Literature DB >> 12931046

Adding further power to the Haseman and Elston method for detecting linkage in larger sibships: weighting sums and differences.

Sanjay Shete1, Kevin B Jacobs, Robert C Elston.   

Abstract

Haseman and Elston (H-E) proposed a robust test to detect linkage between a quantitative trait and a genetic marker. In their method the squared sib-pair trait difference is regressed on the estimated proportion of alleles at a locus shared identical by descent by sib pairs. This method has recently been improved by changing the dependent variable from the squared difference to the mean-corrected product of the sib-pair trait values, a significantly positive regression indicating linkage. Because situations arise in which the original test is more powerful, a further improvement of the H-E method occurs when the dependent variable is changed to a weighted average of the squared sib-pair trait difference and the squared sib-pair mean-corrected trait sum. Here we propose an optimal method of performing this weighting for larger sibships, allowing for the correlation between pairs within a sibship. The optimal weights are inversely proportional to the residual variances obtained from the two different regressions based on the squared sib-pair trait differences and the squared sib-pair mean-corrected trait sums, respectively, allowing for correlations among sib pairs. The proposed method is compared with the existing extension of the H-E approach for larger sibships. Control of the type I error probabilities for sibships of any size can be improved by using a generalized estimating equation approach and the robust sandwich estimate of the variance, or a Monte-Carlo permutation test. Copyright 2003 S. Karger AG, Basel

Mesh:

Year:  2003        PMID: 12931046     DOI: 10.1159/000072312

Source DB:  PubMed          Journal:  Hum Hered        ISSN: 0001-5652            Impact factor:   0.444


  44 in total

1.  Quantitative trait loci on chromosomes 1, 2, 3, 4, 8, 9, 11, 12, and 18 control variation in levels of T and B lymphocyte subpopulations.

Authors:  M A Hall; P J Norman; B Thiel; H Tiwari; A Peiffer; R W Vaughan; S Prescott; M Leppert; N J Schork; J S Lanchbury
Journal:  Am J Hum Genet       Date:  2002-04-09       Impact factor: 11.025

2.  A whole-genome screen of a quantitative trait of age-related maculopathy in sibships from the Beaver Dam Eye Study.

Authors:  James H Schick; Sudha K Iyengar; Barbara E Klein; Ronald Klein; Karlie Reading; Rachel Liptak; Christopher Millard; Kristine E Lee; Sandra C Tomany; Emily L Moore; Bonnie A Fijal; Robert C Elston
Journal:  Am J Hum Genet       Date:  2003-04-24       Impact factor: 11.025

3.  Literacy outcomes of children with early childhood speech sound disorders: impact of endophenotypes.

Authors:  Barbara A Lewis; Allison A Avrich; Lisa A Freebairn; Amy J Hansen; Lara E Sucheston; Iris Kuo; H Gerry Taylor; Sudha K Iyengar; Catherine M Stein
Journal:  J Speech Lang Hear Res       Date:  2011-09-19       Impact factor: 2.297

4.  Dissection of genomewide-scan data in extended families reveals a major locus and oligogenic susceptibility for age-related macular degeneration.

Authors:  Sudha K Iyengar; Danhong Song; Barbara E K Klein; Ronald Klein; James H Schick; Jennifer Humphrey; Christopher Millard; Rachel Liptak; Karlie Russo; Gyungah Jun; Kristine E Lee; Bonnie Fijal; Robert C Elston
Journal:  Am J Hum Genet       Date:  2003-12-19       Impact factor: 11.025

5.  Pleiotropic effects of a chromosome 3 locus on speech-sound disorder and reading.

Authors:  Catherine M Stein; James H Schick; H Gerry Taylor; Lawrence D Shriberg; Christopher Millard; Amy Kundtz-Kluge; Karlie Russo; Nori Minich; Amy Hansen; Lisa A Freebairn; Robert C Elston; Barbara A Lewis; Sudha K Iyengar
Journal:  Am J Hum Genet       Date:  2004-01-20       Impact factor: 11.025

Review 6.  Genetic epidemiological approaches in the study of risk factors for cardiovascular disease.

Authors:  Anastasia Iliadou; Harold Snieder
Journal:  Eur J Epidemiol       Date:  2004       Impact factor: 8.082

7.  Some capabilities for model-based and model-free linkage analysis using the program package S.A.G.E. (Statistical Analysis for Genetic Epidemiology).

Authors:  A H Schnell; X Sun; R P Igo; R C Elston
Journal:  Hum Hered       Date:  2011-12-23       Impact factor: 0.444

8.  A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families.

Authors:  Laurel A Bastone; Richard S Spielman; Xingmei Wang; Thomas R Ten Have; Mary E Putt
Journal:  Hum Hered       Date:  2010-06-17       Impact factor: 0.444

9.  A genomewide search finds major susceptibility loci for nicotine dependence on chromosome 10 in African Americans.

Authors:  Ming D Li; Thomas J Payne; Jennie Z Ma; Xiang-Yang Lou; Dong Zhang; Randolph T Dupont; Karen M Crews; Grant Somes; Nancy J Williams; Robert C Elston
Journal:  Am J Hum Genet       Date:  2006-08-30       Impact factor: 11.025

10.  Genome-wide linkage analysis of multiple metabolic factors: evidence of genetic heterogeneity.

Authors:  Ching-Yu Cheng; Kristine E Lee; Priya Duggal; Emily L Moore; Alexander F Wilson; Ronald Klein; Joan E Bailey-Wilson; Barbara E K Klein
Journal:  Obesity (Silver Spring)       Date:  2009-05-14       Impact factor: 5.002

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