Literature DB >> 16715099

Evaluating coverage of genome-wide association studies.

Jeffrey C Barrett1, Lon R Cardon.   

Abstract

Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.

Mesh:

Year:  2006        PMID: 16715099     DOI: 10.1038/ng1801

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


  165 in total

1.  Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.

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2.  You've gotta be lucky: Coverage and the elusive gene-gene interaction.

Authors:  Matthew Reimherr; Dan L Nicolae
Journal:  Ann Hum Genet       Date:  2010-10-26       Impact factor: 1.670

3.  Mapping of the UGT1A locus identifies an uncommon coding variant that affects mRNA expression and protects from bladder cancer.

Authors:  Wei Tang; Yi-Ping Fu; Jonine D Figueroa; Núria Malats; Montserrat Garcia-Closas; Nilanjan Chatterjee; Manolis Kogevinas; Dalsu Baris; Michael Thun; Jennifer L Hall; Immaculata De Vivo; Demetrius Albanes; Patricia Porter-Gill; Mark P Purdue; Laurie Burdett; Luyang Liu; Amy Hutchinson; Timothy Myers; Adonina Tardón; Consol Serra; Alfredo Carrato; Reina Garcia-Closas; Josep Lloreta; Alison Johnson; Molly Schwenn; Margaret R Karagas; Alan Schned; Amanda Black; Eric J Jacobs; W Ryan Diver; Susan M Gapstur; Jarmo Virtamo; David J Hunter; Joseph F Fraumeni; Stephen J Chanock; Debra T Silverman; Nathaniel Rothman; Ludmila Prokunina-Olsson
Journal:  Hum Mol Genet       Date:  2012-01-06       Impact factor: 6.150

4.  Using Ascertainment for Targeted Resequencing to Increase Power to Identify Causal Variants.

Authors:  M D Swartz; B Peng; C Reyes-Gibby; S Shete
Journal:  Stat Interface       Date:  2011       Impact factor: 0.582

5.  Replication of past candidate loci for common diseases and phenotypes in 100 genome-wide association studies.

Authors:  Konstantinos C M Siontis; Nikolaos A Patsopoulos; John P A Ioannidis
Journal:  Eur J Hum Genet       Date:  2010-03-17       Impact factor: 4.246

Review 6.  Genome-wide association studies of hypertension: have they been fruitful?

Authors:  Sajjad Rafiq; Sonia Anand; Robert Roberts
Journal:  J Cardiovasc Transl Res       Date:  2010-03-30       Impact factor: 4.132

Review 7.  Genome-wide significant associations for variants with minor allele frequency of 5% or less--an overview: A HuGE review.

Authors:  Orestis A Panagiotou; Evangelos Evangelou; John P A Ioannidis
Journal:  Am J Epidemiol       Date:  2010-09-28       Impact factor: 4.897

8.  Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects.

Authors:  Yun J Sung; C Charles Gu; Hemant K Tiwari; Donna K Arnett; Ulrich Broeckel; Dabeeru C Rao
Journal:  Genet Epidemiol       Date:  2012-05-29       Impact factor: 2.135

9.  Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases.

Authors:  Chuanhua Xing; Jie Huang; Yi-Hsiang Hsu; Anita L DeStefano; Nancy L Heard-Costa; Philip A Wolf; Sudha Seshadri; Douglas P Kiel; L Adrienne Cupples; Josée Dupuis
Journal:  Eur J Hum Genet       Date:  2015-11-18       Impact factor: 4.246

Review 10.  Type 2 diabetes: new genes, new understanding.

Authors:  Inga Prokopenko; Mark I McCarthy; Cecilia M Lindgren
Journal:  Trends Genet       Date:  2008-10-25       Impact factor: 11.639

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