Non-HLA Genetics and Application to Living Donor Candidates.

The First Baby Born After Polygenic Embryo Screening

Decision letter: Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study

Should preimplantation genetic testing for polygenic disease be offered to all – or none?

A Blueprint for Genetic Determinism

> It is not in the stars to hold our destiny but in ourselves > > —William Shakespeare The widespread availability and lower costs of genotyping and sequencing have resulted in the performance of a large number of genotype–phenotype association studies in cardiovascular medicine. The stringent requirements for correction for multiple testing and replication have particularly favored genotype–phenotype studies examining the association between genetic variation and quantitative traits that allows for greater statistical power. Multiple genome-wide association studies and a subsequent meta-analysis have identified >180 common and rare genetic variants associated with lipid traits.1,2 However, the effect size of these individual genetic variants is small, explaining only a small fraction of phenotypic variation prompting investigators to use genetic risk scores (GRS) that represent an aggregate of genetic risk to demonstrate clinical utility. Single-nucleotide polymorphisms (SNPs) and GRS have been used to predict cardiovascular disease such as coronary artery disease (CAD) and hypertension, surrogate markers of disease such as coronary calcium, cardiovascular outcomes such as myocardial infarction, and intermediate traits such as blood pressure and lipids.3 Article, see p 495 The Global Lipids Genetics Consortium has performed the largest genetic association study of lipid levels in 188 577 individuals from a total of 60 studies.1,2 There were 157 genetic loci that were identified, 95 were described previously and 62 were novel. The lipid level variance explained by the novel loci in this study ranged from 1.6% for high-density lipoprotein cholesterol (HDL-C) levels to 2.6% for total cholesterol levels. The total lipid variance explained by the previously described loci was 10% to 12%. The population studied was predominantly of European ancestry, and subjects on lipid-lowering therapy were excluded. The association of these genetic loci with lipid levels and a change in lipid levels with intervention in a prediabetic population …

Do Polygenic Scores Inform Psychiatric Disease Risk After Considering Family History?

Polygenic score: An anchor holding the whole life course.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

BackgroundThe popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease.MethodsThis study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature.ResultsThrough robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10–16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed (https://xistance.shinyapps.io/prs-ra/) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs.ConclusionsThese findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

Personalized, Genomic-Based Care Could Mean a Paradigm Shift

Polygenic risk scores (PRS), commonly referred to as genetic or genomic risk scores, aggregate the effects of multiple genetic variants into a single composite estimate of genetic risk. PRS scores are typically used to predict the risk of developing a disease or to explain the phenotypic variation, and are derived from the effect sizes observed in large-scale genome-wide association studies (GWAS). Unlike rare monogenic diseases such as cystic fibrosis, which are attributable to genetic variants in single genes, with large effects on disease status, common complex diseases such as type 2 diabetes mellitus (T2DM) are polygenic, with risk contributed by a panel of genetic variants present throughout the genome. The concept of integrating information from these multiple genetic variants into a single metric of genetic risk was initially proposed in the shape of genetic risk scores, which generally limited the score to include single-nucleotide polymorphisms (SNPs) that were common and reached genome-wide significance in the initial GWASs. In contrast, PRS incorporates information from a much larger set of genetic variants, typically hundreds of thousands, including SNPs below the threshold for genome-wide statistical significance, and often with much more modest effect sizes. Indeed, recent findings have pointed to how polygenic background could also increase the accuracy of risk estimation for individuals with monogenic risk variant in conditions such as familial hypercholesterolaemia, hereditary breast and ovarian cancer, and Lynch syndrome.

Association of asthma and its genetic predisposition with the risk of severe COVID-19

Polygenic Risk Scores in Schizophrenia: Ready for the Real World?

Prostate cancer (PCa) is a highly heritable disease with large disparities in incidence rates across racial and ethnic populations. The inadequate representation of diverse populations in current genome-wide association studies (GWAS) limits the translational potential of findings to the world's populations and could result in biased risk prediction, further exacerbating health disparities. To improve our understanding of genetic risk of PCa, we conducted the largest multiethnic PCa GWAS meta-analysis to date with 107,247 cases and 127,006 controls from the Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome (PRACTICAL) consortium, including 85,554 cases and 91,972 controls of European ancestry, 10,368 cases and 10,986 controls of African ancestry, 8,611 cases and 18,809 controls of Asian ancestry, and 2,714 cases and 5,239 controls of Hispanic ethnicity. We identified 269 genetic risk variants independently associated with PCa risk, 86 of which were novel. Of the 183 previously reported prostate cancer risk variants, we identified stronger markers of risk for 62 variants using fine-mapping. To understand the aggregate effect of the 269 variants, we constructed a genetic risk score (GRS) using the multiethnic weights of the risk variants. Compared to men in the average 40-60% GRS category, the estimated OR for men in the top GRS decile (90-100%) was 5.06 [95% CI 4.84-5.29] for men of European ancestry, 3.74 [95% CI 3.36-4.17] for men of African ancestry, 4.47 [95% CI 3.52-5.68] for men of Asian ancestry, and 4.15 [95% CI 3.33-5.17] for Hispanic men. Men of African ancestry were estimated to have a 2.18-times higher mean GRS [95% CI 2.14-2.22], and men of Asian ancestry 0.73-times lower [95% CI 0.71-0.76], than men of European ancestry. Age significantly modified the GRS association with PCa risk, such that in men of European ancestry, the top decile GRS category was associated with an OR of 6.71 [95 % CI 5.99-7.52] for men ages 55 years or younger and 4.39 [95% CI 4.19-4.60] for men older than 55 years. Similarly, in men of African ancestry, the top GRS decile category was associated with an OR of 4.70 [95 % CI 3.65-6.07] for men ages 55 years or younger and 3.37 [95% CI 2.99-3.80] for men older than 55 years. We found that 51% of aggressive cases of European ancestry and 45% of aggressive cases of African ancestry were within the top 20% of the GRS. The lifetime absolute risk of PCa for men in the top decile of the GRS reached 38% for both African Americans [95% CI 36-41%] and Whites [95% CI 37-39%], 31% [95% CI 27-36%] for Hispanics and 26% [95% CI 22-30%] for Asians. For comparison, we constructed a genome-wide GRS, including the 269 variants and variants associated with PCa with P&amp;lt;1.0 × 10−5. ORs calculated with genome-wide GRS were similar and had nearly identical discriminative ability as the 269 GRS in independent samples of 6,852 cases and 193,117 controls of European ancestry and 1,586 cases and 1,047 controls of African ancestry. This suggests that a genome-wide GRS may not have improved ability to predict PCa risk compared to the 269 GRS. To understand the biological mechanisms impacted by genetic risk of PCa, we conducted integrative analyses of the GRS and serum metabolomics in 611 African ancestry men from the Multiethnic Cohort. We found that a higher GRS was associated with lower sphingolipid levels (beta=-0.08, P=3.1 × 10−4), lower androgenic steroids (beta=-0.06, P=0.003), and higher sarcosine levels (beta=0.11, P=0.002). Further, using a latent clustering approach, we found that these metabolites in conjunction with the GRS contributed to the clustering of men with low-risk, intermediate-risk, and high-risk of PCa. These findings support the role of germline variation contributing to racial and ethnic disparities in PCa risk, with the GRS offering an approach for personalized risk prediction across populations and informing the biological mechanisms underlying PCa risk. Citation Format: Burcu F. Darst, Lilit Moss, Edward J. Saunders, Nicholas Mancuso, Xin Sheng, Alisha Chou, Tokhir Dadaev, Sonja I. Berndt, Stephen K. Van Den Eeden, Stephen J. Chanock, Michael B. Cook, Tracy M. Layne, Demetrius Albanes, Hidewaki Nakagawa, John S. Witte, Practical Consortium, Rosalind A. Eeles, Zsofia Kote-Jarai, David V. Conti, Christopher A. Haiman. Multiethnic prostate cancer GWAS meta-analysis identifies novel variants, improves genetic risk prediction across populations, and informs biological mechanisms of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr NG03.

Challenges With Colorectal Cancer Family History Assessment—Motivation to Translate Polygenic Risk Scores Into Practice