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
Genomicsjournal_title
Genomicsauthors
Shin J,Lee Cdoi
10.1016/j.ygeno.2014.11.001subject
Has Abstractpub_date
2015-01-01 00:00:00pages
1-4issue
1eissn
0888-7543issn
1089-8646pii
S0888-7543(14)00215-8journal_volume
105pub_type
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