Response to “Commentary on ‘Limitations of GCTA as a solution to the missing heritability problem”

Kumar, Sandeep; Mw, Feldman; Dh, Rehkopf; Tuljapurkar, Shripad

doi:10.1101/039594

Cited by 19 publications

(22 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This difference is most likely a consequence of the GCTA method, which only estimates the proportion of variance explained by additive contributions from the genotyped SNPs, not the total heritability. However, the accuracy of the method has also been questioned due to the underlying assumption that may not always be true (33,34). Given this, and the large confidence intervals, the results should be interpreted with caution.…”

Section: Discussionmentioning

confidence: 99%

Genetic determinants of circulating GIP and GLP-1 concentrations

Almgren¹,

Lindqvist²,

Krus³

et al. 2017

JCI Insight

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Genetic determinants of circulating GIP and GLP-1 concentrations

Almgren¹,

Lindqvist²,

Krus³

et al. 2017

JCI Insight

View full text Add to dashboard Cite

“…In addition, recent studies have argued that estimation error associated with genetic similarity measurements and the ill-posedness of the empirical genetic similarity matrix may produce unstable and unreliable SNP heritability estimates [65]. However, this is an area under active investigation and debate [64][65][66][67]. Here, as the first study to screen all UK Biobank variables and provide an overview of the distribution of SNP heritability across different trait domains, and to examine the effect of potential modifying variables on heritability estimates, we used a straightforward and classical modeling approach that is most widely used.…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, as reviewed in [64], a number of empirical genetic similarity measurements computable from genome-wide SNP data have been proposed, which, when utilized in heritability analysis, can give different estimates with different interpretations. In addition, recent studies have argued that estimation error associated with genetic similarity measurements and the ill-posedness of the empirical genetic similarity matrix may produce unstable and unreliable SNP heritability estimates [65]. However, this is an area under active investigation and debate [64][65][66][67].…”

Section: Discussionmentioning

confidence: 99%

Phenome-wide Heritability Analysis of the UK Biobank

Chen

Neale

et al. 2016

Preprint

View full text Add to dashboard Cite

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenomewide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability. Author summaryHeritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals. It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses. Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits. However, heritability is not a fixed property of a phenotype and can vary with population- specific differences in the genetic background and environmental variation. Here, using a computationally and memory efficient heritability estimation method, we report the heritability for a large number of traits derived from the large-scale, population-based UK Biobank, and, for the first time, demonstrate the moderating effect of three major demographic variables (age, sex and socioeconomic status) on heritability estimates derived from genomewide common genetic variation. Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank.

show abstract

“…Other related topics include gene-environment interaction, negative confounding between C and D factors, low power to detect C effects, heritability estimates from Genome-wide Complex Trait Analysis (GCTA), and finally epigenetic mechanisms. It is beyond the scope of this paper to address all these issues, but we refer the interested reader to some relevant literature (Boomsma et al, 2002;Canli & Lesch, 2007;Krishna Kumar et al, 2016;Moffitt et al, 2006;Petronis, 2010;Plomin et al, 2013;Tsankova et al, 2007;Yang et al, 2013).…”

Section: Equal Environment Assumption (Eea)mentioning

confidence: 99%

The beauty, logic and limitations of twin studies

Røysamb¹,

Tambs²

2016

Nor J Epidemiol

View full text Add to dashboard Cite

During the last fifty years more than 2700 twin studies have been published, examining the etiology of a high number of traits. Twin studies enable investigation of both genetic and environmental effects, and thereby also examination of causal factors involved in human traits and disorders. The beauty of twin studies resides in the potential of studying the unobserved by the logic of a design. The aim of this article is to outline central theoretical foundations and possible limitations, and to review selected key findings. We describe the inherent fundamentals of the classic and extended twin designs. The logic of the main analytic approaches is outlined, and the principles of univariate biometric modelling described. Next, we review different multivariate models, including the Cholesky, correlated factors, common factor, common pathway and phenotypic causality models. Additionally, the cotwin-control approach, representing a natural experimental design, and mimicking a counterfactual situation, is outlined. Central assumptions, threats and limitations of the twin design are discussed. In particular, we address the issue of missing heritability, non-random mating, the equal environment assumption and gene-environment correlations. Finally, we review some selected findings from the field of behavior genetics and twin studies.This is an open access article distributed under the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

show abstract

Response to “Commentary on ‘Limitations of GCTA as a solution to the missing heritability problem”

Abstract: In a recent manuscript, Yang and colleagues criticized our paper, "Limitations of GCTA as a solution to the missing heritability problem". Here we show that their main claims are statistically invalid, and our results hold as stated.

Cited by 19 publications

References 19 publications

Genetic determinants of circulating GIP and GLP-1 concentrations

Genetic determinants of circulating GIP and GLP-1 concentrations

Phenome-wide Heritability Analysis of the UK Biobank

The beauty, logic and limitations of twin studies

Contact Info

Product

Resources

About