Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Publisher Copyright: © 2022, The Author(s).Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.Peer reviewe

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Publisher Copyright: © 2022, The Author(s).We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.Peer reviewe

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects

Polygenic scores associated with educational attainment in adults predict educational achievement and ADHD symptoms in children

The following people who are not listed as co-authors on this manuscript contributed to the original GWAS meta-analysis on educational attainment [Rietveld et al., 2013], on which the present paper is based. Data access has been granted under section 4 of the Data Sharing Agreement of the Social Science Genetic Association Consortium (SSGAC). The views presented in the present paper may not reflect the opinions of the individuals listed below. The SSGAC is grateful to the authors of [Rietveld et al., 2013] for providing the meta-analysis data. We thank: Abdel Abdellaoui ... Debbie A. Lawlor ... et al.Abstract not availableEveline L. de Zeeuw, Catharina E.M. van Beijsterveldt, Tina J. Glasner, M. Bartels, Erik A. Ehli, Gareth E. Davies, James J. Hudziak, Social Science Genetic Association Consortium, Cornelius A. Rietveld, Maria M. Groen-Blokhuis, Jouke Jan Hottenga, Eco J.C. de Geus, and Dorret I. Boomsm

Multi-trait analysis of genome-wide association summary statistics using MTAG

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations

Multi-trait analysis of genome-wide association summary statistics using MTAG.

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations

Polygenic risk scores for cigarettes smoked per day do not generalize to a Native American population

BACKGROUND: Recent studies have demonstrated the utility of polygenic risk scores (PRSs) for exploring the genetic etiology of psychiatric phenotypes and the genetic correlations between them. To date, these studies have been conducted almost exclusively using participants of European ancestry, and thus, there is a need for similar studies conducted in other ancestral populations. However, given that the predictive ability of PRSs are sensitive to differences in linkage disequilibrium (LD) patterns and minor allele frequencies across discovery and target samples, the applicability of PRSs developed in European ancestry samples to other ancestral populations has yet to be determined. Therefore, the current study derived PRSs for cigarettes per day (CPD) from predominantly European-ancestry samples and examined their ability to predict nicotine dependence (ND) in a Native American (NA) population sample. METHOD: Results from the Tobacco and Genetics Consortium’s meta-analysis of genome-wide association studies of CPD were used to compute PRSs in a NA community sample (N=288). These scores were then used to predict ND diagnostic status. RESULTS: The PRS was not significantly associated with liability for ND in the full sample. However, a significant interaction between PRS and percent NA ancestry was observed. Risk scores were positively associated with liability for ND at higher levels of European ancestry, but no association was observed at higher levels of NA ancestry. CONCLUSION: These findings illustrate how differences in patterns of LD across discovery and target samples can reduce the predictive ability of PRSs for complex traits

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.Medical Research Council (MC_UU_12015/1), Department of Health (via National Institute for Health Research (NIHR)) (NF-SI-0512-10135), MRC (MC_PC_13048), Department of Health (via National Institute for Health Research (NIHR)) (NF-SI-0617-10149