scholarly journals Psychotic-like experiences and polygenic liability in the ABCD Study

2020 ◽  
Author(s):  
Nicole R. Karcher ◽  
Sarah E Paul ◽  
Emma C Johnson ◽  
Alexander S Hatoum ◽  
David AA Baranger ◽  
...  
Keyword(s):  
Psychotic Disorders ◽  
Brain Volume ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Risk Indices ◽  
Study Session ◽  
Polygenic Scores ◽  
Mediational Models ◽  
Global Brain ◽  
Genomic Risk

Background: Psychotic-like experiences (PLEs) during childhood are harbingers for severe psychopathology, including psychotic disorders, and neurodevelopmental impairments in adolescence and adulthood. Methods: Data from children of genomically-confirmed European ancestries (n=4,650; ages 9-10; 46.8% female) who completed the baseline Adolescent Brain Cognitive Development Study session were used to assess whether PLEs (i.e., both total and the presence of significantly distressing) are associated with polygenic scores (PGS) related to psychopathology (i.e., schizophrenia [SCZ], educational attainment [EDU], psychiatric cross-disorder risk [CROSS], PLEs). We also assessed whether variability in global and region indices of brain structure (i.e., volume, cortical thickness, surface area) as well as behaviors proximal to PGS (e.g., cognition for EDU) indirectly linked PGS to PLEs using mediational models. Findings: EDU and CROSS PGS were associated with total and significantly distressing PLEs (all delta R2s=0.202-0.660%; ps<0.002). Significantly distressing PLEs were also associated with higher SCZ and PLEs PGS (both delta R2=0.120-0.171%; ps<0.02). Global brain volume metrics and cognition indirectly linked EDU PGS to PLEs (proportion mediated: 3.33-32.22%). Interpretation: Total and distressing PLEs were associated with genomic risk indices associated with broad spectrum psychopathology risk (i.e., EDU and CROSS PGS). Significantly distressing PLEs were associated with genomic risk for psychosis (i.e., SCZ, PLEs). Global brain volume metrics and PGS-proximal behaviors represent promising putative intermediary phenotypes that may contribute to genomic risk for psychopathology. Broadly, polygenic scores derived from genome-wide association studies of adult samples can generalize to indices of psychopathology risk among children and aid the identification of putative neural and behavioral intermediaries of risk.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data

Psych ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Random Noise ◽  
Genome Wide Association Studies ◽  
Socioeconomic Variables ◽  
1000 Genomes ◽  
Polygenic Scores ◽  
Polygenic Selection

Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.

scholarly journals Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

2019 ◽  
Vol 28 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Daniel W. Belsky ◽  
K. Paige Harden
Keyword(s):  
Educational Attainment ◽  
Association Studies ◽  
Science Research ◽  
Well Being ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Research Activities ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Sociology, Genetics, and the Coming of Age of Sociogenomics

2020 ◽  
Vol 46 (1) ◽  
pp. 553-581 ◽  
Author(s):  
Melinda C. Mills ◽  
Felix C. Tropf
Keyword(s):  
Coming Of Age ◽  
Association Studies ◽  
Molecular Genetic ◽  
Genetic Correlations ◽  
Genetic Research ◽  
Well Being ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Molecular Genetic Data ◽  
Polygenic Scores

Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.

scholarly journals One size does not fit all. Genomics differentiates among binge-eating disorder, bulimia nervosa, and anorexia nervosa

2020 ◽  
Author(s):  
Christopher Hübel ◽  
Mohamed Abdulkadir ◽  
Moritz Herle ◽  
Ruth J.F. Loos ◽  
Gerome Breen ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Anorexia Nervosa ◽  
Eating Disorder ◽  
Bulimia Nervosa ◽  
Binge Eating ◽  
Binge Eating Disorder ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores

AbstractObjectiveGenome-wide association studies have identified multiple genomic regions associated with anorexia nervosa. Relatively few or no genome-wide studies of other eating disorders, such as bulimia nervosa and binge-eating disorder, have been performed, despite their substantial heritability. Exploratively, we aimed to identify traits that are genetically associated with binge-type eating disorders.MethodWe calculated genome-wide polygenic scores for 269 trait and disease outcomes using PRSice v2.2 and their association with anorexia nervosa, bulimia nervosa, and binge-eating disorder in up to 640 cases and 17,050 controls from the UK Biobank. Significant associations were tested for replication in the Avon Longitudinal Study of Parents and Children (up to 217 cases and 3018 controls).ResultsIndividuals with binge-type eating disorders had higher polygenic scores than controls for other psychiatric disorders, including depression, schizophrenia, and attention deficit hyperactivity disorder, and higher polygenic scores for body mass index.DiscussionOur findings replicate some of the known comorbidities of eating disorders on a genomic level and motivate a deeper investigation of shared and unique genomic factors across the three primary eating disorders.

scholarly journals Demographic history impacts stratification in polygenic scores

2020 ◽  
Author(s):  
Arslan A. Zaidi ◽  
Iain Mathieson
Keyword(s):  
Environmental Effects ◽  
Principal Components ◽  
Complex Traits ◽  
Population Stratification ◽  
Association Studies ◽  
Demographic History ◽  
Hybrid Approach ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Polygenic Scores

AbstractLarge genome-wide association studies (GWAS) have identified many loci exhibiting small but statistically significant associations with complex traits and disease risk. However, control of population stratification continues to be a limiting factor, particularly when calculating polygenic scores where subtle biases can cumulatively lead to large errors. We simulated GWAS under realistic models of demographic history to study the effect of residual stratification in large GWAS. We show that when population structure is recent, it cannot be fully corrected using principal components based on common variants—the standard approach—because common variants are uninformative about recent demographic history. Consequently, polygenic scores calculated from such GWAS results are biased in that they recapitulate non-genetic environmental structure. Principal components calculated from rare variants or identity-by-descent segments largely correct for this structure if environmental effects are smooth. However, even these corrections are not effective for local or batch effects. While sibling-based association tests are immune to stratification, the hybrid approach of ascertaining variants in a standard GWAS and then re-estimating effect sizes in siblings reduces but does not eliminate bias. Finally, we show that rare variant burden tests are relatively robust to stratification. Our results demonstrate that the effect of population stratification on GWAS and polygenic scores depends not only on the frequencies of tested variants and the distribution of environmental effects but also on the demographic history of the population.

scholarly journals Learning polygenic scores for human blood cell traits

2020 ◽  
Author(s):  
Yu Xu ◽  
Dragana Vuckovic ◽  
Scott C Ritchie ◽  
Parsa Akbari ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Cell ◽  
Association Studies ◽  
Relative Effectiveness ◽  
Univariate Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Learning Methods ◽  
Polygenic Scores ◽  
Using Data

AbstractPolygenic scores (PGSs) for blood cell traits can be constructed using summary statistics from genome-wide association studies. As the selection of variants and the modelling of their interactions in PGSs may be limited by univariate analysis, therefore, such a conventional method may yield sub-optional performance. This study evaluated the relative effectiveness of four machine learning and deep learning methods, as well as a univariate method, in the construction of PGSs for 26 blood cell traits, using data from UK Biobank (n=~400,000) and INTERVAL (n=~40,000). Our results showed that learning methods can improve PGSs construction for nearly every blood cell trait considered, with this superiority explained by the ability of machine learning methods to capture interactions among variants. This study also demonstrated that populations can be well stratified by the PGSs of these blood cell traits, even for traits that exhibit large differences between ages and sexes, suggesting potential for disease prevention. As our study found genetic correlations between the PGSs for blood cell traits and PGSs for several common human diseases (recapitulating well-known associations between the blood cell traits themselves and certain diseases), it suggests that blood cell traits may be indicators or/and mediators for a variety of common disorders via shared genetic variants and functional pathways.

scholarly journals Demographic history mediates the effect of stratification on polygenic scores

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Arslan A Zaidi ◽  
Iain Mathieson
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Rare Variants ◽  
Association Studies ◽  
Demographic History ◽  
Hybrid Approach ◽  
Genome Wide Association Studies ◽  
Environmental Structure ◽  
Polygenic Scores ◽  
Family Based

Population stratification continues to bias the results of genome-wide association studies (GWAS). When these results are used to construct polygenic scores, even subtle biases can cumulatively lead to large errors. To study the effect of residual stratification, we simulated GWAS under realistic models of demographic history. We show that when population structure is recent, it cannot be corrected using principal components of common variants because they are uninformative about recent history. Consequently, polygenic scores are biased in that they recapitulate environmental structure. Principal components calculated from rare variants or identity-by-descent segments can correct this stratification for some types of environmental effects. While family-based studies are immune to stratification, the hybrid approach of ascertaining variants in GWAS but reestimating effect sizes in siblings reduces but does not eliminate stratification. We show that the effect of population stratification depends not only on allele frequencies and environmental structure but also on demographic history.

scholarly journals Genomic prediction of cognitive traits in childhood and adolescence

2018 ◽  
Author(s):  
A.G. Allegrini ◽  
S. Selzam ◽  
K. Rimfeld ◽  
S. von Stumm ◽  
J.B. Pingault ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Complex Traits ◽  
Predictive Power ◽  
Prediction Models ◽  
Educational Achievement ◽  
Association Studies ◽  
Occupational Status ◽  
Childhood And Adolescence ◽  
Genome Wide Association Studies ◽  
Polygenic Scores

AbstractRecent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

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