Genotype–covariate interaction effects and the heritability of adult body mass index

Robinson, Matthew R.; English, Geoffrey; Moser, G.; Lloyd‐Jones, Luke R.; Triplett, Marcus A.; Zhu, Zhihong; Nolte, Ilja M.; Vliet-Ostaptchouk, Jana V. van; Snieder, Harold; Esko, Tõnu; Milani, Lili; Mägi, Reedik; Metspalu, Andres; Magnusson, Patrik K. E.; Pedersen, Nancy L.; Ingelsson, Erik; Johannesson, Magnus; Yang, Jian; Cesarini, David; Visscher, Peter M.

doi:10.1038/ng.3912

Cited by 142 publications

(191 citation statements)

References 53 publications

Supporting

Mentioning

159

Contrasting

Order By: Relevance

“…The increasing need for large, well‐characterized cohorts of healthy individuals for modern biomedical research, such as genomics or phenomics studies, typically including tens or even hundreds of thousands of subjects, has posed the possibility of using blood services as an option for collecting samples and related data. The possibility to recontact blood donors for repeated sampling or asking for additional data has further increased interest in collecting large biobanks from blood donors.…”

mentioning

confidence: 99%

Blood donors' preferences for blood donation for biomedical research

et al. 2018

View full text Add to dashboard Cite

BACKGROUND: Increasing numbers of blood donors are recruited to participate in biomedical research. As blood services depend on voluntary donors, successful recruitment calls for a better understanding of donors' expectations and attitudes toward the use of samples in research. STUDY DESIGN AND METHODS:Sixty-one semistructured interviews were conducted with blood donors at eight Finnish Red Cross Blood Service donation sites in Finland. The 10-to 30-minute interviews included open-ended questions about donors' views on blood donation for patients and for biomedical research. Central motives to donate blood for patients were identified against which views on research use were compared to see how these reflections differed.RESULTS: Six central motives for donating blood for patients were identified among donors. The interviewees were, in general, willing to donate blood for research, but considered research donation more likely if it could be easily integrated into their usual blood donation habits. Biomedical research was perceived as important but its social benefits were more abstract than a direct help to patients.CONCLUSIONS: Familiarity and reciprocity were key to the relationship between the blood service and blood donors. Donation for research introduces a new, more complex context to blood donation. Challenge to recognize concrete outcomes and benefits of donation may affect willingness to donate for research. Transparent communication of the role of the blood service in research and of the beneficiaries of the research is essential in maintaining trust. These results will help blood services in their planning to recruit blood donors for research projects.

show abstract

mentioning

confidence: 99%

Blood donors' preferences for blood donation for biomedical research

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Robinson et al 6 have previously attempted to quantify the contribution of GxE interactions to the heritability of BMI in a study performed on imputed SNPs from the interim UK Biobank release. Using the GCI-GREML model implemented in GCTA 43 and eight environmental variables that included measures of smoking, hours of TV watched and alcohol frequency, Robinson et al 6 reported that only smoking had significant GxE heritability (4.0%).…”

Section: Discussionmentioning

confidence: 99%

“…Previous cross-sectional studies have reported GxE interactions between a linear predictor formed from BMI-associated SNPs and alcohol intake frequency 32 , Townsend Index 32 , physical activity measures 5,32,33 and time watching TV 32,33 , all of which are upweighted in the LEMMA lifestyle score. An alternative approach from Robinson et al 6 binned samples according to their environmental exposure (eg. age) and tested for significant differences in SNP heritability using a likelihood ratio test.…”

Section: Gxe Interaction Analysis In the Uk Biobankmentioning

confidence: 99%

Gene-environment interactions using a Bayesian whole genome regression model

Kerin

Marchini

2019

Preprint

View full text Add to dashboard Cite

The contribution of gene-environment (GxE) interactions for many human traits and diseases is poorly characterised. We propose a method, LEMMA, that estimates an interpretable environmental score (ES) that interacts with genetic markers throughout the genome. When applied to body mass index, systolic, diastolic and pulse pressure in the UK Biobank we estimate that 9.3%, 3.9%, 1.6% and 12.5% of phenotypic variance is explained by GxE interactions, and that rare variants explain most of this variance. We also identify 3 loci that interact with the estimated environmental scores (− log 10 p > 7). IntroductionDespite longstanding interest in gene-by-environment (GxE) interactions 1 , this facet of genetic architecture remains poorly characterised in humans. Detection of GxE interactions is inherently 1 more difficult than detection of additive genetics in genome wide association studies (GWAS).One difficulty is that of sample size; a commonly cited rule of thumb suggests that detection of interaction effects requires a sample size at least four times larger than that required to detect a main effect of comparable effect size 2 . Another is that an individual's environment, which occurs through time, is very hard to measure in a comprehensive way, and is inherently high dimensional.Also, there are many environmental variables that could plausibly interact with the genome and many ways to combine them, and typically these factors were not all present in the same dataset.The recently released UK Biobank 3 dataset, a large population cohort study with deep genotyping and sequencing and extensive phenotyping, offers a unique opportunity to explore GxE effects 4-10 .Models that consider environmental variables jointly can be advantageous, particularly if several environmental variables drive interactions at individual loci or if an unobserved environment that drives interactions is better reflected by a combination of observed environments. StructLMM 7 models the environmental similarity between individuals (over multiple environments) as a random effect, and then tests each SNP independently for GxE interactions, but does not model the genome wide contribution of all the markers, which is often a major component of phenotypic variance.Advances in methods applied to detect genetic main effects in standard GWAS have shown that linear mixed models (LMMs) can reduce false positive associations due to population structure, and improve power by implicitly conditioning on other loci across the genome [11][12][13] . Often these methods model the unobserved polygenic contribution as a multivariate Gaussian with covariance structure proportional to a genetic relationship matrix (GRM) [14][15][16] . This approach is (RHE) regression on UK Biobank scale datasets 22, 23 . This heritability analysis can be run on genotyped or imputed SNPs and stratified by MAF and LD to better interrogate the genetic architecture of GxE interactions. The ES is also used to test for GxE interactions one variant at a time, typically at a larger set of i...

show abstract

“…Most traits and common diseases in humans are complex because they are influenced by many genetic variants as well as environmental factors 1,2 . Genome-wide association studies (GWASs) have discovered >70,000 genetic variants associated with human complex traits and diseases 3,4 .…”

Section: Introductionmentioning

confidence: 99%

Quantifying genetic heterogeneity between continental populations for human height and body mass index

Guo

Bakshi

Wang

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ( " ) or genome-wide significant SNPs ( "($%&) ) for height and body mass index (BMI) in samples of European (EUR; = 49,839) and African (AFR; = 17,426) ancestry. The ̂" between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding " ($%&) was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that ̂" differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

show abstract

Genotype–covariate interaction effects and the heritability of adult body mass index

Cited by 142 publications

References 53 publications

Blood donors' preferences for blood donation for biomedical research

Blood donors' preferences for blood donation for biomedical research

Gene-environment interactions using a Bayesian whole genome regression model

Quantifying genetic heterogeneity between continental populations for human height and body mass index

Contact Info

Product

Resources

About