Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model.

Abstract:

:Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs). This study investigated the statistical power for identifying nucleotide variants associated with quantitative traits using the mixed model methodology. Quantitative traits were simulated through design of heritability, the number of causal variants (NCV), the number of polygenic variants, and genetic variance ratio of causal to polygenic variants (VRCTP). Statistical power estimates were influenced not only by individual factors of heritability, NCV, and VRCTP, but also by their interactions (P < 0.05). As the genetic variance ratio decreased, the difference in power between heritabilities of 0.3 and 0.5 increased with the use of 20 causal variants, but decreased when there were 100 causal variants (P < 0.05). The power empirically estimated from the simulation study would be applicable to the design of GWAS for quantitative traits with known genetic parameters by predicting the degree of false negative associations.

journal_name

Genomics

journal_title

Genomics

authors

Shin J,Lee C

doi

10.1016/j.ygeno.2014.11.001

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

1-4

issue

1

eissn

0888-7543

issn

1089-8646

pii

S0888-7543(14)00215-8

journal_volume

105

pub_type

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