Literature DB >> 12548671

Limits of fine-mapping a quantitative trait.

Larry D Atwood1, Nancy L Heard-Costa.   

Abstract

Once a significant linkage is found, an important goal is reducing the error in the estimated location of the linked locus. A common approach to reducing location error, called fine-mapping, is the genotyping of additional markers in the linked region to increase the genetic information. The utility of fine-mapping for quantitative trait linkage analysis is largely unknown. To explore this issue, we performed a fine-mapping simulation in which the region containing a significant linkage at a 10-centiMorgan (cM) resolution was fine-mapped at 2, 1, and 0.5 cM. We simulated six quantitative trait models in which the proportion of variation due to the quantitative trait locus (QTL) ranged from 0.20-0.90. We used four sampling designs that were all combinations of 100 and 200 families of sizes 5 and 7. Variance components linkage analysis (Genehunter) was performed until 1,000 replicates were found with a maximum lodscore greater than 3.0. For each of these 1,000 replications, we repeated the linkage analysis three times: once for each of the fine-map resolutions. For the most realistic model, reduction in the average location error ranged from 3-15% for 2-cM fine-mapping and from 3-18% for 1-cM fine-mapping, depending on the number of families and family size. Fine-mapping at 0.5 cM did not differ from the 1-cM results. Thus, if the QTL accounts for a small proportion of the variation, as is the case for realistic traits, fine-mapping has little value. Copyright 2003 Wiley-Liss, Inc.

Mesh:

Year:  2003        PMID: 12548671     DOI: 10.1002/gepi.10225

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


  7 in total

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2.  Agreement among type 2 diabetes linkage studies but a poor correlation with results from genome-wide association studies.

Authors:  S Lillioja; A Wilton
Journal:  Diabetologia       Date:  2009-03-19       Impact factor: 10.122

3.  Genomewide scan for real-word reading subphenotypes of dyslexia: novel chromosome 13 locus and genetic complexity.

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4.  Genome scan for cognitive trait loci of dyslexia: Rapid naming and rapid switching of letters, numbers, and colors.

Authors:  Kevin B Rubenstein; Wendy H Raskind; Virginia W Berninger; Mark M Matsushita; Ellen M Wijsman
Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2014-05-08       Impact factor: 3.568

5.  Joint linkage and segregation analysis under multiallelic trait inheritance: simplifying interpretations for complex traits.

Authors:  Elisabeth A Rosenthal; Ellen M Wijsman
Journal:  Genet Epidemiol       Date:  2010-05       Impact factor: 2.135

6.  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

7.  Genome scan of a nonword repetition phenotype in families with dyslexia: evidence for multiple loci.

Authors:  Zoran Brkanac; Nicola H Chapman; Robert P Igo; Mark M Matsushita; Kathleen Nielsen; Virginia W Berninger; Ellen M Wijsman; Wendy H Raskind
Journal:  Behav Genet       Date:  2008-07-08       Impact factor: 2.805

  7 in total

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