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Risk of false positive genetic associations in complex traits with underlying population structure: a case study.

ABSTRACT: Genome-wide association (GWA) studies are widely used to investigate the genetic etiology of diseases in domestic animals. In the horse, GWA studies using 40-50,000 single nucleotide polymorphisms (SNPs) in sample sizes of 30-40 individuals, consisting of only 6-14 affected horses, have led to the discovery of genetic mutations for simple monogenic traits. Equine neuroaxonal dystrophy is a common inherited neurological disorder characterized by symmetric ataxia. A case-control GWA study was performed using genotypes from 42,819 SNP marker loci distributed across the genome in 99 clinically phenotyped Quarter horses (37 affected, 62 unaffected). A significant GWA was not achieved although a suggestive association was uncovered when only the most stringently phenotyped NAD-affected horses (n?=?10) were included (chromosome 8:62130605 and 62134644 [log(1/P)?=?5.56]). Candidate genes (PIK3C3, RIT2, and SYT4) within the associated region were excluded through sequencing, association testing of uncovered variants and quantitative RT-PCR. It was concluded that variants in PIK3C3, RIT2, and SYT4 are not responsible for equine neuroaxonal dystrophy. This study demonstrates the risk of false positive associations when performing GWA studies on complex traits and underlying population structure when using 40-50,000 SNP markers and small sample size.

SUBMITTER: Finno CJ

PROVIDER: S-EPMC4337777 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Risk of false positive genetic associations in complex traits with underlying population structure: a case study.

Finno Carrie J CJ Aleman Monica M Higgins Robert J RJ Madigan John E JE Bannasch Danika L DL

Veterinary journal (London, England : 1997) 20140921 3

Genome-wide association (GWA) studies are widely used to investigate the genetic etiology of diseases in domestic animals. In the horse, GWA studies using 40-50,000 single nucleotide polymorphisms (SNPs) in sample sizes of 30-40 individuals, consisting of only 6-14 affected horses, have led to the discovery of genetic mutations for simple monogenic traits. Equine neuroaxonal dystrophy is a common inherited neurological disorder characterized by symmetric ataxia. A case-control GWA study was perf ...[more]

PMID: 25278384

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Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

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Risk of false positive genetic associations in complex traits with underlying population structure: a case study.

Publications

Risk of false positive genetic associations in complex traits with underlying population structure: a case study.

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