2016
DOI: 10.1038/ncomms13299
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A multi-marker association method for genome-wide association studies without the need for population structure correction

Abstract: All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes… Show more

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Cited by 41 publications
(58 citation statements)
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“…bslmm jointly model all markers and are well suited to GWA analyses on quantitative traits, as they provide estimates of the proportion of phenotypic variance explained by large‐effect (PGE) vs. polygenic markers (PVE) allowing inference of their relative contributions. At last, a multimarker method, quantitative trait cluster association testing ( qtcat , Klasen et al., ), was implemented. qtcat may increase power in GWAS on polygenic traits by searching for clusters of markers significantly associated with a given trait, mitigating the need to correct for population structure and genetic background by accounting for correlation between markers whilst simultaneously associating them with the phenotype.…”
Section: Methodsmentioning
confidence: 99%
“…bslmm jointly model all markers and are well suited to GWA analyses on quantitative traits, as they provide estimates of the proportion of phenotypic variance explained by large‐effect (PGE) vs. polygenic markers (PVE) allowing inference of their relative contributions. At last, a multimarker method, quantitative trait cluster association testing ( qtcat , Klasen et al., ), was implemented. qtcat may increase power in GWAS on polygenic traits by searching for clusters of markers significantly associated with a given trait, mitigating the need to correct for population structure and genetic background by accounting for correlation between markers whilst simultaneously associating them with the phenotype.…”
Section: Methodsmentioning
confidence: 99%
“…The Quantitative Trait Cluster Association Test (QTCAT) also has the potential to circumvent false positives due to demographic history. The QTCAT enables simultaneous multi‐marker associations while considering correlations between markers, thus avoiding the need to correct for population structure (Klasen et al ., ). This approach could prove useful given how little we know about the extent to which the microbiome is correlated with population structure.…”
Section: Workflowmentioning
confidence: 97%
“…Population structure is another issue that could be affecting the current results. Controlling for population structure is a standard procedure in GWAS analyses, as we did by using the Q+K model; however, it reduces the statistical power to detect associations when phenotypes strongly correlate with relatedness (Reif et al, 2010;Brachi et al, 2011;Würschum et al, 2012;Ogut et al, 2015;Han et al, 2016;Klasen et al, 2016).…”
Section: Current Challenges and Perspectives Of Gwas In The Blueberrymentioning
confidence: 99%