Accounting for heteroscedasticity and censoring in chromosome partitioning analyses.

ABSTRACT: A fundamental assumption in quantitative genetics is that traits are controlled by many loci of small effect. Using genomic data, this assumption can be tested using chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2 c ), is regressed on its size. However, as h2 c -estimates are necessarily positive (censoring) and the variance increases with chromosome size (heteroscedasticity), two fundamental assumptions of ordinary least squares (OLS) regression are violated. Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture. The degree of bias depends mainly on the number of chromosomes and their size distribution and is therefore specific to the species; using

SUBMITTER: Kemppainen P 

PROVIDER: S-EPMC6292708 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature