Literature DB >> 23535720

Predicting the influence of common variants.

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Abstract

An ever-larger proportion of the liability to common and complex disease can be obtained by progressively larger studies. However, for most diseases, the sample sizes required to gain usable predictions will be out of reach of sequencing technologies for the foreseeable future. Array-based genotyping genome-wide association studies(GWAS) still offer a reliable harvest of biological hypotheses for many diseases, together with the secondary benefit of slowly improving prediction.

Mesh:

Year:  2013        PMID: 23535720     DOI: 10.1038/ng.2605

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


  2 in total

1.  The complexity of disease combinations in the Medicare population.

Authors:  James Sorace; Hui-Hsing Wong; Chris Worrall; Jeffrey Kelman; Shahin Saneinejad; Thomas MaCurdy
Journal:  Popul Health Manag       Date:  2011-01-17       Impact factor: 2.459

2.  Common SNPs explain a large proportion of the heritability for human height.

Authors:  Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher
Journal:  Nat Genet       Date:  2010-06-20       Impact factor: 38.330

  2 in total
  1 in total

1.  Why significant variables aren't automatically good predictors.

Authors:  Adeline Lo; Herman Chernoff; Tian Zheng; Shaw-Hwa Lo
Journal:  Proc Natl Acad Sci U S A       Date:  2015-10-26       Impact factor: 11.205

  1 in total

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