Literature DB >> 11754467

A study comparing precision of the maximum multipoint heterogeneity LOD statistic to three model-free multipoint linkage methods.

S J Finch1, C H Chen, D Gordon, N R Mendell.   

Abstract

This study compared the performance of the maximum lod (MLOD), maximum heterogeneity lod (MHLOD), maximum non-parametric linkage score (MNPL), maximum Kong and Cox linear extension (MKC(lin)) of NPL, and maximum Kong and Cox exponential extension (MKC(exp)) of NPL as calculated in Genehunter 1.2 and Genehunter-Plus. Our performance measure was the distance between the marker with maximum value for each linkage statistic and the trait locus. We performed a simulation study considering: 1) four modes of transmission, 2) 100 replicates for each model, 3) 58 pedigrees (with 592 subjects) per replicate, 4) three linked marker loci each having three equally frequent alleles, and 5) either 0% unlinked families (linkage homogeneity) or 50% unlinked families (linkage heterogeneity). For each replicate, we obtained the Haldane map position of the location at which each of the five statistics is maximized. The MLOD and MHLOD were obtained by maximizing over penetrances, phenocopy rate, and risk-allele frequencies. For the models simulated, MHLOD appeared to be the best statistic both in terms of identifying a marker locus having the smallest mean distance from the trait locus and in terms of the strongest negative correlation between maximum linkage statistic and distance of the identified position and the trait locus. The marker loci with maximum value of the Kong and Cox extensions of the NPL statistic also were closer to the trait locus than the marker locus with maximum value of the NPL statistic. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11754467     DOI: 10.1002/gepi.1037

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  5 in total

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5.  A Rare Variant Nonparametric Linkage Method for Nuclear and Extended Pedigrees with Application to Late-Onset Alzheimer Disease via WGS Data.

Authors:  Linhai Zhao; Zongxiao He; Di Zhang; Gao T Wang; Alan E Renton; Badri N Vardarajan; Michael Nothnagel; Alison M Goate; Richard Mayeux; Suzanne M Leal
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  5 in total

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