Publications by authors named "Ronald de Vlaming"

15 Publications

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Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.

Commun Biol 2021 10 12;4(1):1180. Epub 2021 Oct 12.

Department of Applied Economics, Erasmus School of Economics, Rotterdam, The Netherlands.

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.
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http://dx.doi.org/10.1038/s42003-021-02712-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511103PMC
October 2021

Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction.

Nat Neurosci 2021 10 26;24(10):1367-1376. Epub 2021 Aug 26.

Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA.

Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.
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http://dx.doi.org/10.1038/s41593-021-00908-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484054PMC
October 2021

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

Mol Psychiatry 2021 06 11;26(6):2056-2069. Epub 2020 May 11.

Department of Endocrinology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r| ≈ 0.1-0.3) and positive genetic correlations with physical activity (r ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.
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http://dx.doi.org/10.1038/s41380-020-0697-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767645PMC
June 2021

Mendelian randomization: the challenge of unobserved environmental confounds.

Int J Epidemiol 2019 06;48(3):665-671

Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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http://dx.doi.org/10.1093/ije/dyz138DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659461PMC
June 2019

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits.

Nat Hum Behav 2019 05 8;3(5):513-525. Epub 2019 Apr 8.

Department of Psychology, University of Texas at Austin, Austin, TX, USA.

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.
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http://dx.doi.org/10.1038/s41562-019-0566-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520146PMC
May 2019

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Nat Genet 2019 02 14;51(2):245-257. Epub 2019 Jan 14.

Team Loyalty BV, Hoofddorp, the Netherlands.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.
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http://dx.doi.org/10.1038/s41588-018-0309-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713272PMC
February 2019

Signatures of negative selection in the genetic architecture of human complex traits.

Nat Genet 2018 05 16;50(5):746-753. Epub 2018 Apr 16.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.
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http://dx.doi.org/10.1038/s41588-018-0101-4DOI Listing
May 2018

Hidden heritability due to heterogeneity across seven populations.

Nat Hum Behav 2017 Oct 11;1(10):757-765. Epub 2017 Sep 11.

Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK.

Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from diverse historical time periods and populations. Genetic scores derived from GWAS explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the 'hidden heritability' puzzle. Using seven sampling populations (N=35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.
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http://dx.doi.org/10.1038/s41562-017-0195-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642946PMC
October 2017

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.

PLoS Genet 2017 Jan 17;13(1):e1006495. Epub 2017 Jan 17.

Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.
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http://dx.doi.org/10.1371/journal.pgen.1006495DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240919PMC
January 2017

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

Nat Genet 2016 12 31;48(12):1462-1472. Epub 2016 Oct 31.

Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.
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http://dx.doi.org/10.1038/ng.3698DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695684PMC
December 2016

Genome-wide association study identifies 74 loci associated with educational attainment.

Nature 2016 05 11;533(7604):539-42. Epub 2016 May 11.

Department of Neurology, General Hospital and Medical University Graz, Graz 8036, Austria.

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.
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http://dx.doi.org/10.1038/nature17671DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883595PMC
May 2016

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.

Nat Genet 2016 06 18;48(6):624-33. Epub 2016 Apr 18.

Max Planck Institute for Human Development, Berlin, Germany.

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
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http://dx.doi.org/10.1038/ng.3552DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4884152PMC
June 2016

The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.

Biomed Res Int 2015 26;2015:143712. Epub 2015 Jul 26.

Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Postbus 1738, 3000 DR Rotterdam, Netherlands.

In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.
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http://dx.doi.org/10.1155/2015/143712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529984PMC
May 2016
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