Performance evaluation of four type-specific commercial assays for detection of herpes simplex virus type 1 antibodies in a Middle East and North Africa population.

The number of diagnostic assays for the detection of herpes simplex virus type 1 (HSV-1) antibodies has increased over the years. However, their performance characteristics could vary among global populations. To investigate performance of two commercial ELISA kits, HerpeSelect1 ELISA and Euroimmun Anti-HSV-1 (gC1) ELISA (IgG); and two commercial immunoblot (IB)/Western blot (WB) assays, HerpeSelect1 and 2 Immunoblot IgG, and Euroimmun Anti-HSV-1/HSV-2 gG2 Euroline-WB (IgG/IgM); in detecting HSV-1 antibodies in a Middle East and North Africa (MENA) population. Blood specimens were collected from blood donors in Doha, Qatar, June 2013-2016. Twenty specimens were randomly selected from 10 MENA nationalities (Egypt, Iran, Jordan, Lebanon, Pakistan, Palestine, Qatar, Sudan, Syria, and Yemen; total = 200), and tested for HSV-1 antibodies. Across all six comparisons between assays, positive percent agreement ranged between 95.7% (95% CI: 91.4-98.3%) and 100.0% (95% CI: 97.8-100.0%). Negative percent agreement ranged between 86.2% (95% CI: 68.3-96.1%) and 96.2% (95% CI: 80.4-99.9%). Overall percent agreement ranged between 95.7% (95% CI: 91.7-97.8%) and 99.4% (95% CI: 96.7-99.9%). Cohen's kappa statistic ranged between 0.84 (95% CI: 0.73-0.95) and 0.98 (95% CI: 0.93-1.00). Compared against IB/WB, HerpeSelectand Euroimmun had sensitivities and specificities >96% and >86%, respectively. Positive and negative predictive values were >97% and >83%, respectively. The assays showed excellent concordance with one another, and with a high kappa statistic. The ELISA kits demonstrated robust diagnostic performance compared to the IB/WB assays. These findings support the assays' utility in clinical diagnosis and research in MENA populations

Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank

peer reviewedPolygenic risk score (PRS) predictions often show bias toward the population of available genome-wide association studies (GWASs), which is typically of European ancestry. This study aimed to assess the performance differences of ancestry-specific PRS and test the implementation of multi-ancestry PRS to enhance the generalizability of low-density lipoprotein (LDL) cholesterol predictions in the East Asian (EAS) population. In this study, we computed ancestry-specific and multi-ancestry PRSs for LDL using data obtained from the Global Lipid Genetics Consortium, while accounting for population-specific linkage disequilibrium patterns using the PRS-CSx method in the United Kingdom Biobank dataset (UKB, n = 423,596) and Taiwan Biobank dataset (TWB, n = 68,978). Population-specific PRSs were able to predict LDL levels better within the target population, whereas multi-ancestry PRSs were more generalizable. In the TWB dataset, covariate-adjusted R2 values were 9.3% for ancestry-specific PRS, 6.7% for multi-ancestry PRS, and 4.5% for European-specific PRS. Similar trends (8.6%, 7.8%, and 6.2%) were observed in the smaller EAS population of the UKB (n = 1,480). Consistent with R2 values, PRS stratification in EAS regions (TWB) effectively captured a heterogenous variability in LDL blood cholesterol levels across PRS strata. The mean difference in LDL levels between the lowest and highest EAS-specific PRS (EAS_PRS) deciles was 0.82, compared to 0.59 for European-specific PRS (EUR_PRS) and 0.76 for multi-ancestry PRS. Notably, the mean LDL values in the top decile of multi-ancestry PRS were comparable to those of EAS_PRS (3.543 vs. 3.541, p = 0.86). Our analysis of the PRS prediction model for LDL cholesterol further supports the issue of PRS generalizability across populations. Our targeted analysis of the EAS population revealed that integrating non-European genotyping data with a powerful European-based GWAS can enhance the generalizability of LDL PRS.3. Good health and well-bein

pharmacome/knowledge v0.0.2

Knowledge curated for the Human Brain Pharmacome projec

Assessing the role of polygenic background on the penetrance of monogenic forms in Parkinson\textquoterights disease. 2021.06.06.21253270

Background: Several rare and common variants are associated with Parkinson's disease. However, there is still an incomplete penetrance in the carriers of rare variants associated with Parkinson's disease. To address this issue, we investigated whether a PRS calculated from significant GWAS SNPs affects the penetrance of Parkinson's disease among carriers of rare monogenic variants in known Parkinson's disease genes and those with a family history. Methods: We calculated the PRS based on common variants and selected the carriers of rare monogenic variants by using the exome data from UK Biobank. Individuals were divided into three risk categories based on PRS: low (90%) risk groups. We then compared how PRS affects Parkinson\textquoterights disease risk among carriers of rare monogenic variants and those with family-history. Results: We observed a two-fold higher odds ratio for a carrier of a monogenic variant that had a high PRS (OR 4.07,95\% CI, 1.72-8.08) compared to carriers with a low PRS (OR 1.91, 95\% CI, 0.31-6.05). In the same line, carriers with a first-degree family history and with \>90\% PRS have even a higher risk of developing PD (OR 23.53, 95\%CI 5.39-71.54) compared to those with \<90\% PRS (OR 9.54, 95\% CI 3.32-21.65). Conclusions: Our results show that PRS, carrier status, and family history contribute independently and additively to the Parkinson's disease risk

Gene-based burden scores identify rare variant associations for 28 blood biomarkers

Abstract Background A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. Methods We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). Results Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. Conclusion This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level

Is happiness always a personal(ity) thing? A quasi-replication and extension of previous well-being studies.

Neuroticism, extraversion, and conscientiousness are the most notable personality predictors of subjective well-being (SWB) in phenotypic and behavior genetic studies. We aim to quasi-replicate and extend previous findings. Using data from the German twin family panel TwinLife, we analyzed Big Five personality traits and life satisfaction of 3,080 twin pairs from three birth cohorts (born 2003/04, 1997/98, and 1990–93). Prior research has repeatedly provided evidence that genetic variance in personality largely explained genetic variance in SWB. Our results replicated this pattern with no significant unique genetic variance in life satisfaction. However, we found significant age cohort differences (e.g., only in ages 17–23 did genetic variance in openness partly explain genetic variance in life satisfaction). Furthermore, we tested personality–SWB associations using polygenic scores (PGSs) for Big Five personality traits and SWB. PGSs were calculated for N = 5,355 genotyped twin family members from the TwinSNPs study (age range 8–77). PGSs showed genetic association patterns similar to the twin study results, with neuroticism as the most robust predictor ( = -.130) and openness showing near-null associations. Despite low overall explanatory power, phenotype prediction mirrored this as well (e.g., neuroticism PGSs predicted life satisfaction phenotypes with = -.057)

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Abstract Male-pattern hair loss (MPHL) is common and highly heritable. While genome-wide association studies (GWAS) have generated insights into the contribution of common variants to MPHL etiology, the relevance of rare variants remains unclear. To determine the contribution of rare variants to MPHL etiology, we perform gene-based and single-variant analyses in exome-sequencing data from 72,469 male UK Biobank participants. While our population-level risk prediction suggests that rare variants make only a minor contribution to general MPHL risk, our rare variant collapsing tests identified a total of five significant gene associations. These findings provide additional evidence for previously implicated genes (EDA2R, WNT10A) and highlight novel risk genes at and beyond GWAS loci (HEPH, CEPT1, EIF3F). Furthermore, MPHL-associated genes are enriched for genes considered causal for monogenic trichoses. Together, our findings broaden the MPHL-associated allelic spectrum and provide insights into MPHL pathobiology and a shared basis with monogenic hair loss disorders

Breast and prostate cancer risk: The interplay of polygenic risk, rare pathogenic germline variants, and family history

Purpose Investigate to which extent polygenic risk scores (PRS), pathogenic or likely rare pathogenic germline variants (PV), and family history jointly influence breast and prostate cancer risk. Methods 200,643 individuals from the UK Biobank were stratified as follows: 1. Heterozygotes or non-heterozygotes of PV in moderate to high cancer risk genes, 2. PRS strata, 3. with or without a family history of cancer. Multivariable logistic regression and Cox proportional hazards models were used to compute the odds ratio (OR) across groups and the cumulative incidence through life. Results Cumulative incidence by age 70 among non-heterozygotes across PRS strata ranged from 9% to 32% and from 9% to 35% for breast and prostate cancer, respectively. Among PV heterozygotes it ranged from 20% to 48% in moderate-risk genes and from 51% to 74% in high-risk genes for breast cancer, and it ranged from 30% to 59% in prostate cancer risk genes. Family history is always associated with an increased cancer OR. Conclusion PRS provides a meaningful risk gradient leading alone to a cancer risk comparable to PV in moderate risk genes while acting as risk modifier for high-risk genes. Including family history beside PV and PRS further improves cancer risk stratification

Breast and prostate cancer risk: the interplay of polygenic risk, high-impact monogenic variants, and family history 2021.06.04.21258277

Purpose: Investigate to which extent polygenic risk scores (PRS), high-impact monogenic variants, and family history affect breast and prostate cancer risk by assessing cancer prevalence and cancer cumulative lifetime incidence. Methods 200,643 individuals from the UK Biobank were stratified as follows: 1. carriers or non-carriers of high impact constitutive, monogenic variants in cancer susceptibility genes, 2. high or non-high PRS (90th percentile threshold), 3. with or without a family history of cancer. Multivariable logistic regression was used to compare the odds ratio (OR) across the different groups while Cox proportional hazards models were used to compute the cumulative incidence through life. Results Breast and prostate cancer cumulative incidence by age 70 is 7 and 5 for non-carriers with non-high PRS and reaches 37 and 32 among carriers of high-impact variants in cancer susceptibility genes with high PRS. The additional presence of family history is associated with a further increase of the risk of developing cancer reaching an OR of 14 and 21 for breast and prostate cancer, respectively. Conclusion: High PRS confers a cancer risk comparable to high-impact monogenic variants. Family history, monogenic variants, and PRS contribute additively to breast and prostate cancer risk