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Abstract

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributableto additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally,this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationshipsbetween family members. An alternative, when dense genotype data are available, is to directly measure theempirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levelspre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential OligogenicLinkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK)were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted usingeach kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation.The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall,the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generatedusing IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated usingIBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknownrelatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was notexpected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic dataare available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.

Item Type:	Article
Authors/Creators:	Blackburn, NB and Porto, A and Peralta, JM and Blangero, J
Keywords:	kinship, lipids, genetics
Journal or Publication Title:	BMC Proceedings
Publisher:	BioMed Central
ISSN:	1753-6561
DOI / ID Number:	10.1186/s12919-018-0133-x
Copyright Information:	© The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/) which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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