Data from: Accounting for heteroscedasticity and censoring in chromosome partitioning analyses

Petri Kemppainen & Arild Husby
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 (h2c), is regressed on its size. However, as h2c-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...
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