the overall contribution of all SNPs to the phenotypic variance of a trait (disease) and the proportion of genetic overlap between traits (diseases). A frequently asked question is that how many samples are required to estimate the proportion of variance attributable to all SNPs and the proportion of genetic overlap with useful precision. In this study, we derive the standard errors of the estimated parameters from theory and find that they are highly consistent with those observed values from published results and those obtained from simulation. The theory together with an online application tool will be helpful to plan experimental design to quantify the missing heritability, and to estimate the genetic overlap between traits (diseases) especially when it is unfeasible to have the traits (diseases) measured on the same individuals.

Vyšlo v časopise: Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples. PLoS Genet 10(4): e32767. doi:10.1371/journal.pgen.1004269
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004269

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