Accounting for heteroscedasticity and censoring in chromosome partitioning analyses

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Pysyväisosoite

http://hdl.handle.net/10138/299699

Lähdeviite

Kemppainen , P & Husby , A 2018 , ' Accounting for heteroscedasticity and censoring in chromosome partitioning analyses ' , Evolution Letters , vol. 2 , no. 6 , pp. 599-609 . https://doi.org/10.1002/evl3.88

Julkaisun nimi: Accounting for heteroscedasticity and censoring in chromosome partitioning analyses
Tekijä: Kemppainen, Petri; Husby, Arild
Tekijän organisaatio: External Funding
Ecological Genetics Research Unit
Päiväys: 2018-12
Kieli: eng
Sivumäärä: 11
Kuuluu julkaisusarjaan: Evolution Letters
ISSN: 2056-3744
DOI-tunniste: https://doi.org/10.1002/evl3.88
URI: http://hdl.handle.net/10138/299699
Tiivistelmä: 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 (h(c)(2)), is regressed on its size. However, as h(c)(2)-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 published data across many different species we estimate that not accounting for this effect overall resulted in 28% false positives. We introduce a new and computationally efficient resampling method that corrects for inflation caused by heteroscedasticity and censoring and that works under a large range of dataset sizes and genetic architectures in empirical datasets. Our new method substantially improves the robustness of inferences from chromosome partitioning analyses.
Avainsanat: Chromosome partitioning
heritability
infinitesimal model
genomic relatedness
GCTA
SNP heritability
GENOME-WIDE ASSOCIATION
GENETIC ARCHITECTURE
MISSING HERITABILITY
QUANTITATIVE TRAITS
COMMON SNPS
CHALLENGES
SCHIZOPHRENIA
PROPORTION
DISSECTION
SIZE
219 Environmental biotechnology
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: cc_by
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: publishedVersion


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