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Objective: - To ascertain whether restriction of dietary sodium lowers blood pressure in hypertensive and normotensive individuals.Data Sources: - An English-language computerized literature search, restricted to human studies with Medical Subject Heading terms, "hypertension," "blood pressure," "vascular resistance," "sodium and dietary," "diet and sodium restricted," "sodium chloride," "clinical trial," "randomized controlled trial," and "prospective studies," was conducted. Bibliographies of review articles and personal files were also searched.Trial Selection: - Trials that had randomized allocation to control and dietary sodium intervention groups, monitored by timed sodium excretion, with outcome measures of both systolic and diastolic blood pressure were selected by blinded review of the methods section.Data Extraction: - Two observers extracted data independently, using purpose-designed forms, and discrepancies were resolved by discussion.Data Synthesis: - The 56 trials that met our inclusion criteria showed significant heterogeneity. Publication bias was also evident. The mean reduction (95% confidence interval) in daily urinary sodium excretion, a proxy measure of dietary sodium intake, was 95 mmol/d (71-119 mmol/d) in 28 trials with 1131 hypertensive subjects and 125 mmol/d (95-156 mmol/d) in 28 trials with 2374 normotensive subjects. After adjustment for measurement error of urinary sodium excretion, the decrease in blood pressure for a 100-mmol/d reduction in daily sodium excretion was 3.7 mm Hg (2.35-5.05 mm Hg) for systolic (P<.001) and 0.9 mm Hg (-0.13 to 1.85 mm Hg) for diastolic (P=.09) in the hypertensive trials, and 1.0 mm Hg (0.51-1.56 mm Hg) for systolic (P<.001) and 0.1 mm Hg (-0.32 to 0.51 mm Hg) for diastolic (P=.64) in the normotensive trials. Decreases in blood pressure were larger in trials of older hypertensive individuals and small and nonsignificant in trials of normotensive individuals whose meals were prepared and who lived outside the institutional setting.Conclusion: - Dietary sodium restriction for older hypertensive individuals might be considered, but the evidence in the normotensive population does not support current recommendations for universal dietary sodium restriction. [ABSTRACT FROM AUTHOR]

Background: Whether to stop oral anticoagulants after a first unprovoked venous thromboembolism (VTE) is challenging, partially due to an intriguingly higher risk of VTE recurrence (rVTE) in men after therapy discontinuation. DNA methylation (DNAm) differences between men and women might underly this sex-biased rVTE risk difference., Aim: To investigate sex-specific associations between DNAm at cytosine-phosphate-guanine (CpG) sites and rVTE., Methods: In 417 unprovoked VTE patients, including 101 experiencing recurrences over 5-year follow-up (REVERSE I), we analyzed blood DNAm using the Illumina EPIC array and performed a sex-stratified epigenome-wide association study. We further examined 181 major provoked VTE patients, including 36 recurrences over 14-year follow-up (MARTHA), to investigate whether DNAm are risk factors for rVTE after anticoagulation therapy., Results: Hypomethylated CpGs at genes TBC1D22B-cg01060850 and ZHX2-cg07808424 in men and DIP2B-ch.12.1038646R and DENND3-cg03401656 in women were associated with rVTE at genome-wide level (p<7e-8). Though not statistically significant, DENND3-cg03401656 had the same direction of effect in MARTHA women. Sensitivity analysis confirmed the robustness of the estimates including potential confounders, adaptations of the Cox model, non-Europeans, and proximal methylation quantitative trait loci (meQTLs) in the association. The associated CpGs were situated at genes for membrane trafficking, corroborating the participation of Rab regulatory proteins in rVTE, and transcription factors., Conclusions: We identified DNAm marks as potential risk factors for sex-biased recurrence in unprovoked VTE. Further replication and experimental validation could refine our understanding on the regulation of the identified DNAm sites and help optimize personalized decision-making for long-term anticoagulation after first VTE., (Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes., Methods: Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies., Results: After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression., Conclusions: The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

A novel algorithm, AlphaMissense, has been shown to have an improved ability to predict the pathogenicity of rare missense genetic variants. However, it is not known whether AlphaMissense improves the ability of gene-based testing to identify disease-influencing genes. Using whole-exome sequencing data from the UK Biobank, we compared gene-based association analysis strategies including sets of deleterious variants: predicted loss-of-function (pLoF) variants only, pLoF plus AlphaMissense pathogenic variants, pLoF with missense variants predicted to be deleterious by any of five commonly utilized annotation methods (Missense (1/5)) or only variants predicted to be deleterious by all five methods (Missense (5/5)). We measured performance to identify 519 previously identified positive control genes, which can lead to Mendelian diseases, or are the targets of successfully developed medicines. These strategies identified 0.85 million pLoF variants and 5 million deleterious missense variants, including 22,131 likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at p < 1.25 × 10 -7 ) across 24 common traits and diseases. Compared with pLoFs plus Missense (5/5), tests using pLoFs and AlphaMissense variants found slightly more significant gene-disease and gene-trait associations, albeit with a marginally lower proportion of positive control genes. Nevertheless, their overall performance was similar. Merging AlphaMissense with Missense (5/5), whether through their intersection or union, did not yield any further enhancement in performance. In summary, employing AlphaMissense to select deleterious variants for gene-based testing did not improve the ability to identify genes that are known to influence disease., Competing Interests: Declaration of interests J.B.R. is the CEO of 5 Prime Sciences (www.5primesciences.com), which provides research services for biotech, pharma, and venture capital companies for projects unrelated to this research. He has served as an advisor to GlaxoSmithKline and Deerfield Capital. J.B.R.’s institution has received investigator-initiated grant funding from Eli Lilly, GlaxoSmithKline, and Biogen for projects unrelated to this research. Y.C. is an employee of 5 Prime Sciences. T.S. has received an endowment unrelated to this research from Eli Lilly., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS.", (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

This study explored the interactions among prenatal stress, child sex, and polygenic risk scores (PGS) for attention-deficit/hyperactivity disorder (ADHD) on structural developmental changes of brain regions implicated in ADHD. We used data from two population-based birth cohorts: Growing Up in Singapore Towards healthy Outcomes (GUSTO) from Singapore (n = 113) and Generation R from Rotterdam, the Netherlands (n = 433). Prenatal stress was assessed using questionnaires. We obtained latent constructs of prenatal adversity and prenatal mood problems using confirmatory factor analyses. The participants were genotyped using genome-wide single nucleotide polymorphism arrays, and ADHD PGSs were computed. Magnetic resonance imaging scans were acquired at 4.5 and 6 years (GUSTO), and at 10 and 14 years (Generation R). We estimated the age-related rate of change for brain outcomes related to ADHD and performed (1) prenatal stress by sex interaction models, (2) prenatal stress by ADHD PGS interaction models, and (3) 3-way interaction models, including prenatal stress, sex, and ADHD PGS. We observed an interaction between prenatal stress and ADHD PGS on mean cortical thickness annual rate of change in Generation R (i.e., in individuals with higher ADHD PGS, higher prenatal stress was associated with a lower rate of cortical thinning, whereas in individuals with lower ADHD PGS, higher prenatal stress was associated with a higher rate of cortical thinning). None of the other tested interactions were statistically significant. Higher prenatal stress may promote a slower brain developmental rate during adolescence in individuals with higher ADHD genetic vulnerability, whereas it may promote a faster brain developmental rate in individuals with lower ADHD genetic vulnerability., (© 2024 The Authors. Developmental Psychobiology published by Wiley Periodicals LLC.)

The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Garg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Context: Effects of modest alcohol consumption remain controversial. Mendelian randomization (MR) can help to mitigate biases due to confounding and reverse causation in observational studies, and evaluate the potential causal role of alcohol consumption., Objective: This work aimed to evaluate dose-dependent effect of alcohol consumption on obesity and type 2 diabetes., Methods: Assessing 408 540 participants of European ancestry in the UK Biobank, we first tested the association between self-reported alcohol intake frequency and 10 anthropometric measurements, obesity, and type 2 diabetes. We then conducted MR analyses both in the overall population and in subpopulations stratified by alcohol intake frequency., Results: Among individuals having more than 14 drinks per week, a 1-drink-per-week increase in genetically predicted alcohol intake frequency was associated with a 0.36-kg increase in fat mass (SD = 0.03 kg), a 1.08-fold increased odds of obesity (95% CI, 1.06-1.10), and a 1.10-fold increased odds of type 2 diabetes (95% CI, 1.06-1.13). These associations were stronger in women than in men. Furthermore, no evidence was found supporting the association between genetically increased alcohol intake frequency and improved health outcomes among individuals having 7 or fewer drinks per week, as MR estimates largely overlapped with the null. These results withstood multiple sensitivity analyses assessing the validity of MR assumptions., Conclusion: As opposed to observational associations, MR results suggest there may not be protective effects of modest alcohol consumption on obesity traits and type 2 diabetes. Heavy alcohol consumption could lead to increased measures of obesity as well as increased risk of type 2 diabetes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

DNA methylation (DNAm) is a dynamic, age-dependent epigenetic modification that can be used to study interactions between genetic and environmental factors. Environmental exposures during critical periods of growth and development may alter DNAm patterns, leading to increased susceptibility to diseases such as asthma and allergies. One method to study the role of DNAm is the epigenetic clock-an algorithm that uses DNAm levels at select age-informative Cytosine-phosphate-Guanine (CpG) dinucleotides to predict epigenetic age (EA). The difference between EA and calendar age (CA) is termed epigenetic age acceleration (EAA) and reveals information about the biological capacity of an individual. Associations between EAA and disease susceptibility have been demonstrated for a variety of age-related conditions and, more recently, phenotypes such as asthma and allergic diseases, which often begin in childhood and progress throughout the lifespan. In this review, we explore different epigenetic clocks and how they have been applied, particularly as related to childhood asthma. We delve into how in utero and early life exposures (e.g., smoking, air pollution, maternal BMI) result in methylation changes. Furthermore, we explore the potential for EAA to be used as a biomarker for asthma and allergic diseases and identify areas for further study.

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10 -7 ). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10 -5 ). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10 -10 ). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated., Methods: Leveraging a meta-analysis of major depressive disorder genome-wide association studies ( N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank ( N = 130,092 European ancestry individuals). In a UK Biobank test dataset ( N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression., Results: In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively., Conclusion: We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression., Competing Interests: TL was an employee of 5 Prime Sciences Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lu, Silveira and Greenwood.)

Objective: The primary purpose of this study was to investigate the contribution of genetic predispositions to depression and inflammation, as measured through polygenic risk scores, on symptom burden (physical and psychological) in patients with head and neck cancer in the immediate post-treatment period (i.e., at three months post-diagnosis), as well as on 3-, 6-, 12-, 24- and 36-month survival., Methods: Prospective longitudinal study of 223 adults (72 % participation) newly diagnosed with a first occurrence of primary head and neck cancer, paired with genetic data (Illumina PsychArray), validated psychometric measures, Structured Clinical Interviews for DSM Disorders (SCID-I), and medical chart reviews., Results: Symptom burden at 3 months was predicted by (R 2 adj. = 0.38, p < 0.001): a baseline SCID-I Anxiety Disorder (b = 1.69, B = 0.23, 95%CI = 0.43-2.94; p = 0.009), baseline levels of HADS anxiety (b = 0.20, B = 0.29, 95%CI = 0.07-0.34; p = 0.003), the polygenic risk score (PRS) for depression (b = 0.66, B = 0.18, 95%CI = 0.003-1.32; p = 0.049), and cumulated dose of radiotherapy (b = 0.002, B = 0.46, 95%CI = 0.001-0.003; p < 0.001). When controlling for factors known to be associated with cancer survival, patients with a higher PRS associated with depression and inflammation, respectively, presented higher risk of death within 36 months (b = 1.75, Exp(B) = 5.75, 95%CI = 1.55-21.27, p = 0.009 and b = 0.14, Exp(B) = 1.15, 95%CI = 1.01-1.30, p = 0.03)., Conclusions: Our results outline three potential pathways of symptom burden in patients with head and neck cancer: a genetic predisposition towards depression; an initial anxiety disorder upon being diagnosed with cancer or high levels of anxiety upon diagnosis; and a dose-related response to radiotherapy. One may want to investigate early interventions in these areas to alleviate symptom burden in patients faced with a life-threatening disease, as well as consider targeting genetic predisposition towards depression and inflammation implicated in survival. The high prevalence of distress in patients with head and neck cancer is an opportunity to study genetic predispositions, which could potentially be broadly generalized to other cancers and diseases., Competing Interests: Declaration of competing interest There is no conflict of interest related to this publication., (Published by Elsevier B.V.)

Background: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods., Methods: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter., Results: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders., Conclusions: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis., (© 2023. The Author(s).)

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care., (© 2023. The Author(s).)

Background: Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC., Methods: This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model., Results: GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC., Conclusion: GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted., Competing Interests: Authors HS and WT disclose patent filings protecting claims of intellectual property and the monoclonal antibodies within this report, under License to AOA Dx where authors AJ and PG work and where HS, AD and SB served as consultants. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Galan, Papaluca, Nejatie, Matanes, Brahimi, Tong, Hachim, Yasmeen, Carmona, Klein, Billes, Dawod, Gawande, Jeter, Mes-Masson, Greenwood, Gotlieb and Saragovi.)

Not all familial ovarian cancer (OC) cases are explained by pathogenic germline variants in known risk genes. A candidate gene approach involving DNA repair pathway genes was applied to identify rare recurring pathogenic variants in familial OC cases not associated with known OC risk genes from a population exhibiting genetic drift. Whole exome sequencing (WES) data of 15 OC cases from 13 families tested negative for pathogenic variants in known OC risk genes were investigated for candidate variants in 468 DNA repair pathway genes. Filtering and prioritization criteria were applied to WES data to select top candidates for further analyses. Candidates were genotyped in ancestry defined study groups of 214 familial and 998 sporadic OC or breast cancer (BC) cases and 1025 population-matched controls and screened for additional carriers in 605 population-matched OC cases. The candidate genes were also analyzed in WES data from 937 familial or sporadic OC cases of diverse ancestries. Top candidate variants in ERCC5 , EXO1 , FANCC, NEIL1 and NTHL1 were identified in 5/13 (39%) OC families. Collectively, candidate variants were identified in 7/435 (1.6%) sporadic OC cases and 1/566 (0.2%) sporadic BC cases versus 1/1025 (0.1%) controls. Additional carriers were identified in 6/605 (0.9%) OC cases. Tumour DNA from ERCC5, NEIL1 and NTHL1 variant carriers exhibited loss of the wild-type allele. Carriers of various candidate variants in these genes were identified in 31/937 (3.3%) OC cases of diverse ancestries versus 0-0.004% in cancer-free controls. The strategy of applying a candidate gene approach in a population exhibiting genetic drift identified new candidate OC predisposition variants in DNA repair pathway genes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Alenezi, Fierheller, Serruya, Revil, Oros, Subramanian, Bruce, Spiegelman, Pugh, Campbell, Mes-Masson, Provencher, Foulkes, Haffaf, Rouleau, Bouchard, Greenwood, Ragoussis and Tonin.)

FANCI was recently identified as a new candidate ovarian cancer (OC)-predisposing gene from the genetic analysis of carriers of FANCI c.1813C>T; p.L605F in OC families. Here, we aimed to investigate the molecular genetic characteristics of FANCI, as they have not been described in the context of cancer. We first investigated the germline genetic landscape of two sisters with OC from the discovery FANCI c.1813C>T; p.L605F family (F1528) to re-affirm the plausibility of this candidate. As we did not find other conclusive candidates, we then performed a candidate gene approach to identify other candidate variants in genes involved in the FANCI protein interactome in OC families negative for pathogenic variants in BRCA1 , BRCA2 , BRIP1 , RAD51C , RAD51D , and FANCI , which identified four candidate variants. We then investigated FANCI in high-grade serous ovarian carcinoma (HGSC) from FANCI c.1813C>T carriers and found evidence of loss of the wild-type allele in tumour DNA from some of these cases. The somatic genetic landscape of OC tumours from FANCI c.1813C>T carriers was investigated for mutations in selected genes, copy number alterations, and mutational signatures, which determined that the profiles of tumours from carriers were characteristic of features exhibited by HGSC cases. As other OC-predisposing genes such as BRCA1 and BRCA2 are known to increase the risk of other cancers including breast cancer, we investigated the carrier frequency of germline FANCI c.1813C>T in various cancer types and found overall more carriers among cancer cases compared to cancer-free controls ( p = 0.007). In these different tumour types, we also identified a spectrum of somatic variants in FANCI that were not restricted to any specific region within the gene. Collectively, these findings expand on the characteristics described for OC cases carrying FANCI c.1813C>T; p.L605F and suggest the possible involvement of FANCI in other cancer types at the germline and/or somatic level.

Background: There is a pressing need for novel drug targets for psychiatric disorders. Circulating proteins are potential candidates because they are relatively easy to measure and modulate and play important roles in signaling., Methods: We performed two-sample Mendelian randomization analyses to estimate the associations between circulating protein abundances and risk of 10 psychiatric disorders. Genetic variants associated with 1611 circulating protein abundances identified in 6 large-scale proteomic studies were used as genetic instruments. Effects of the circulating proteins on psychiatric disorders were estimated by Wald ratio or inverse variance-weighted ratio tests. Horizontal pleiotropy, colocalization, and protein-altering effects were examined to validate the assumptions of Mendelian randomization., Results: Nine circulating protein-to-disease associations withstood multiple sensitivity analyses. Among them, 2 circulating proteins had associations replicated in 3 proteomic studies. A 1 standard deviation increase in the genetically predicted circulating TIMP4 level was associated with a reduced risk of anorexia nervosa (minimum odds ratio [OR] = 0.83; 95% CI, 0.76-0.91) and bipolar disorder (minimum OR = 0.88; 95% CI, 0.82-0.94). A 1 standard deviation increase in the genetically predicted circulating ESAM level was associated with an increased risk of schizophrenia (maximum OR = 1.32; 95% CI, 1.22-1.43). In addition, 58 suggestive protein-to-disease associations warrant validation with observational or experimental evidence. For instance, a 1 standard deviation increase in the ERLEC1-201-to-ERLEC1-202 splice variant ratio was associated with a reduced risk of schizophrenia (OR = 0.94; 95% CI, 0.90-0.97)., Conclusions: Prioritized circulating proteins appear to influence the risk of psychiatric disease and may be explored as intervention targets., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Genomic risk prediction is on the emerging path toward personalized medicine. However, the accuracy of polygenic prediction varies strongly in different individuals. Based on up to 352,277 European ancestry participants in the UK Biobank, we constructed polygenic risk scores for 15 physiological and biochemical quantitative traits. We identified a total of 185 polygenic prediction variability quantitative trait loci for 11 traits by Levene's test among 254,376 unrelated individuals. We validated the effects of prediction variability quantitative trait loci using an independent test set of 58,927 individuals. For instance, a score aggregating 51 prediction variability quantitative trait locus variants for triglycerides had the strongest Spearman correlation of 0.185 (P-value <1.0 × 10-300) with the squared prediction errors. We found a strong enrichment of complex genetic effects conferred by prediction variability quantitative trait loci compared to risk loci identified in genome-wide association studies, including 89 prediction variability quantitative trait loci exhibiting dominance effects. Incorporation of dominance effects into polygenic risk scores significantly improved polygenic prediction for triglycerides, low-density lipoprotein cholesterol, vitamin D, and platelet. In conclusion, we have discovered and profiled genetic determinants of polygenic prediction variability for 11 quantitative biomarkers. These findings may assist interpretation of genomic risk prediction in various contexts and encourage novel approaches for constructing polygenic risk scores with complex genetic effects., (© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future., (© 2022. Crown.)

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 St.-Pierre, Zhang, Lu, Jiang, Loffree, Wang, Bhatnagar and Greenwood.)

CDC20 is a coactivator of the anaphase promoting complex/cyclosome (APC/C) and is essential for mitotic progression. APC/CCDC20 is inhibited by the spindle assembly checkpoint (SAC), which prevents premature separation of sister chromatids and aneuploidy in daughter cells. Although overexpression of CDC20 is common in many cancers, oncogenic mutations have never been identified in humans. Using whole-exome sequencing, we identified heterozygous missense CDC20 variants (L151R and N331K) that segregate with ovarian germ cell tumors in two families. Functional characterization showed these mutants retain APC/C activation activity but have impaired binding to BUBR1, a component of the SAC. Expression of L151R and N331K variants promoted mitotic slippage in HeLa cells and primary skin fibroblasts derived from carriers. Generation of mice carrying the N331K variant using CRISPR-Cas9 showed that, although homozygous N331K mice were nonviable, heterozygotes displayed accelerated oncogenicity of Myc-driven cancers. These findings highlight an unappreciated role for CDC20 variants as tumor-promoting genes., Significance: Two germline CDC20 missense variants that segregate with cancer in two families compromise the spindle assembly checkpoint and lead to aberrant mitotic progression, which could predispose cells to transformation. See related commentary by Villarroya-Beltri and Malumbres, p. 3432., (©2022 American Association for Cancer Research.)

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels., (© 2022 Wiley Periodicals LLC.)

Introduction: Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions., Methods: We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support., Results: 580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10 -4 ). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex., Conclusions: Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection., (© 2022. The Author(s).)

Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

New therapies may help to prevent osteoporotic fractures other than through increasing bone mineral density (BMD). Because fracture risk has an important genetic component, we aim to identify loci increasing fracture risk that do not decrease BMD, using a recently-proposed structural equation model adapted to remove genetic influences of BMD on fracture risk. We used summary statistics of the largest genome-wide association studies (GWASs) for BMD and for fracture in these analyses. We next estimated the genetic correlation between the non-BMD or BMD-related genetic effects and other clinical risk factors for fracture. Last, based on white British participants in the UK Biobank, we conducted genetic risk score analyses to assess whether the aggregated genetic effects conferred increased major osteoporotic fracture risk. We found that only three loci affecting fracture risk exhibited genetic effects not mediated by BMD: SOST, CPED1-WNT16, and RSPO3, while these three loci simultaneously conferred BMD-related effects. No strong genetic associations between non-BMD or BMD-related effects and 16 clinical risk factors were observed. However, non-BMD effects might be genetic correlated with hip bone size. In the UK Biobank, a 1 standard deviation (1-SD) increase in the non-BMD genetic risk score conferred an odds ratio of 1.17 for incident major osteoporotic fracture, compared to 1.29 by a BMD-related genetic risk score. Our study suggests that the majority of common genetic predisposition toward fracture risk acts upon BMD. Although non-BMD genetic effects may exist, they are not strongly correlated with most traditional clinical risk factors. Risk loci harboring non-BMD genetic effects may influence other perspectives of bone quality, or confer effects that existing GWASs fail to capture, but they demonstrate weaker impact on fracture risk than BMD-related genetic effects. These findings suggest that most successful drug development programs for osteoporosis should focus on pathways identified through BMD-associated loci. © 2022 American Society for Bone and Mineral Research (ASBMR)., (© 2022 American Society for Bone and Mineral Research (ASBMR).)

Purpose: The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases., Methods: Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history., Results: Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%., Conclusion: Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing., Competing Interests: Conflict of Interest J.B.R. is the founder of 5 Prime Sciences and has served as a consultant to GlaxoSmithKline plc and Deerfield for their genetics programs. All other authors declare no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ~ 55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ~ 400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models., (© 2022. The Author(s).)

Background: Polygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance., Methods: We computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor., Results: Seven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology., Conclusions: The results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders., (© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.)

A priori power and sample size calculations are crucial to appropriately test null hypotheses and obtain valid conclusions from all clinical studies. Statistical tests to evaluate hypotheses in microbiome studies need to consider intrinsic features of microbiome datasets that do not apply to classic sample size calculation. In this review, we summarize statistical approaches to calculate sample sizes for typical microbiome study scenarios, including those that hypothesize microbiome features to be the outcome, the exposure or the mediator, and provide relevant R scripts to conduct some of these calculations. This review is intended to be a resource to facilitate the conduct of sample size calculations that are based on testable hypotheses across several dimensions of the microbiome. Implementation of these methods will improve the quality of human or animal microbiome studies, enabling reliable conclusions that will generalize beyond the study sample., (© 2022. The Author(s), under exclusive licence to Society for Mucosal Immunology.)

To identify candidate variants in RAD51C and RAD51D ovarian cancer (OC) predisposing genes by investigating French Canadians (FC) exhibiting unique genetic architecture. Candidates were identified by whole exome sequencing analysis of 17 OC families and 53 early-onset OC cases. Carrier frequencies were determined by the genetic analysis of 100 OC or HBOC families, 438 sporadic OC cases and 1025 controls. Variants of unknown function were assayed for their biological impact and/or cellular sensitivity to olaparib. RAD51C c.414G>C;p.Leu138Phe and c.705G>T;p.Lys235Asn and RAD51D c.137C>G;p.Ser46Cys, c.620C>T;p.Ser207Leu and c.694C>T;p.Arg232Ter were identified in 17.6% of families and 11.3% of early-onset cases. The highest carrier frequency was observed in OC families (1/44, 2.3%) and sporadic cases (15/438, 3.4%) harbouring RAD51D c.620C>T versus controls (1/1025, 0.1%). Carriers of c.620C>T (n = 7), c.705G>T (n = 2) and c.137C>G (n = 1) were identified in another 538 FC OC cases. RAD51C c.705G>T affected splicing by skipping exon four, while RAD51D p.Ser46Cys affected protein stability and conferred olaparib sensitivity. Genetic and functional assays implicate RAD51C c.705G>T and RAD51D c.137C>G as likely pathogenic variants in OC. The high carrier frequency of RAD51D c.620C>T in FC OC cases validates previous findings. Our findings further support the role of RAD51C and RAD51D in hereditary OC.