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Literature DB >> 27457962

Reply to Yang et al.: GCTA produces unreliable heritability estimates.

Siddharth Krishna Kumar¹, Marcus W Feldman², David H Rehkopf³, Shripad Tuljapurkar².

Abstract

Mesh：

Year: 2016 PMID： 27457962 PMCID： PMC4987787 DOI： 10.1073/pnas.1608425113

Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN： 0027-8424 Impact factor: 11.205

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4 in total

1. Limitations of GCTA as a solution to the missing heritability problem.

Authors: Siddharth Krishna Kumar; Marcus W Feldman; David H Rehkopf; Shripad Tuljapurkar
Journal: Proc Natl Acad Sci U S A Date: 2015-12-22 Impact factor: 11.205

2. Correction for Krishna Kumar et al., Limitations of GCTA as a solution to the missing heritability problem.

Authors: Siddharth Krishna Kumar; Marcus W Feldman; David H Rehkopf; Shripad Tuljapurkar
Journal: Proc Natl Acad Sci U S A Date: 2016-02-01 Impact factor: 11.205

3. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs.

Authors: Jian Yang; S Hong Lee; Naomi R Wray; Michael E Goddard; Peter M Visscher
Journal: Proc Natl Acad Sci U S A Date: 2016-07-25 Impact factor: 11.205

4. 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

4 in total

Reply to Yang et al.: GCTA produces unreliable heritability estimates.

1. Limitations of GCTA as a solution to the missing heritability problem.

2. Correction for Krishna Kumar et al., Limitations of GCTA as a solution to the missing heritability problem.

3. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs.

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

1. The Impact of Genes on Adolescent Substance Use: A Developmental Perspective.

2. Phenome-wide heritability analysis of the UK Biobank.

3. Statistical properties of simple random-effects models for genetic heritability.

4. Best Prediction of the Additive Genomic Variance in Random-Effects Models.