Showing papers by "Chris Haley published in 2014"

Detecting epistasis in human complex traits

Abstract: Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

A genome-wide linkage analysis for reproductive traits in F2 Large White × Meishan cross gilts

Abstract: Female reproductive performance traits in pigs have low heritabilities thus limiting improvement through traditional selective breeding programmes. However, there is substantial genetic variation found between pig breeds with the Chinese Meishan being one of the most prolific pig breeds known. In this study, three cohorts of Large White × Meishan F2 cross-bred pigs were analysed to identify quantitative trait loci (QTL) with effects on reproductive traits, including ovulation rate, teat number, litter size, total born alive and prenatal survival. A total of 307 individuals were genotyped for 174 genetic markers across the genome. The genome-wide analysis of the trait-recorded F2 gilts in their first parity/litter revealed one QTL for teat number significant at the genome level and a total of 12 QTL, which are significant at the chromosome-wide level, for: litter size (three QTL), total born alive (two QTL), ovulation rate (four QTL), prenatal survival (one QTL) and teat number (two QTL). Further support for eight of these QTL is provided by results from other studies. Four of these 12 QTL were mapped for the first time in this study: on SSC15 for ovulation rate and on SSC18 for teat number, ovulation rate and litter size.

Abundant local interactions in the 4p16.1 region suggest functional mechanisms underlying SLC2A9 associations with human serum uric acid

Abstract: Human serum uric acid concentration (SUA) is a complex trait. A recent meta-analysis of multiple genome-wide association studies (GWAS) identified 28 loci associated with SUA jointly explaining only 7.7% of the SUA variance, with 3.4% explained by two major loci (SLC2A9 and ABCG2). Here we examined whether gene-gene interactions had any roles in regulating SUA using two large GWAS cohorts included in the meta-analysis [the Atherosclerosis Risk in Communities study cohort (ARIC) and the Framingham Heart Study cohort (FHS)]. We found abundant genome-wide significant local interactions in ARIC in the 4p16.1 region located mostly in an intergenic area near SLC2A9 that were not driven by linkage disequilibrium and were replicated in FHS. Taking the forward selection approach, we constructed a model of five SNPs with marginal effects and three epistatic SNP pairs in ARIC-three marginal SNPs were located within SLC2A9 and the remaining SNPs were all located in the nearby intergenic area. The full model explained 1.5% more SUA variance than that explained by the lead SNP alone, but only 0.3% was contributed by the marginal and epistatic effects of the SNPs in the intergenic area. Functional analysis revealed strong evidence that the epistatically interacting SNPs in the intergenic area were unusually enriched at enhancers active in ENCODE hepatic (HepG2, P = 4.7E-05) and precursor red blood (K562, P = 5.0E-06) cells, putatively regulating transcription of WDR1 and SLC2A9. These results suggest that exploring epistatic interactions is valuable in uncovering the complex functional mechanisms underlying the 4p16.1 region.

Analysis of the genetics of boar taint reveals both single SNPs and regional effects.

Abstract: Boar taint is an offensive urine or faecal-like odour, affecting the smell and taste of cooked pork from some mature non-castrated male pigs. Androstenone and skatole in fat are the molecules responsible. In most pig production systems, males, which are not required for breeding, are castrated shortly after birth to reduce the risk of boar taint. There is evidence for genetic variation in the predisposition to boar taint. A genome-wide association study (GWAS) was performed to identify loci with effects on boar taint. Five hundred Danish Landrace boars with high levels of skatole in fat (>0.3 μg/g), were each matched with a litter mate with low levels of skatole and measured for androstenone. DNA from these 1,000 non-castrated boars was genotyped using the Illumina PorcineSNP60 Beadchip. After quality control, tests for SNPs associated with boar taint were performed on 938 phenotyped individuals and 44,648 SNPs. Empirical significance thresholds were set by permutation (100,000). For androstenone, a ‘regional heritability approach’ combining information from multiple SNPs was used to estimate the genetic variation attributable to individual autosomes. A highly significant association was found between variation in skatole levels and SNPs within the CYP2E1 gene on chromosome 14 (SSC14), which encodes an enzyme involved in degradation of skatole. Nominal significance was found for effects on skatole associated with 4 other SNPs including a region of SSC6 reported previously. Genome-wide significance was found for an association between SNPs on SSC5 and androstenone levels and nominal significance for associations with SNPs on SSC13 and SSC17. The regional analyses confirmed large effects on SSC5 for androstenone and suggest that SSC5 explains 23% of the genetic variation in androstenone. The autosomal heritability analyses also suggest that there is a large effect associated with androstenone on SSC2, not detected using GWAS. Significant SNP associations were found for skatole on SSC14 and for androstenone on SSC5 in Landrace pigs. The study agrees with evidence that the CYP2E1 gene has effects on skatole breakdown in the liver. Autosomal heritability estimates can uncover clusters of smaller genetic effects that individually do not exceed the threshold for GWAS significance.

Using Haplotype Mapping to Uncover the Missing Heritability: A Simulation Study

Abstract: An improved regional mapping method is proposed based on haplotype information with haplotype blocks being used as analysis regions. Genomic data were simulated using circa 300K SNPs. The simulated phenotypes’ heritability was 0.30 from which 0.05 was regional heritability. Twenty different regions of genome were selected to be trait associated in the simulation. Four scenarios were used to generate regional variance with either one SNP, all SNPs, one haplotype or all haplotypes from the region being the causal variants. Regional genomic relationship matrices constructed with SNP-based or haplotype-based methods were used in a REML framework to estimate the variance explained by the region. Our results show that the proposed haplotype-based method always captures the effect of the regions, albeit with decreased performance with increasing block size in SNP-based variants. SNP-based methods often do not detect the effect of causal haplotype(s).

Genomic prediction of health traits in humans: demonstrating the value of marker selection.

Abstract: In this study, we explored prediction of human height, high-density lipoproteins (HDL) and body mass index (BMI) using SNPs within a Croatian (N=2,186) and into a UK population (N=810) in Bayes-C (using Gibbs sampling) and G-BLUP frameworks. Correlation between predicted and observed trait values in 10-fold cross-validation was used to assess prediction accuracy. Using all available 263,357 SNPs, Bayes-C and G-BLUP had similar prediction accuracy across traits within the Croatian data, and for height and BMI when predicting into the UK population. However, Bayes-C outperformed G-BLUP in the prediction of less polygenic HDL into the UK population. Supervised feature selection allowed G-BLUP to achieve equivalent predictive performance to Bayes-C across all three traits with greatly reduced computational effort. Feature selection in the GBLUP framework therefore provides a flexible and efficient alternative to computationally expensive BayesC for traits considered in this study.