Literature DB >> 11935321

Effectiveness of computational methods in haplotype prediction.

Chun-Fang Xu1, Karen Lewis, Kathryn L Cantone, Parveen Khan, Christine Donnelly, Nicola White, Nikki Crocker, Pete R Boyd, Dmitri V Zaykin, Ian J Purvis.   

Abstract

Haplotype analysis has been used for narrowing down the location of disease-susceptibility genes and for investigating many population processes. Computational algorithms have been developed to estimate haplotype frequencies and to predict haplotype phases from genotype data for unrelated individuals. However, the accuracy of such computational methods needs to be evaluated before their applications can be advocated. We have experimentally determined the haplotypes at two loci, the N-acetyltransferase 2 gene ( NAT2, 850 bp, n=81) and a 140-kb region on chromosome X ( n=77), each consisting of five single nucleotide polymorphisms (SNPs). We empirically evaluated and compared the accuracy of the subtraction method, the expectation-maximization (EM) method, and the PHASE method in haplotype frequency estimation and in haplotype phase prediction. Where there was near complete linkage disequilibrium (LD) between SNPs (the NAT2 gene), all three methods provided effective and accurate estimates for haplotype frequencies and individual haplotype phases. For a genomic region in which marked LD was not maintained (the chromosome X locus), the computational methods were adequate in estimating overall haplotype frequencies. However, none of the methods was accurate in predicting individual haplotype phases. The EM and the PHASE methods provided better estimates for overall haplotype frequencies than the subtraction method for both genomic regions.

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Year:  2001        PMID: 11935321     DOI: 10.1007/s00439-001-0656-4

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  18 in total

1.  Estimation of haplotype frequencies, linkage-disequilibrium measures, and combination of haplotype copies in each pool by use of pooled DNA data.

Authors:  Toshikazu Ito; Suenori Chiku; Eisuke Inoue; Makoto Tomita; Takayuki Morisaki; Hiroko Morisaki; Naoyuki Kamatani
Journal:  Am J Hum Genet       Date:  2003-01-17       Impact factor: 11.025

2.  Control of confounding of genetic associations in stratified populations.

Authors:  Clive J Hoggart; Eteban J Parra; Mark D Shriver; Carolina Bonilla; Rick A Kittles; David G Clayton; Paul M McKeigue
Journal:  Am J Hum Genet       Date:  2003-06       Impact factor: 11.025

3.  Haplotype diversity across 100 candidate genes for inflammation, lipid metabolism, and blood pressure regulation in two populations.

Authors:  Dana C Crawford; Christopher S Carlson; Mark J Rieder; Dana P Carrington; Qian Yi; Joshua D Smith; Michael A Eberle; Leonid Kruglyak; Deborah A Nickerson
Journal:  Am J Hum Genet       Date:  2004-03-10       Impact factor: 11.025

4.  Genetic analysis of BDNF and TrkB gene polymorphisms in Alzheimer's disease.

Authors:  Saila Vepsäläinen; Eero Castren; Seppo Helisalmi; Susan Iivonen; Arto Mannermaa; Maarit Lehtovirta; Tuomo Hänninen; Hilkka Soininen; Mikko Hiltunen
Journal:  J Neurol       Date:  2005-02-23       Impact factor: 4.849

5.  A novel strategy for defining haplotypes by selective depletion using restriction enzymes.

Authors:  Anna S Smirnova; Kátia C Ferreira-Silva; Karina L Mine; Vinicius Andrade-Oliveira; Natalia Shulzhenko; Maria Gerbase-DeLima; Andrey Morgun
Journal:  Immunogenetics       Date:  2006-12-05       Impact factor: 2.846

Review 6.  A maximum-likelihood estimation of pairwise relatedness for autopolyploids.

Authors:  K Huang; S T Guo; M R Shattuck; S T Chen; X G Qi; P Zhang; B G Li
Journal:  Heredity (Edinb)       Date:  2014-11-05       Impact factor: 3.821

7.  Modulation of the BP response to diet by genes in the renin-angiotensin system and the adrenergic nervous system.

Authors:  Laura P Svetkey; Emily L Harris; Eden Martin; William M Vollmer; Gayle T Meltesen; Vincent Ricchiuti; Gordon Williams; Lawrence J Appel; George A Bray; Thomas J Moore; Michelle P Winn; Paul R Conlin
Journal:  Am J Hypertens       Date:  2010-11-18       Impact factor: 2.689

8.  Analysis and exploration of the use of rule-based algorithms and consensus methods for the inferral of haplotypes.

Authors:  Steven Hecht Orzack; Daniel Gusfield; Jeffrey Olson; Steven Nesbitt; Lakshman Subrahmanyan; Vincent P Stanton
Journal:  Genetics       Date:  2003-10       Impact factor: 4.562

Review 9.  A comprehensive literature review of haplotyping software and methods for use with unrelated individuals.

Authors:  Rany M Salem; Jennifer Wessel; Nicholas J Schork
Journal:  Hum Genomics       Date:  2005-03       Impact factor: 4.639

10.  A novel tool for individual haplotype inference using mixed data.

Authors:  Chen-Pang Lin; Cathy S J Fann
Journal:  J Biomed Sci       Date:  2009-06-02       Impact factor: 8.410

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