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Recently a paper authored by ourselves and a number of co-authors about the proportion of phenotypic variation in height that is explained by common SNPs was published in Nature Genetics (Yang et al., 2010). Common SNPs explain a large proportion of the heritability for human height (Yang et al.). During the refereeing process (the paper was rejected by two other journals before publication in Nature Genetics) and following the publication of Yang et al. (2010) it became clear to us that the methodology we applied, the interpretation of the results and the consequences of the findings on the genetic architecture of human height and that for other traits such as complex disease are not well understood or appreciated. Here we explain some of these issues in a style that is different from the primary publication, that is, in the form of a number of comments and questions and answers. We also report a number of additional results that show that the estimates of additive genetic variation are not driven by population structure. [ABSTRACT FROM AUTHOR]

In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship ,0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent "related," and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions. [ABSTRACT FROM AUTHOR]