scholarly journals The production of within-family inequality: Insights and implications of integrating genetic data

2020 ◽  
Author(s):  
Jason M. Fletcher ◽  
Yuchang Wu ◽  
Zijie Zhao ◽  
Qiongshi Lu
Keyword(s):  
Educational Attainment ◽  
Large Scale ◽  
Genetic Data ◽  
Parental Investments ◽  
Sibling Pairs ◽  
Polygenic Scores ◽  
Investments In Children ◽  
Family Based ◽  
The Uk ◽  
Family Inequality

AbstractThe integration of genetic data within large-scale social and health surveys provides new opportunities to test long standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children’s abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children’s abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings’ polygenic scores for educational attainment is consequential for actual attainments. We find strong evidence of compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using siblingdifference designs.

scholarly journals Maximizing GWAS discovery and genomic prediction accuracy in Biobank data

2021 ◽  
Author(s):  
Etienne J Orliac ◽  
Daniel Trejo Banos ◽  
Sven Erik Ojavee ◽  
Kristi Läll ◽  
Reedik Mägi ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Mixed Linear Model ◽  
Snp Markers ◽  
Genetic Associations ◽  
Baseline Model ◽  
Individual Level ◽  
Polygenic Scores ◽  
The Uk

Across 21 heritable traits in the UK and Estonian Biobank data, a Bayesian grouped mixture of regressions model (GMRM) obtains the highest genomic prediction accuracy reported to date, 15% (SD 10%) greater than a baseline model without MAF-LD-annotation groups, and 106% (SD 50%) greater than mixed-linear model association (MLMA) estimate polygenic scores. Prediction accuracy was up to 13% (mean 4%, SD 3%) higher than theoretical expectations, at 76% of the h2SNP for height (R2 of 47%) and over 50% of the h2SNP for 12 traits. Using these predictors in MLMA, increased the independent GWAS loci detected from 16,899 using standard approaches to 18,837 using GMRM, an 11.5% increase. Modelling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for large-scale individual-level biobank-scale analyses and is facilitated by our scalable highly parallel open source GMRM software.

scholarly journals Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

2019 ◽  
Author(s):  
Ben Brumpton ◽  
Eleanor Sanderson ◽  
Fernando Pires Hartwig ◽  
Sean Harrison ◽  
Gunnhild Åberge Vie ◽  
...  
Keyword(s):  
Population Stratification ◽  
Mendelian Randomization ◽  
Genetic Data ◽  
Population Based ◽  
Health Study ◽  
Uk Biobank ◽  
Family Effects ◽  
Family Based ◽  
Using Data ◽  
The Uk

AbstractMendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

scholarly journals Adjusting for Common Variant Polygenic Scores Improves Yield in Rare Variant Association Analyses

2021 ◽  
Author(s):  
Sean J. Jurgens ◽  
James P. Pirruccello ◽  
Seung Hoan Choi ◽  
Valerie N. Morrill ◽  
Mark D. Chaffin ◽  
...  
Keyword(s):  
Rare Variant ◽  
Quantitative Traits ◽  
Large Scale ◽  
Common Variant ◽  
Sequencing Data ◽  
Rare Variant Association ◽  
Association Analyses ◽  
Polygenic Scores ◽  
Association Discovery ◽  
The Uk

With the emergence of large-scale sequencing data, methods for improving power in rare variant analyses (RVAT) are needed. Here, we show that adjusting for common variant polygenic scores improves the yield in gene-based RVAT across 65 quantitative traits in the UK Biobank (up to 20% increase at α=2.6x10-6), without a marked increase in false-positive rates or genomic inflation. Our results illustrate how adjusting for common variant effects can aid in rare variant association discovery.

scholarly journals Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

2020 ◽  
Vol 31 (5) ◽  
pp. 582-591 ◽  
Author(s):  
Rosa Cheesman ◽  
Avina Hunjan ◽  
Jonathan R. I. Coleman ◽  
Yasmin Ahmadzadeh ◽  
Robert Plomin ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Home Environment ◽  
Family Environment ◽  
Adoptive Parents ◽  
Genetic Influences ◽  
Uk Biobank ◽  
Gene Environment ◽  
Polygenic Scores ◽  
The Family ◽  
The Uk

Polygenic scores now explain approximately 10% of the variation in educational attainment. However, they capture not only genetic propensity but also information about the family environment. This is because of passive gene–environment correlation, whereby the correlation between offspring and parent genotypes results in an association between offspring genotypes and the rearing environment. We measured passive gene–environment correlation using information on 6,311 adoptees in the UK Biobank. Adoptees’ genotypes were less correlated with their rearing environments because they did not share genes with their adoptive parents. We found that polygenic scores were twice as predictive of years of education in nonadopted individuals compared with adoptees ( R2s = .074 vs. .037, p = 8.23 × 10−24). Individuals in the lowest decile of polygenic scores for education attained significantly more education if they were adopted, possibly because of educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment.

scholarly journals Childhood trauma, life-time self-harm, and suicidal behaviour and ideation are associated with polygenic scores for autism

2019 ◽  
Author(s):  
Varun Warrier ◽  
Simon Baron-Cohen
Keyword(s):  
Educational Attainment ◽  
Childhood Trauma ◽  
Genetic Predisposition ◽  
Suicidal Behaviour ◽  
Genetic Correlations ◽  
Life Time ◽  
Gene Environment ◽  
Polygenic Scores ◽  
Self Harm ◽  
The Uk

Abstract Autistic individuals experience significantly elevated rates of childhood trauma, self-harm and suicidal behaviour and ideation (SSBI). Is this purely the result of negative environmental experiences, or does this interact with genetic predisposition? In this study we investigated if a genetic predisposition for autism is associated with childhood trauma using polygenic scores (PGS) and genetic correlations in the UK Biobank (105,222 < N < 105,638), and tested potential mediators and moderators of the association between autism, childhood trauma and SSBI. Autism PGS were significantly associated with childhood trauma (max R2 = 0.096%, P < 2 × 10−16), self-harm ideation (max R2 = 0.108%, P < 2 × 10−16), and self-harm (max R2 = 0.13%, P < 2 × 10−16). Supporting this, we identified significant genetic correlations between autism and childhood trauma (rg = 0.36 ± 0.05, P = 8.13 × 10−11), self-harm ideation (rg = 0.49 ± 0.05, P = 4.17 × 10−21) and self-harm (rg = 0.48 ± 0.05, P = 4.58 × 10−21), and an over-transmission of PGS for the two SSBI phenotypes from parents to autistic probands. Male sex negatively moderated the effect of autism PGS on childhood trauma (β = −0.023 ± 0.005, P = 6.74 × 10−5). Further, childhood trauma positively moderated the effect of autism PGS on self-harm score (β = 8.37 × 10−3 ± 2.76 × 10−3, P = 2.42 × 10−3) and self-harm ideation (β = 7.47 × 10−3 ± 2.76 × 10−3, P = 6.71 × 10−3). Finally, depressive symptoms, quality and frequency of social interactions, and educational attainment were significant mediators of the effect of autism PGS on SSBI, with the proportion of effect mediated ranging from 0.23 (95% CI: 0.09–0.32) for depression to 0.008 (95% CI: 0.004–0.01) for educational attainment. Our findings identify that a genetic predisposition for autism is associated with adverse life-time outcomes, which represent complex gene-environment interactions, and prioritizes potential mediators and moderators of this shared biology. It is important to identify sources of trauma for autistic individuals in order to reduce their occurrence and impact.

scholarly journals Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs

PLoS Genetics ◽  
2020 ◽  
Vol 16 (10) ◽  
pp. e1009154 ◽  
Author(s):  
Liang-Dar Hwang ◽  
Justin D. Tubbs ◽  
Justin Luong ◽  
Mischa Lundberg ◽  
Gunn-Helen Moen ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Web Application ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Late Onset ◽  
Genetic Effects ◽  
Association Analyses ◽  
Sibling Pairs ◽  
Genome Wide

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to their children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset conditions, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating “virtual” mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We apply our approach to 19066 sibling pairs from the UK Biobank and show that a polygenic score consisting of imputed parental educational attainment SNP dosages is strongly related to offspring educational attainment even after correcting for offspring genotype at the same loci. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring.

scholarly journals Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications

2018 ◽  
Author(s):  
Simon Haworth ◽  
Ruth Mitchell ◽  
Laura Corbin ◽  
Kaitlin H Wade ◽  
Tom Dudding ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Data ◽  
Population Based ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Common Genetic Variants ◽  
The Uk

Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.We found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

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