Familial influences on Neuroticism and Education in the UK Biobank

AbstractGenome-wide studies often exclude family members, even though they are a valuable source of information. We identified parent-offspring pairs, siblings and couples in the UK Biobank and implemented a family-based DNA-derived heritability method to capture additional genetic effects and multiple sources of environmental influence on neuroticism and years of education. Compared to estimates from unrelated individuals, heritability increased from 10% to 27% and from 19% to 57% for neuroticism and education respectively by including family-based genetic effects. We detected no family environmental influences on neuroticism, but years of education was substantially influenced by couple similarity (38%). Overall, our genetic and environmental estimates closely replicate previous findings from an independent sample, but more research is required to dissect contributions to the additional heritability, particularly rare and structural genetic effects and residual environmental confounding. The latter is especially relevant for years of education, a highly socially-contingent variable, for which our heritability estimate is at the upper end of twin estimates in the literature. Family-based genetic effects narrow the gap between twin and DNA-based heritability methods, and could be harnessed to improve polygenic prediction.

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Familial Influences on Neuroticism and Education in the UK Biobank

Behavior Genetics ◽

10.1007/s10519-019-09984-5 ◽

2019 ◽

Vol 50 (2) ◽

pp. 84-93 ◽

Cited By ~ 1

Author(s):

R. Cheesman ◽

J. Coleman ◽

C. Rayner ◽

K. L. Purves ◽

G. Morneau-Vaillancourt ◽

...

Keyword(s):

Assortative Mating ◽

Environmental Influence ◽

Heritability Estimate ◽

Genetic Effects ◽

Uk Biobank ◽

Multiple Sources ◽

Genome Wide ◽

Familial Influences ◽

Family Based ◽

The Uk

AbstractGenome-wide studies often exclude family members, even though they are a valuable source of information. We identified parent–offspring pairs, siblings and couples in the UK Biobank and implemented a family-based DNA-derived heritability method to capture additional genetic effects and multiple sources of environmental influence on neuroticism and years of education. Compared to estimates from unrelated individuals, total heritability increased from 10 to 27% and from 17 to 56% for neuroticism and education respectively by including family-based genetic effects. We detected no family environmental influences on neuroticism. The couple similarity variance component explained 35% of the variation in years of education, probably reflecting assortative mating. Overall, our genetic and environmental estimates closely replicate previous findings from an independent sample. However, more research is required to dissect contributions to the additional heritability by rare and structural genetic effects, assortative mating, and residual environmental confounding. The latter is especially relevant for years of education, a highly socially contingent variable, for which our heritability estimate is at the upper end of twin estimates in the literature. Family-based genetic effects could be harnessed to improve polygenic prediction.

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Evaluating the genetic effects of sex hormone on the development of mental traits: a polygenic score analysis and genome-wide genetic interaction analysis in the UK Biobank cohort

10.21203/rs.3.rs-32855/v1 ◽

2020 ◽

Author(s):

Xiao Liang ◽

ShiQiang Cheng ◽

Jing Ye ◽

XiaoMeng Chu ◽

Yan Wen ◽

...

Keyword(s):

Alcohol Consumption ◽

Fluid Intelligence ◽

Genetic Interaction ◽

Interaction Analysis ◽

Genetic Effects ◽

Sex Hormone ◽

Uk Biobank ◽

Genome Wide ◽

Study Results ◽

The Uk

Abstract Objective: To evaluate the genetic effects of sex hormone on the development of mental traits.Methods: The SNPs significantly associated with sex hormone traits were driven from a two-stage genome-wide association study (GWAS). Four sex hormone were selected in this study, including sex hormone-binding globulin (SHBG), testosterone, bioavailable testosterone and estradiol. The polygenic risk scores (PRS) of sex hormone traits were calculated from individual-level genotype data of the UK Biobank cohort. We then used logistic and linear regression models to assess the associations between individual PRS of sex hormone traits and the frequency of alcohol consumption, anxiety, intelligence and so on. Finally, genome-wide genetic interaction study (GWGIS) was performed to detect novel candidate genes interacting with the sex hormone on the development of fluid intelligence and the frequency of smoking and alcohol consumption by PLINK2.0.Results: We observed positive associations between SHBG and the frequency of alcohol consumption (b=0.01, p=3.84×10–11) in males and females. In addition, estradiol was positively associated with the frequency of alcohol consumption (b=0.01, p=1,96×10–8), fluid intelligence (b=0.01, p=1.90×10–2) and the frequency of smoking (b=0.01, p=1.77×10–2) in males. Moreover, SHBG was associated with the frequency of alcohol consumption (b=0.01, p=2.60×10–3), fluid intelligence (b=0.01, p=4.25×10–2) and anxiety (b=-0.01, p=3.79×10–2) in females. Finally, GWGIS identified one significant loci, Tenascin R (TNR) (rs34633780, p=3.45×10–8) interacting with total testosterone for fluid intelligence.Conclusion: Our study results support the genetic effects of sex hormone on the development of intelligence and the frequency of alcohol consumption.

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Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data: a comparison of multivariate methods

10.1101/2020.11.02.365981 ◽

2020 ◽

Author(s):

Nicole M. Warrington ◽

Liang-Dar Hwang ◽

Michel Nivard ◽

David M. Evans

Keyword(s):

Genetic Effect ◽

Genetic Effects ◽

Computational Time ◽

Standard Errors ◽

Multivariate Methods ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Indirect Genetic Effects ◽

Genome Wide ◽

The Uk

AbstractEstimation of the direct genetic effect of an individuals’ own genotype on their phenotype, independent of any contaminating indirect parental genetic effects is becoming increasingly important. These conditional estimates are of interest in their own right, but are also useful for downstream analyses such as intergenerational Mendelian randomization. We compare several available multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate conditional direct and indirect genetic effects, while accounting for sample overlap. Using robustly associated birth weight variants and data from the UK Biobank, we contrasted the point estimates and their standard errors at each of the individual loci compared to those obtained using individual level genotype data, in addition to comparing the computational time, inflation of the test statistics and number of genome-wide significant SNPs identified by each of the methods. We show that Genomic SEM outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We subsequently applied Genomic SEM to fertility data in the UK Biobank and partitioned the genetic effect into female and male fertility in addition to a sibling specific effect. This analysis identified one novel locus for fertility and replicated seven previously identified loci. We also identified genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We therefore recommend Genomic SEM to be used to partition genetic effects across the genome into direct and indirect components.

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Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data

Nature Communications ◽

10.1038/s41467-021-25723-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Nicole M. Warrington ◽

Liang-Dar Hwang ◽

Michel G. Nivard ◽

David M. Evans

Keyword(s):

Association Studies ◽

Well Being ◽

Genetic Effects ◽

Genome Wide Association ◽

Standard Errors ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Indirect Genetic Effects ◽

Genome Wide ◽

The Uk

AbstractEstimation of direct and indirect (i.e. parental and/or sibling) genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies.

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Analysis of genetic dominance in the UK Biobank

10.1101/2021.08.15.456387 ◽

2021 ◽

Author(s):

Duncan S Palmer ◽

Wei Zhou ◽

Liam Abbott ◽

Nik Baya ◽

Claire Churchhouse ◽

...

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Model Organisms ◽

Systematic Evaluation ◽

Hair Color ◽

Phenotypic Variance ◽

Additive Effects ◽

Uk Biobank ◽

Genome Wide ◽

The Uk

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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Genome-Wide Association Meta-Analysis Supports Genes Involved in Valve and Cardiac Development to Associate With Mitral Valve Prolapse

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.120.003148 ◽

2021 ◽

Author(s):

Mengyao Yu ◽

Sergiy Kyryachenko ◽

Stephanie Debette ◽

Philippe Amouyel ◽

Jean-Jacques Schott ◽

...

Keyword(s):

Mitral Valve ◽

Association Study ◽

Genome Wide Association Study ◽

Cardiac Development ◽

Meta Analysis ◽

Genome Wide Association ◽

Uk Biobank ◽

Additional Risk ◽

Genome Wide ◽

The Uk

Background: Mitral valve prolapse (MVP) is a common cardiac valve disease, which affects 1 in 40 in the general population. Previous genome-wide association study have identified 6 risk loci for MVP. But these loci explained only partially the genetic risk for MVP. We aim to identify additional risk loci for MVP by adding data set from the UK Biobank. Methods: We reanalyzed 1007/479 cases from the MVP-France study, 1469/862 controls from the MVP-Nantes study for reimputation genotypes using HRC and TOPMed panels. We also incorporated 434 MVP cases and 4527 controls from the UK Biobank for discovery analyses. Genetic association was conducted using SNPTEST and meta-analyses using METAL. We used FUMA for post-genome-wide association study annotations and MAGMA for gene-based and gene-set analyses. Results: We found TOPMed imputation to perform better in terms of accuracy in the lower ranges of minor allele frequency below 0.1. Our updated meta-analysis included UK Biobank study for ≈8 million common single-nucleotide polymorphisms (minor allele frequency >0.01) and replicated the association on Chr2 as the top association signal near TNS1 . We identified an additional risk locus on Chr1 ( SYT2 ) and 2 suggestive risk loci on chr8 ( MSRA ) and chr19 ( FBXO46 ), all driven by common variants. Gene-based association using MAGMA revealed 6 risk genes for MVP with pronounced expression levels in cardiovascular tissues, especially the heart and globally part of enriched GO terms related to cardiac development. Conclusions: We report an updated meta-analysis genome-wide association study for MVP using dense imputation coverage and an improved case-control sample. We describe several loci and genes with MVP spanning biological mechanisms highly relevant to MVP, especially during valve and heart development.

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