Association weight matrix for the genetic dissection of puberty in beef cattle

Abstract

We describe a systems biology approach for the genetic dissection of complex traits based on applying gene network theory to the results from genome-wide associations. The associations of singlenucleotide polymorphisms (SNP) that were individually associated with a primary phenotype of interest, age at puberty in our study, were explored across 22 related traits. Genomic regions were surveyed for genes harboring the selected SNP. As a result, an association weight matrix (AWM) was constructed with as many rows as genes and as many columns as traits. Each {i, j} cell value in the AWM corresponds to the z-score normalized additive effect of the ith gene (via its neighboring SNP) on the jth trait. Columnwise, the AWM recovered the genetic correlations estimated via pedigree-based restricted maximum-likelihood methods. Rowwise, a combination of hierarchical clustering, gene network, and pathway analyses identified genetic drivers that would have been missed by standard genome-wide association studies. Finally, the promoter regions of the AWM-predicted targets of three key transcription factors (TFs), estrogen-related receptor γ (ESRRG), Pal3 motif, bound by a PPAR-γ homodimer, IR3 sites (PPARG), and Prophet of Pit 1, PROP paired-like homeobox 1 (PROP1), were surveyed to identify binding sites corresponding to those TFs. Applied to our case, the AWM results recapitulate the known biology of puberty, captured experimentally validated binding sites, and identified candidate genes and genegene interactions for further investigation.bovine | complex traits | fertility | reproduction | systems biology T he analysis of genome-wide association studies (GWASs) applied to complex traits remains a challenge (1). Addressing a complex trait by a single, often binary, phenotypic measure is common practice but is limiting. It is not easy to find the right balance between applying a conservative significance threshold that gives rise to a small number of strong and hopefully biologically meaningful associations and applying a relaxed threshold yielding numerous associations, many of which are new but potentially false. In addition, an increase in sample size coupled with a denser chip results in a larger number of associations that, on average, have a much smaller effect (2). Accepting a large number of associations while simultaneously reducing the number of false positives would be ideal. It is reasonable to propose that a holistic approach applied to a relaxed significance threshold could be the solution. Such a strategy would be particularly useful when investigating the genetic basis of complex traits that, by definition, are influenced by numerous genes and pathways.In our study, age at puberty was the complex phenotype considered. Puberty, or the progression to sexual maturity, is a developmental process with genetic drivers conserved among species (3). It is an important phenotype for the beef industry because late puberty has negative effects on reproduction rates and profitability (4). Age at puberty is moderately heritab...

BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection1–3

“…A network with 3,159 genes related to heifer puberty was constructed from an association weight matrix (AWM) describing gene-phenotype associations for age at fi rst corpus luteum and 21 other measures related to growth, body composition, and fertility. This analysis revealed puberty-related genes that would have been missed by single-trait GWAS and predicted gene-gene interactions consistent with experimentally validated transcription factor-target relationships (Fortes et al, 2010). Overrepresentation analysis of the AWM genes also revealed biological processes relevant to puberty that were not implicated by single-trait GWAS and gene set analysis of age at fi rst corpus luteum.…”

“…Assignment based on distance between SNP and annotated genes positions is simple, but there is no standard for SNP-gene separation. Using the dense BovineSNP50, Fortes et al (2010) considered genes within 2.5 kbp of a SNP whereas Rolf et al (2011) assigned genes to SNP within 500 kbp. Alternatives may be to assign genes to the closest SNP, with a limit on the maximum separation between SNP and genes, and LD-based assignment to genes overlapped by haplotype blocks.…”

Global differential gene expression in the pituitary gland and the ovaries of pre- and postpubertal Brahman heifers

“…The TF PPARG was previously identified as a key regulator of cattle puberty, in a network prediction from genomewide association data (Fortes et al, 2010). It is feasible to speculate that PPAR signaling is important for puberty, because of its relevance to fat metabolism and the well-known interplay between nutrition, metabolism, and puberty in cattle (Cardoso et al, 2014(Cardoso et al, , 2015.…”

PHYSIOLOGY AND ENDOCRINOLOGY SYMPOSIUM: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females1,2,3,4

“…Information from multiple sources can also be leveraged by using gene set and network analysis to integrate SNP identified by GWAS with gene expression, functional annotation, regulatory pathways, and other evidence to develop panels likely to contain biologically relevant SNP (Medina et al, 2009;Zhong et al, 2010;Wang et al, 2011). Fortes et al (2010) describe a systems biology approach to constructing an association weight matrix (AWM) from GWAS of several traits, with support from pathway and transcription factor networks to develop gene networks associated with complex traits. The AWM approach was applied to GWAS of age of observation of the first corpus luteum and 21 additional measures of heifer puberty, BW, growth, and body composition taken on separate populations of Bos indicus and Bos taurus × B. indicus composite females.…”

“…Following methodology described by Fortes et al (2010), minimally associated SNP (P < 0.05) and their effects estimated for the 10 traits provided the basis for an AWM and underlying gene network related to first service conception ). An initial network of 1,555 genes indicated by univariate GWAS was filtered for genes expressed in the hypothalamus of pre-and postpubertal half-sib heifers, resulting in a network of 1,096 genes supported by GWAS and hypothalamic expression.…”