Computation of ancestry scores with mixed families and unrelated individuals

Computation of ancestry scores with mixed families and unrelated individuals IntroductionDiffering ancestries of human subpopulations create systematic differences in genetic allele frequencies across the genome, a phenomenon known as population stratification or substructure. If a phenotypic trait such as disease is associated with subpopulation membership, a genetic association study can identify spurious relationships with genetic markers. Singular value decomposition (SVD) of genotype data or eigen decomposition of covariance matrices can be used to identify population stratification. The eigenvectors (essentially principal component scores) that correspond to large eigenvalues can be used as covariates in association analysis (Levine et al., ). The combined analysis of unrelated and related individuals is a common feature of genetic association studies (Zhu et al., ). However, the presence of close‐degree relatives in a genetic dataset presents difficulties, as the family structure can greatly influence the eigenvalues and eigenvectors.Cystic fibrosis (CF) is a recessive genetic lung disorder, caused by a mutation in the single gene CFTR. However, considerable genetic variation remains in the severity of disease, and evidence indicates this variation is complex and influenced by numerous genes (Wright et al., ). Genotypes gathered by the North American CF Consortium are typical of a large‐scale genomewide association study (GWAS), with thousands of individuals and over 1 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Computation of ancestry scores with mixed families and unrelated individuals

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12708
Publisher site
See Article on Publisher Site

Abstract

IntroductionDiffering ancestries of human subpopulations create systematic differences in genetic allele frequencies across the genome, a phenomenon known as population stratification or substructure. If a phenotypic trait such as disease is associated with subpopulation membership, a genetic association study can identify spurious relationships with genetic markers. Singular value decomposition (SVD) of genotype data or eigen decomposition of covariance matrices can be used to identify population stratification. The eigenvectors (essentially principal component scores) that correspond to large eigenvalues can be used as covariates in association analysis (Levine et al., ). The combined analysis of unrelated and related individuals is a common feature of genetic association studies (Zhu et al., ). However, the presence of close‐degree relatives in a genetic dataset presents difficulties, as the family structure can greatly influence the eigenvalues and eigenvectors.Cystic fibrosis (CF) is a recessive genetic lung disorder, caused by a mutation in the single gene CFTR. However, considerable genetic variation remains in the severity of disease, and evidence indicates this variation is complex and influenced by numerous genes (Wright et al., ). Genotypes gathered by the North American CF Consortium are typical of a large‐scale genomewide association study (GWAS), with thousands of individuals and over 1

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ;

References

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