Literature DB >> 18197190

Within-family outliers: segregating alleles or environmental effects? A linkage analysis of height from 5815 sibling pairs.

Beben Benyamin1, Markus Perola, Belinda K Cornes, Pamela Af Madden, Aarno Palotie, Dale R Nyholt, Grant W Montgomery, Leena Peltonen, Nicholas G Martin, Peter M Visscher.   

Abstract

Most information in linkage analysis for quantitative traits comes from pairs of relatives that are phenotypically most discordant or concordant. Confounding this, within-family outliers from non-genetic causes may create false positives and negatives. We investigated the influence of within-family outliers empirically, using one of the largest genome-wide linkage scans for height. The subjects were drawn from Australian twin cohorts consisting of 8447 individuals in 2861 families, providing a total of 5815 possible pairs of siblings in sibships. A variance component linkage analysis was performed, either including or excluding the within-family outliers. Using the entire dataset, the largest LOD scores were on chromosome 15q (LOD 2.3) and 11q (1.5). Excluding within-family outliers increased the LOD score for most regions, but the LOD score on chromosome 15 decreased from 2.3 to 1.2, suggesting that the outliers may create false negatives and false positives, although rare alleles of large effect may also be an explanation. Several regions suggestive of linkage to height were found after removing the outliers, including 1q23.1 (2.0), 3q22.1 (1.9) and 5q32 (2.3). We conclude that the investigation of the effect of within-family outliers, which is usually neglected, should be a standard quality control measure in linkage analysis for complex traits and may reduce the noise for the search of common variants of modest effect size as well as help identify rare variants of large effect and clinical significance. We suggest that the effect of within-family outliers deserves further investigation via theoretical and simulation studies.

Mesh:

Year:  2008        PMID: 18197190     DOI: 10.1038/sj.ejhg.5201992

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  8 in total

1.  A versatile gene-based test for genome-wide association studies.

Authors:  Jimmy Z Liu; Allan F McRae; Dale R Nyholt; Sarah E Medland; Naomi R Wray; Kevin M Brown; Nicholas K Hayward; Grant W Montgomery; Peter M Visscher; Nicholas G Martin; Stuart Macgregor
Journal:  Am J Hum Genet       Date:  2010-07-09       Impact factor: 11.025

2.  A genome-wide linkage scan for age at menarche in three populations of European descent.

Authors:  Carl A Anderson; Gu Zhu; Mario Falchi; Stéphanie M van den Berg; Susan A Treloar; Timothy D Spector; Nicholas G Martin; Dorret I Boomsma; Peter M Visscher; Grant W Montgomery
Journal:  J Clin Endocrinol Metab       Date:  2008-07-22       Impact factor: 5.958

3.  Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs.

Authors:  Gibran Hemani; Jian Yang; Anna Vinkhuyzen; Joseph E Powell; Gonneke Willemsen; Jouke-Jan Hottenga; Abdel Abdellaoui; Massimo Mangino; Ana M Valdes; Sarah E Medland; Pamela A Madden; Andrew C Heath; Anjali K Henders; Dale R Nyholt; Eco J C de Geus; Patrik K E Magnusson; Erik Ingelsson; Grant W Montgomery; Timothy D Spector; Dorret I Boomsma; Nancy L Pedersen; Nicholas G Martin; Peter M Visscher
Journal:  Am J Hum Genet       Date:  2013-10-31       Impact factor: 11.025

4.  Phenotype matters: the case for careful characterization of relevant traits.

Authors:  Linda M Brzustowicz; Anne S Bassett
Journal:  Am J Psychiatry       Date:  2008-09       Impact factor: 18.112

5.  Genome-wide association study of height and body mass index in Australian twin families.

Authors:  Jimmy Z Liu; Sarah E Medland; Margaret J Wright; Anjali K Henders; Andrew C Heath; Pamela A F Madden; Alexis Duncan; Grant W Montgomery; Nicholas G Martin; Allan F McRae
Journal:  Twin Res Hum Genet       Date:  2010-04       Impact factor: 1.587

6.  Can we identify genes for alcohol consumption in samples ascertained for heterogeneous purposes?

Authors:  Narelle K Hansell; Arpana Agrawal; John B Whitfield; Katherine I Morley; Scott D Gordon; Penelope A Lind; Michele L Pergadia; Grant W Montgomery; Pamela A F Madden; Richard D Todd; Andrew C Heath; Nicholas G Martin
Journal:  Alcohol Clin Exp Res       Date:  2009-01-22       Impact factor: 3.455

7.  Linkage analysis of alcohol dependence symptoms in the community.

Authors:  Narelle K Hansell; Arpana Agrawal; John B Whitfield; Katherine I Morley; Scott D Gordon; Penelope A Lind; Michele L Pergadia; Grant W Montgomery; Pamela A F Madden; Richard D Todd; Andrew C Heath; Nicholas G Martin
Journal:  Alcohol Clin Exp Res       Date:  2009-10-23       Impact factor: 3.455

8.  Linkage analysis of adult height in a large pedigree from a Dutch genetically isolated population.

Authors:  Tatiana I Axenovich; I V Zorkoltseva; N M Belonogova; M V Struchalin; A V Kirichenko; M Kayser; B A Oostra; C M van Duijn; Y S Aulchenko
Journal:  Hum Genet       Date:  2009-05-24       Impact factor: 4.132

  8 in total

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