Do Molecular Markers Inform About Pleiotropy?

Abstract

The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural.KEYWORDS genetic correlation; genomic correlation; genomic heritability; linkage disequilibrium; pleiotropy T HE interindividual differences for a trait or disease risk that can be explained by genetic factors, such as trait heritability (h 2 ), the genetic correlation (r G ), and the coheritability between two traits (r G h 1 h 2 ), are very important parameters in quantitative genetic studies of animals, humans, and plants. These quantities play a role in the study of evolution due to artificial and natural selection, and knowledge thereof is required for statistical prediction of outcomes in animal and plant breeding as well as medicine. Traditionally, these parameters have been estimated using phenotypes and pedigrees, e.g., family and twin data in human genetics. The availability of dense panels of common single-nucleotide polymorphisms (SNPs) and of sequence data more recently has made it possible to assess kinship among distantly related individuals (Morton et al. 1971;Thompson 1975;Ritland 1996;Lynch and Ritland 1999). This development has opened new opportunities for study of the genetic architecture of complex traits and diseases. For instance, Yang et al. (2010) suggested using whole-genome regressions (WGRs) (Meuwissen et al. 2001) to assess the proportion of variance of a trait or disease risk that can be explained by a regression of phenotypes on common SNPs or genomic heritability and a related parameter, the "missing heritability." More recently, WGR models have been extended for the analysis of systems of multiple traits, so the concept of genomic correlation also has entered into the picture (Jia and Jannink 2012; Lee et al. 2012). For instance, Maier et al. (2015) used multivariate WGR models and reported estimates of genetic correlations between psychiatric disorders, and Furlotte and Eskin (2015) presented a methodology that incorporates genetic marker information for the analysis of multiple traits that, according to the authors, "provide fundamental insights into the nature of co-expressed genes." In a similar spirit, Korte et al. (2012) argued that multitrait-marker-enabled regressions can be useful for understanding pleiotropy....

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

“…It is also important to note that a genetic correlation does not always indicate that a pair of phenotypes has a shared mechanism, as it is possible that SNPs used to compute the correlations may be simultaneously tagging disparate causative genetic variants. 49 Finally, these analyses were limited to subjects of European ancestry, as the numbers of subjects of other ancestries was insufficient for analysis. Further validation of this approach in other ancestral groups is needed.…”

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

“…By evaluating power performance, it is shown that it can be advantageous to perform the proposed pleiotropy analysis instead of individual trait analysis. [1][2][3][4][5][6][7][8][9][10]27,44 Among other merits, the MFLM can handle missing genotype data naturally.…”

“…We assume that the m c variants are located with ordered physical positions 0rt c1 o?ot cmc . To make the notation simpler, we normalized the region ½t c1 ; t cmc to be [0,1]. For the i-th individual in the c-th study, let y cij denote her/his j-th quantitative trait (j = 1,2,⋯,J), G ci ¼ ðx ci ðt c1 Þ; ?…”

“…where B c ðt ck Þ is a vector of the genetic effects at position t ck for the c-th study, and the other terms are the same as those in the general MFLM (1). In model (6) …”

“…[1][2][3][4][5][6][7][8][9][10] To our knowledge, metaanalysis and pleiotropy analysis have been performed separately so far, and there are no gene-based meta-analysis methods for combining multiple studies together and for carrying out a unified pleiotropy analysis. Here, multivariate functional linear models (MFLM) are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates in a meta-analysis context.…”