scholarly journals Integrative multi-omics analysis of childhood aggressive behavior

2021 ◽  
Author(s):  
Fiona A. Hagenbeek ◽  
Jenny van Dongen ◽  
René Pool ◽  
Peter J Roetman ◽  
Amy C Harms ◽  
...  
Keyword(s):  
Aggressive Behavior ◽  
Complex Traits ◽  
Predictive Accuracy ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Validation Data ◽  
Clinical Cohort ◽  
Snp Data ◽  
Genome Wide ◽  
Polygenic Scores

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. Using multivariate statistical methods, we integrated 45 polygenic scores (PGSs) based on genome-wide SNP data, 78,772 CpGs, and 90 metabolites for 645 twins (cases=42.0%, controls=58.0%). The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics biomarker panel comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy in the test (N=277, cases=42.2%, controls=57.8%) and validation data (N=142 participants from a clinical cohort, cases=45.1%, controls=54.9%) ranged from 43.0% to 57.0% for the single- and multi-omics models. The average correlations across omics layers of omics traits selected for aggression in single-omics models ranged from 0.18 to 0.28. In the multi-omics model higher correlations were found and we describe five sets of correlational patterns with high absolute correlations (|r| ≥ 0.60) of aggression-related omics traits selected into the multi-omics model, providing novel biological insights.

scholarly journals The power of pathway-based polygenic risk scores

2021 ◽  
Author(s):  
Paul O’Reilly ◽  
Shing Choi ◽  
Judit Garcia-Gonzalez ◽  
Yunfeng Ruan ◽  
Hei Man Wu ◽  
...  
Keyword(s):  
Complex Disease ◽  
Predictive Accuracy ◽  
Risk Scores ◽  
Distinct Advantage ◽  
Genetic Profile ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
Individual Level ◽  
Genome Wide ◽  
Polygenic Scores

Abstract Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, all of these distil genetic liability to a single number based on aggregation of an individual’s genome-wide alleles. This results in a key loss of information about an individual’s genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. Here we evaluate the performance of pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual, and we introduce a software, PRSet, for computing and analysing pathway PRSs. We find that pathway PRSs have similar power for evaluating pathway enrichment of GWAS signal as the leading methods, with the distinct advantage of providing estimates of pathway genetic liability at the individual-level. Exemplifying their utility, we demonstrate that pathway PRSs can stratify diseases into subtypes in the UK Biobank with substantially greater power than genome-wide PRSs. Compared to genome-wide PRSs, we expect pathway-based PRSs to offer greater insights into the heterogeneity of complex disease and treatment response, generate more biologically tractable therapeutic targets, and provide a more powerful path to precision medicine.

scholarly journals Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Declan Bennett ◽  
Donal O’Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

AbstractOngoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

scholarly journals LDpred2: better, faster, stronger

2020 ◽  
Author(s):  
Florian Privé ◽  
Julyan Arbel ◽  
Bjarni J. Vilhjálmsson
Keyword(s):  
Human Genetics ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Real Data ◽  
R Package ◽  
Summary Statistics ◽  
Genetics Research ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Central Tool

AbstractPolygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance. Here we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a “sparse” option that can learn effects that are exactly 0, and an “auto” option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that, in contrast to what was recommended in the first version of this paper, we now recommend to run LDpred2 genome-wide instead of per chromosome. LDpred2 is implemented in R package bigsnpr.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Ronald de Vlaming ◽  
Patrick J. F. Groenen
Keyword(s):  
Quantitative Genetics ◽  
Ridge Regression ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Nonlinear Effects ◽  
Sample Sizes ◽  
Snp Data ◽  
Genome Wide

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 ofridge regressionfor prediction in quantitative genetics usingsingle-nucleotide polymorphismdata 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.,dominanceandepistasis). 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 classicalgenome-wide association studyapproach ofrepeated 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.

scholarly journals Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

2018 ◽  
Author(s):  
Urmo Võsa ◽  
Annique Claringbould ◽  
Harm-Jan Westra ◽  
Marc Jan Bonder ◽  
Patrick Deelen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Genome Wide ◽  
Polygenic Scores

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.To identify these effects, we performedcis-andtrans-expressionquantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.We observed thatcis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to usecis-eQTLs to pinpoint causal genes within susceptibility loci.In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

scholarly journals LDpred2: better, faster, stronger

2020 ◽  
Author(s):  
Florian Privé ◽  
Julyan Arbel ◽  
Bjarni J Vilhjálmsson
Keyword(s):  
Human Genetics ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Genetics Research ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Central Tool

Abstract Motivation Polygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance. Results Here, we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a ‘sparse’ option that can learn effects that are exactly 0, and an ‘auto’ option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that LDpred2 provides more accurate polygenic scores when run genome-wide, instead of per chromosome. Availability and implementation LDpred2 is implemented in R package bigsnpr. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

2021 ◽  
Author(s):  
Camiel M. van der Laan ◽  
José J. Morosoli-García ◽  
Steve G. A. van de Weijer ◽  
Lucía Colodro-Conde ◽  
Hill F. Ip ◽  
...  
Keyword(s):  
Individual Differences ◽  
The Netherlands ◽  
Life Course ◽  
Aggressive Behavior ◽  
Meta Analysis ◽  
Genetic Influences ◽  
Genome Wide ◽  
A Genome ◽  
Polygenic Scores ◽  
The Life Course

AbstractWe test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12–70 years, Australia: 16–73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a ‘rolling weights’ model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41–70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

scholarly journals Non-additive polygenic models improve predictions of fitness traits in three eukaryote model species

2020 ◽  
Author(s):  
Moises Exposito-Alonso ◽  
Peter Wilton ◽  
Rasmus Nielsen
Keyword(s):  
Genetic Variants ◽  
Statistical Models ◽  
Complex Traits ◽  
Disease Susceptibility ◽  
Living Organism ◽  
Wild Type ◽  
Model Species ◽  
Fitness Traits ◽  
Genome Wide ◽  
Polygenic Scores

ABSTRACTTo describe a living organism it is often said that “the whole is greater than the sum of its parts”. In genetics, we may also think that the effect of multiple mutations on an organism is greater than their additive individual effect, a phenomenon called epistasis or multiplicity. Despite the last decade’s discovery that many disease- and fitness-related traits are polygenic, or controlled by many genetic variants, it is still debated whether the effects of individual genes combine additively or not. Here we develop a flexible likelihood framework for genome-wide associations to fit complex traits such as fitness under both additive and non-additive polygenic architectures. Analyses of simulated datasets under different true additive, multiplicative, or other epistatic models, confirm that our method can identify global non-additive selection. Applying the model to experimental datasets of wild type lines of Arabidopsis thaliana, Drosophila melanogaster, and Saccharomyces cerevisiae, we find that fitness is often best explained with non-additive polygenic models. Instead, a multiplicative polygenic model appears to better explain fitness in some experimental environments. The statistical models presented here have the potential to improve prediction of phenotypes, such as disease susceptibility, over the standard methods for calculating polygenic scores which assume additivity.

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