Unifying Ideas for Non-Parametric Linkage Analysis | Zendy

Aaron G. DayWilliams | Zendy; John Blangero | Zendy; Thomas D. Dyer | Zendy; Kenneth Lange | Zendy; Eric M. Sobel | Zendy

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Unifying Ideas for Non-Parametric Linkage Analysis

Author(s) -

Aaron G. DayWilliams,

John Blangero,

Thomas D. Dyer,

Kenneth Lange,

Eric M. Sobel

Publication year - 2011

Publication title -

human heredity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.423

H-Index - 62

eISSN - 1423-0062

pISSN - 0001-5652

DOI - 10.1159/000323752

Subject(s) - linkage (software) , genetic linkage , genetics , computational biology , biology , evolutionary biology , computer science , gene

Non-parametric linkage analysis (NPL) exploits marker allele sharing among affected relatives to map genes influencing complex traits. Computational barriers force approximate analysis on large pedigrees and the adoption of a questionable perfect data assumption (PDA) in assigning p values. To improve NPL significance testing on large pedigrees, we examine the adverse consequences of missing data and PDA. We also introduce a novel statistic, Q-NPL, appropriate for NPL analysis of quantitative traits.

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