Leveraging genetically simple traits to identify small-effect variants for complex phenotypes

Abstract

BackgroundPolymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ2 P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression).ResultsQuantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ2 P for milk yield, and 0.11 σ2 P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy.ConclusionsMilk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3175-3) contains supplementary material, which is available to authorized users.

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

“…Our analysis also highlights the importance of intermediate trait QTL, including QTLs for metabolic traits and gene expression (mQTLs, geQTLs, eeQTLs, sQTLs and aseQTLs). This is not a surprising result as the significant contribution of different intermediate trait QTLs to complex trait variations have been reported in humans (7,26,(39)(40)(41) and cattle (13,(42)(43)(44). To our knowledge, no study has systematically compared the genetic importance of mQTLs with eQTLs.…”

Multiple QTL underlie milk phenotypes at the CSF2RB locus

“…Several genes with substantial impacts on milk yield are known, including DGAT1 [5], ABCG2 [6], GHR [7], SLC37A1 [8], and MGST1 [9]. Recently, as part of work presented elsewhere [10], we performed a milk volume genome-wide association analysis in 4,982 mixed breed cattle using a BayesB model [11,12], using a panel of 3,695 variants selected as tag-SNPs representing expression QTL (eQTL) from lactating mammary tissue.…”

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

“…An example follows. Kemper et al [25] found that a QTL that explained 0.001 of the genetic variance for milk yield co-segregated with a QTL that explained 0.1 of the genetic variance for phosphorus concentration in milk. The SNPs with a large effect on both traits mapped near a gene for a phosphorus anti-porter that transports glucose-6-phosphate in one direction and phosphorus in the other direction across cell membranes.…”