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Genes account for a significant proportion of the risk for most common diseases. The genome-wide association scan (GWAS) era of genetic epidemiology has generated a massive amount of data, revolutionized our thinking on the geneti...
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Genes account for a significant proportion of the risk for most common diseases. The genome-wide association scan (GWAS) era of genetic epidemiology has generated a massive amount of data, revolutionized our thinking on the genetic architecture of common diseases and positioned the field to realistically consider risk prediction for common polygenic diseases, such as non-familial cancers, and autoimmune, cardiovascular, and psychiatric diseases. Polygenic scoring is an approach that shows promise for understanding the polygenic contribution to common human diseases. This is an approach typically relying on genome-wide SNP data, where a set of SNPs identified in a discovery GWAS are used to construct composite polygenic scores. These scores are then used in additional samples for association testing or risk prediction. This review summarizes the extant literature on the use, power, and accuracy of polygenic scores in studies of the etiology of disease and the promise for disease risk prediction.
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Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and intera...
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Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
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Genome-wide association studies (GWAS) have revealed that many traits are highly polygenic, in that their within-population variance is governed, in part, by small-effect variants at many genetic loci. Standard population-genetic ...
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Genome-wide association studies (GWAS) have revealed that many traits are highly polygenic, in that their within-population variance is governed, in part, by small-effect variants at many genetic loci. Standard population-genetic methods for inferring evolutionary history are ill-suited for polygenic traits: when there are many variants of small effect, signatures of natural selection are spread across the genome and are subtle at any one locus. In the last several years, various methods have emerged for detecting the action of natural selection on polygenic scores, sums of genotypes weighted by GWAS effect sizes. However, most existing methods do not reveal the timing or strength of selection. Here, we present a set of methods for estimating the historical time course of a population-mean polygenic score using local coalescent trees at GWAS loci. These time courses are estimated by using coalescent theory to relate the branch lengths of trees to allele-frequency change. The resulting time course can be tested for evidence of natural selection. We present theory and simulations supporting our procedures, as well as estimated time courses of polygenic scores for human height. Because of its grounding in coalescent theory, the framework presented here can be extended to a variety of demographic scenarios, and its usefulness will increase as both GWAS and ancestral-recombination-graph inference continue to progress.
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Introduction: Since 2005, disease-related human genetic diversity has been intensively characterized using genome-wide association studies (GWAS). Understanding how and by whom this work was performed may yield valuable insights i...
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Introduction: Since 2005, disease-related human genetic diversity has been intensively characterized using genome-wide association studies (GWAS). Understanding how and by whom this work was performed may yield valuable insights into the generalizability of GWAS discoveries to global populations and how high-impact genetics research can be equitably sustained in the future. Materials and Methods: We mined the NHGRI-EBI GWAS Catalog (2005-2022) for the most burdensome non-communicable causes of death worldwide. We then compared (i) the geographic, ethnic and socioeconomic characteristics of study populations; (ii) the geographic and socioeconomic characteristics of the regions within which researchers were located and (iii) the extent to which male and female investigators undertook and led the research. Results: The research institutions leading the work are often US-based (37%), while the origin of samples is more diverse, with the Nordic countries having contributed as much data to GWAS as the United States (similar to 17% of data). The majority of first (60%), senior (75%) and all (66%) authors are male; although proportions vary by disease and leadership level, male co-authors are the ubiquitous majority. The vast majority (91%) of complex trait GWAS has been performed in European ancestry populations, with cohorts and scientists predominantly located in medium-to-high socioeconomically ranked countries; apart from East Asians (similar to 5%), other ethnicities rarely feature in published GWAS. See: https://hugofitipaldi.shinyapps.io/gwas_results/ to browse all results. Conclusion: Most GWAS cohorts are of European ancestry residing outside the United States, with a smaller yet meaningful proportion of East Asian ancestry. Papers describing GWAS research are predominantly authored by male scientists based in medium-to-high income countries. [GRAPHICS] .
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We estimate the maximum prediction accuracy for the risk of Alzheimer's disease based on disease prevalence and heritability of liability. We demonstrate that the recently reported AUC values for predicting of Alzheimer's disease ...
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We estimate the maximum prediction accuracy for the risk of Alzheimer's disease based on disease prevalence and heritability of liability. We demonstrate that the recently reported AUC values for predicting of Alzheimer's disease using polygenic scores reach about 90% of the estimated maximum accuracy that can be achieved by predictors of genetic risk based on genomic profiles. (C) 2016 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
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Background A central nosological problem concerns the etiological relationship of emotional dysregulation with ADHD . Molecular genetic risk scores provide a novel method for informing this question. Methods Participants were 514 ...
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Background A central nosological problem concerns the etiological relationship of emotional dysregulation with ADHD . Molecular genetic risk scores provide a novel method for informing this question. Methods Participants were 514 community‐recruited children of Northern European descent age 7‐11 defined as ADHD or non‐ ADHD by detailed research evaluation. Parents‐rated ADHD on standardized ratings and child temperament on the Temperament in Middle Childhood Questionnaire ( TMCQ ) and reported on ADHD and comorbid disorders by semi‐structured clinical interview. Categorical and dimensional variables were created for ADHD , emotional dysregulation (implicating disruption of regulation of both anger‐irritability and of positive valence surgency‐sensation seeking), and irritability alone (anger dysregulation). Genome‐wide polygenic risk scores ( PRS ) were computed for ADHD and depression genetic liability. Structural equation models and computationally derived emotion profiles guided analysis. Results The ADHD PRS was associated in variable‐centered analyses with irritability (β?=?.179, 95% CI?=?0.087–0.280; Δ R 2 ?=?.034, p? <?.0002), but also with surgency/sensation seeking (B?=?.146, 95% CI ?=?0.052–0.240, Δ R 2 =.022, p? =?.002). In person‐centered analysis, the ADHD PRS was elevated in the emotion dysregulation ADHD group versus other ADHD children ( OR ?=?1.44, 95% CI ?=?1.03–2.20, Nagelkerke Δ R 2 ?=?.013, p? =?.033) but did not differentiate irritable from surgent ADHD profiles. All effects were independent of variation in ADHD severity across traits or groups. The depression PRS was related to oppositional defiant disorder but not to ADHD emotion dysregulation. Conclusions Irritability‐anger and surgency‐sensation seeking, as forms of negative and positively valenced dysregulated affect in ADHD populations, both relate principally to ADHD genetic risk and not mood‐related genetic risk.
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The present study aimed to identify whether polygenic scores (PGSs) for education, health and psychological factors are related to subjective age in a large sample of older adults. Participants were 7,763 individuals of European a...
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The present study aimed to identify whether polygenic scores (PGSs) for education, health and psychological factors are related to subjective age in a large sample of older adults. Participants were 7,763 individuals of European ancestry (57% women, Mean age = 69.15, SD = 10.18) from the Health and Retirement Study who were genotyped and provided subjective age data. Higher PGSs for educational achievement and well-being were related to a younger subjective age, whereas higher PGSs for neuroticism, body mass index, waist circumference, and depressive symptoms were associated with an older subjective age. This study provides new evidence on the potential genetic underpinnings of subjective age.
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