Literature DB >> 25112185

Complex pedigrees in the sequencing era: to track transmissions or decorrelate?

Dalin Li1, Jin Zhou, Duncan C Thomas, David W Fardo.   

Abstract

Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).
© 2014 WILEY PERIODICALS, INC.

Entities:  

Keywords:  Genetic Analysis Workshop 18; decorrelation strategies; family-based association testing; next-generation sequencing

Mesh:

Year:  2014        PMID: 25112185      PMCID: PMC4272198          DOI: 10.1002/gepi.21822

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.344


  52 in total

1.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Authors:  Andrew D Skol; Laura J Scott; Gonçalo R Abecasis; Michael Boehnke
Journal:  Nat Genet       Date:  2006-01-15       Impact factor: 38.330

2.  Using linkage genome scans to improve power of association in genome scans.

Authors:  Kathryn Roeder; Silvi-Alin Bacanu; Larry Wasserman; B Devlin
Journal:  Am J Hum Genet       Date:  2006-01-03       Impact factor: 11.025

3.  Genomic screening and replication using the same data set in family-based association testing.

Authors:  Kristel Van Steen; Matthew B McQueen; Alan Herbert; Benjamin Raby; Helen Lyon; Dawn L Demeo; Amy Murphy; Jessica Su; Soma Datta; Carsten Rosenow; Michael Christman; Edwin K Silverman; Nan M Laird; Scott T Weiss; Christoph Lange
Journal:  Nat Genet       Date:  2005-06-05       Impact factor: 38.330

4.  Adaptive two-stage analysis of genetic association in case-control designs.

Authors:  Gang Zheng; Kijoung Song; Robert C Elston
Journal:  Hum Hered       Date:  2007-02-19       Impact factor: 0.444

5.  Gene-environment interaction in genome-wide association studies.

Authors:  Cassandra E Murcray; Juan Pablo Lewinger; W James Gauderman
Journal:  Am J Epidemiol       Date:  2008-11-20       Impact factor: 4.897

6.  Variance component model to account for sample structure in genome-wide association studies.

Authors:  Hyun Min Kang; Jae Hoon Sul; Susan K Service; Noah A Zaitlen; Sit-Yee Kong; Nelson B Freimer; Chiara Sabatti; Eleazar Eskin
Journal:  Nat Genet       Date:  2010-03-07       Impact factor: 38.330

7.  Sample size requirements to detect gene-environment interactions in genome-wide association studies.

Authors:  Cassandra E Murcray; Juan Pablo Lewinger; David V Conti; Duncan C Thomas; W James Gauderman
Journal:  Genet Epidemiol       Date:  2011-02-09       Impact factor: 2.135

8.  SNP set association analysis for familial data.

Authors:  Elizabeth D Schifano; Michael P Epstein; Lawrence F Bielak; Min A Jhun; Sharon L R Kardia; Patricia A Peyser; Xihong Lin
Journal:  Genet Epidemiol       Date:  2012-09-11       Impact factor: 2.135

9.  Genome-wide efficient mixed-model analysis for association studies.

Authors:  Xiang Zhou; Matthew Stephens
Journal:  Nat Genet       Date:  2012-06-17       Impact factor: 38.330

10.  Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data.

Authors:  Jian Wang; Robert Yu; Sanjay Shete
Journal:  BMC Proc       Date:  2014-06-17
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  1 in total

1.  Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population.

Authors:  Sergey A Kornilov; Natalia Rakhlin; Roman Koposov; Maria Lee; Carolyn Yrigollen; Ahmet Okay Caglayan; James S Magnuson; Shrikant Mane; Joseph T Chang; Elena L Grigorenko
Journal:  Pediatrics       Date:  2016-03-25       Impact factor: 7.124

  1 in total

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