Gamete simulation improves polygenic transmission disequilibrium analysis

AbstractPolygenic risk scores (PRS) derived from summary statistics of genome-wide association studies (GWAS) have enjoyed great popularity in human genetics research. Applied to population cohorts, PRS can effectively stratify individuals by risk group and has promising applications in early diagnosis and clinical intervention. However, our understanding of within-family polygenic risk is incomplete, in part because the small samples per family significantly limits power. Here, to address this challenge, we introduce ORIGAMI, a computational framework that uses parental genotype data to simulate offspring genomes. ORIGAMI uses state-of-the-art genetic maps to simulate realistic recombination events on phased parental genomes and allows quantifying the prospective PRS variability within each family. We quantify and showcase the substantially reduced yet highly heterogeneous PRS variation within families for numerous complex traits. Further, we incorporate within-family PRS variability to improve polygenic transmission disequilibrium test (pTDT). Through simulations, we demonstrate that modeling within-family risk substantially improves the statistical power of pTDT. Applied to 7,805 trios of autism spectrum disorder (ASD) probands and healthy parents, we successfully replicated previously reported over-transmission of ASD, educational attainment, and schizophrenia risk, and identified multiple novel traits with significant transmission disequilibrium. These results provided novel etiologic insights into the shared genetic basis of various complex traits and ASD.

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PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics

Genome Biology ◽

10.1186/s13059-021-02479-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zijie Zhao ◽

Yanyao Yi ◽

Jie Song ◽

Yuchang Wu ◽

Xiaoyuan Zhong ◽

...

Keyword(s):

Statistical Power ◽

Human Genetics ◽

Association Studies ◽

Fine Tuning ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Polygenic Risk ◽

Individual Level ◽

Genetics Research

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

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Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits

Nature Communications ◽

10.1038/s41467-021-23130-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Bingxin Zhao ◽

Yue Shan ◽

Yue Yang ◽

Zhaolong Yu ◽

Tengfei Li ◽

...

Keyword(s):

Association Analysis ◽

Complex Traits ◽

Association Studies ◽

Brain Structures ◽

Imaging Genetics ◽

Risk Scores ◽

Phenotypic Variance ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Wide Range

AbstractStructural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10−8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10−31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.

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ExPRSweb - An Online Repository with Polygenic Risk Scores for Common Health-related Exposures

10.1101/2022.01.13.22269176 ◽

2022 ◽

Author(s):

Ying Ma ◽

Snehal Patil ◽

Xiang Zhou ◽

Bhramar Mukherjee ◽

Lars G. Fritsche

Keyword(s):

Complex Traits ◽

Chronic Conditions ◽

Prediction Models ◽

Association Studies ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Genetic Modifiers ◽

Polygenic Risk ◽

Health Related ◽

The Uk

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Since measurements are often unfeasible, Exposure Polygenic Risk Scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks, the Michigan Genomics Initiative and the UK Biobank. We established ExPRS for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially, the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

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Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa289 ◽

2020 ◽

Author(s):

Niccolo’ Tesi ◽

Sven J van der Lee ◽

Marc Hulsman ◽

Iris E Jansen ◽

Najada Stringa ◽

...

Keyword(s):

Older Adults ◽

Cellular Response ◽

Molecular Mechanisms ◽

Association Studies ◽

Risk Scores ◽

Human Longevity ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Age Related

Abstract Studying the genome of centenarians may give insights into the molecular mechanisms underlying extreme human longevity and the escape of age-related diseases. Here, we set out to construct polygenic risk scores (PRSs) for longevity and to investigate the functions of longevity-associated variants. Using a cohort of centenarians with maintained cognitive health (N = 343), a population-matched cohort of older adults from 5 cohorts (N = 2905), and summary statistics data from genome-wide association studies on parental longevity, we constructed a PRS including 330 variants that significantly discriminated between centenarians and older adults. This PRS was also associated with longer survival in an independent sample of younger individuals (p = .02), leading up to a 4-year difference in survival based on common genetic factors only. We show that this PRS was, in part, able to compensate for the deleterious effect of the APOE-ε4 allele. Using an integrative framework, we annotated the 330 variants included in this PRS by the genes they associate with. We find that they are enriched with genes associated with cellular differentiation, developmental processes, and cellular response to stress. Together, our results indicate that an extended human life span is, in part, the result of a constellation of variants each exerting small advantageous effects on aging-related biological mechanisms that maintain overall health and decrease the risk of age-related diseases.

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An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

10.1101/467910 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tom G. Richardson ◽

Sean Harrison ◽

Gibran Hemani ◽

George Davey Smith

Keyword(s):

Web Application ◽

Large Scale ◽

Complex Disease ◽

Association Studies ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Genetic Liability ◽

Polygenic Risk ◽

The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

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Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

10.1101/810713 ◽

2019 ◽

Cited By ~ 4

Author(s):

Zijie Zhao ◽

Yanyao Yi ◽

Yuchang Wu ◽

Xiaoyuan Zhong ◽

Yupei Lin ◽

...

Keyword(s):

Association Studies ◽

Fine Tuning ◽

Risk Scores ◽

Training Dataset ◽

Validation Dataset ◽

P Value ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Polygenic Risk ◽

Model Tuning

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require individual-level genetic data as the training dataset or as a validation dataset independent from both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS (Parameter-tuning Using Marginal Association Statistics), a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. On average, PUMAS improves the predictive R2 by 205.6% and 62.5% compared to PRSs with arbitrary p-value cutoffs of 0.01 and 1, respectively. Applied to 211 neuroimaging traits and Alzheimer’s disease, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

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Age-related late-onset disease heritability patterns and implications for genome-wide association studies

10.1101/349019 ◽

2018 ◽

Cited By ~ 1

Author(s):

Roman Teo Oliynyk

Keyword(s):

Old Age ◽

Cumulative Incidence ◽

Late Onset ◽

Association Studies ◽

Genome Wide Association ◽

Risk Scores ◽

Cerebral Stroke ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Genome Wide

AbstractBackgroundGenome-wide association studies and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem.MethodsComputer simulations of polygenic late-onset diseases in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes.ResultsThe incidence rate for late-onset diseases grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for genome-wide association studies overrepresent older individuals with lower polygenic risk scores, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and genome-wide association studies. It also explains the relatively constant-with-age heritability found for late-onset diseases of lower prevalence, exemplified by cancers.ConclusionsFor late-onset polygenic diseases showing high cumulative incidence together with high initial heritability, rather than using relatively old age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

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Insights into the genomics of affective disorders

Medizinische Genetik ◽

10.1515/medgen-2020-2003 ◽

2020 ◽

Vol 32 (1) ◽

pp. 9-18

Author(s):

Andreas J. Forstner ◽

Per Hoffmann ◽

Markus M. Nöthen ◽

Sven Cichon

Keyword(s):

Bipolar Disorder ◽

Major Depression ◽

Affective Disorders ◽

Association Studies ◽

Phenotypic Variability ◽

Genetic Research ◽

Autism Spectrum ◽

Risk Scores ◽

Depression Symptoms ◽

Genome Wide Association Studies

Abstract Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression. Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

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Using Genetics to Examine a General Liability to Childhood Psychopathology

Behavior Genetics ◽

10.1007/s10519-019-09985-4 ◽

2019 ◽

Vol 50 (4) ◽

pp. 213-220 ◽

Cited By ~ 7

Author(s):

Lucy Riglin ◽

Ajay K. Thapar ◽

Beate Leppert ◽

Joanna Martin ◽

Alexander Richards ◽

...

Keyword(s):

Association Studies ◽

Full Range ◽

Population Based ◽

Autism Spectrum ◽

Developmental Changes ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Childhood Psychopathology ◽

Specific Factors ◽

P Factor

AbstractPsychiatric disorders show phenotypic as well as genetic overlaps. There are however also marked developmental changes throughout childhood. We investigated the extent to which, for a full range of early childhood psychopathology, a general “p” factor was explained by genetic liability, as indexed by multiple different psychiatric polygenic risk scores (PRS) and whether these relationships altered with age. The sample was a UK, prospective, population-based cohort with psychopathology data at age 7 (N = 8161) and age 13 (N = 7017). PRS were generated from large published genome-wide association studies. At both ages, we found evidence for a childhood “p” factor as well as for specific factors. Schizophrenia and attention-deficit/hyperactivity disorder (ADHD) PRS were associated with this general “p” factor at both ages but depression and autism spectrum disorder (ASD) PRS were not. We also found some evidence of associations between schizophrenia, ADHD and depression PRS with specific factors, but these were less robust and there was evidence for developmental changes.

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