Rare-variant association analysis: study designs and statistical tests

Whole-exome sequencing identifies 5 novel genes associated with carpal tunnel syndrome

Carpal tunnel syndrome (CTS), a common peripheral nerve entrapment disorder, has a high estimated heritability index. Although previous genome-wide association studies have assessed common genetic components of CTS, the risk contributed by coding variants is still not well understood. Here, we performed the largest exome-wide analyses using UK Biobank data from 350 770 participants to find coding variants associated with CTS. We then explored the relative contribution of both rare mutations and polygenic risk score (PRS) to CTS risk in survival analyses. Finally, we investigated the functional pathways of the CTS-related coding genes identified above. Aside from conforming 6 known CTS genes, 5 novel genes were identified (SPSB1, SYNC, ITGB5, MUC13 and LOXL4). The associations of most genes we identified with incident CTS were striking in survival analyses. Additionally, we provided evidence that combining rare coding alleles and polygenic risk score can improve the genetic prediction of CTS. Functional enrichment analyses revealed potential roles of the identified coding variants in CTS pathogenesis, where they contributed to extracellular matrix organization. Our results evaluated the contribution to CTS etiology from quantities of coding variants accessible to exome sequencing data.

GWAS significance thresholds in large cohorts of European ancestry

While the P-value threshold of 5.0×10-8 remains the standard for genome-wide association studies (GWAS) in humans and other species, it still needs to be updated to reflect the current era of large-scale GWAS, where tens of thousands of sample sizes are used to discover genetic associations at loci with smaller minor allele frequencies. In this study, we used a dataset of 348,501 individuals of European ancestry from the UK Biobank to determine the GWAS thresholds required for multiple testing corrections when considering rare and common variants in additive and dominant GWAS models. Additionally, we employed conditional and joint analysis to quantify the proportion of false significant hits in the GWAS results for 72 traits in the UK Biobank when applying the traditional GWAS cutoff vs our newly proposed P-value thresholds. Overall, the results indicate that the conventional GWAS significance threshold of 5.0×10-8 yields a false-positive rate of between 20% and 30% in GWAS studies that utilize large sample sizes and less common variants. Instead, a more stringent GWAS P-value threshold of 5.0×10-9 is needed when rare variants (with minor allele frequency > 0.1%) are included in the association test for both additive and dominance models within the European ancestry population. However, further validation across diverse datasets and study designs, is needed to evaluate the broader applicability of this proposed threshold.

FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study

BACKGROUND

As humans age at different rates, physical appearance can yield insights into biological age and physiological health more reliably than chronological age. In medicine, however, appearance is incorporated into medical judgements in a subjective and non-standardised way. In this study, we aimed to develop and validate FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs.

METHODS

FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb-Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan-Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes.

FINDINGS

FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p<0·0001). We found that FaceAge can improve physicians' survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70-0·78] to 0·8 [0·76-0·83]; p<0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not.

INTERPRETATION

Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as FaceAge could be used to translate a patient's visual appearance into objective, quantitative, and clinically valuable measures.

FUNDING

US National Institutes of Health and EU European Research Council.

Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method

Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.

Whole exome sequencing identified six novel genes for depressive symptoms

Previous genome-wide association studies of depression have primarily focused on common variants, limiting our comprehensive understanding of the genetic architecture. In contrast, whole-exome sequencing can capture rare coding variants, helping to explore the phenotypic consequences of altering protein-coding genes. Here, we conducted a large-scale exome-wide association study on 296,199 participants from the UK Biobank, assessing their depressive symptom scores through the Patient Health Questionnaire-4. We identified 22 genes associated with depressive symptoms, including 6 newly discovered genes (TRIM27, UBD, SVOP, ADGRB2, IRF2BPL, and ANKRD12). Both ontology enrichment analysis and plasma proteomics association analysis consistently revealed that the identified genes were associated with immune responses. Furthermore, we identified associations between these genes and brain regions related to depression, such as anterior cingulate cortex and orbitofrontal cortex. Additionally, phenome-wide association analysis demonstrated that TRIM27 and UBD were associated with neuropsychiatric, cognitive, biochemistry, and inflammatory traits. Our findings offer new insights into the potential mechanisms and genetic architecture of depressive symptoms.

Identifying Rare Germline Variants Associated with Metastatic Prostate Cancer Through an Extreme Phenotype Study

Background

Studies of germline variants in prostate cancer (PCa) have largely focused on their connections to cancer predisposition. However, an understanding of how heritable factors contribute to cancer progression and metastasis remain limited.

Objective

To identify low frequency to rare germline nonsynonymous variants associated with increased risk for metastatic PCa (mPCa), while providing functional validation.

Design

We assembled an extreme phenotype cohort (EPC) of 52 patients diagnosed with predominantly high-grade (Gleason Score (GS) ≥ 8) PCa and > 7 years of follow-up for which localized treatment naïve tumor tissues were available. In half of the cases, the tumor had metastasized to bone, providing an even distribution of bone mPCa and non mPCa cases. Tumor and matched distant benign DNA samples were exome sequenced and analyzed for germline variants with population-wide minor allelic frequencies ≤ 2%. Findings were validated using two independent PCa germline cohorts, including a closely matched Australian study biased to aggressive disease (n = 53) and Pan Prostate Cancer Group (PPCG, n = 976). Two mPCa-promoting candidate variants in KDM6B and BRCA2 were engineered into cell lines and functionalized.

Results

Germline nonsynonymous rare variants (gnsRVs) identified in 25 DNA Damage Repair (DDR) genes were significantly enriched in the mPCa patients (p=4.57e-06). Conversely, the prevalence of synonymous variants at minor allele frequencies of ≤ 2% were similar between the mPCa and non mPCa patients. The predictive power of variants in 53 non-DDR genes was validated in the Australian cohort (p=0.028) and correlated with high-risk PCa in PPCG (p=0.03). KDM6B K973Q showed functional significance despite being annotated as benign in ClinVar, while BRCA2 I1962T showed sensitivity to Olaparib. In total, six EPC variants related to DNA repair or epigenetics were found to alter enzymatic activity.

Conclusions

EPCs coupled with low frequency/rare variant analyses may advance understanding of interactions between the germline and tumor in PCa. We identified a series of germline variants that were enriched among mPCa patients. Moreover, we showed that one of these variants confers a metastatic phenotype. Our findings suggest that germline testing at diagnosis may improve treatment stratification in PCa.

Patient summary

The presence of specific genetic variants among men with PCa may elevate the risk of mPCa once PCa develops. Knowledge of the variant burden at time of diagnosis may enable accurate stratification of some patients for aggressive therapeutic interventions.

CR1 variants contribute to FSGS susceptibility across multiple populations

Focal segmental glomerulosclerosis (FSGS) is a leading cause of nephrotic syndrome, with an annual incidence of 24 cases per million among African-Americans and 5 per million among European-Americans in the United States. It ranks as the second most common glomerular disease in Europe and Latin America and the fifth in Asia. We conducted a case-control study involving 726 FSGS cases and 13,994 controls from diverse ethnic backgrounds, using panel sequencing of ∼2,500 podocyte-expressed genes. Rare variant association tests confirmed known risk genes (KANK1, COLAPOL1) and identified a significant association with the CR1 gene. The CR1 variant rs17047661, which encodes the Sl1/Sl2 (R1601G) allele, was previously linked to cerebral malaria protection and is now identified as a risk variant for FSGS. This highlights an evolutionary trade-off between infectious disease resistance and kidney disease susceptibility, emphasizing the role of adaptive immunity in FSGS pathogenesis and potential therapeutic targets.

Exploring Monogenic, Polygenic, and Epigenetic Models of Common Variable Immunodeficiency

Common variable immunodeficiency (CVID) is the most frequent symptomatic inborn error of immunity (IEI). CVID is genetically heterogeneous and occurs in sporadic or familial forms with different inheritance patterns. Monogenic mutations have been found in a low percentage of patients, and multifactorial or polygenic inheritance may be involved in unsolved patients. In the complex disease model, the epistatic effect of multiple variants in several genes and environmental factors such as infections may contribute. Epigenetic modifications, such as DNA methylation changes, are also proposed to be involved in CVID pathogenesis. In general, the pathogenic mechanism and molecular basis of CVID disease are still unknown, and identifying patterns of association across the genome in polygenic models and epigenetic modification profiles in CVID requires more studies. Here, we describe the current knowledge of the molecular genetic basis of CVID from monogenic, polygenic, and epigenetic aspects.

The contribution of coding variants to the heritability of multiple cancer types using UK Biobank whole-exome sequencing data

Genome-wide association studies have been highly successful at identifying common variants associated with cancer; however, they do not explain all the inherited risks of cancer. Family-based studies, targeted sequencing, and, more recently, exome-wide association studies have identified rare coding variants in some genes associated with cancer risk, but the overall contribution of these variants to the heritability of cancer is less clear. Here, we describe a method to estimate the genome-wide contribution of rare coding variants to heritability that fits models to the burden effect sizes using an empirical Bayesian approach. We apply this method to the burden of protein-truncating variants in over 15,000 genes for 11 cancers in the UK Biobank using whole-exome sequencing data on over 400,000 individuals. We extend the method to consider the overlap of genes contributing to pairs of cancers. We found ovarian cancer to have the greatest proportion of heritability attributable to protein-truncating variants in genes (46%). The joint cancer models highlight significant clustering of cancer types, including a near-complete overlap in susceptibility genes for breast, ovarian, prostate, and pancreatic cancer. Our results provide insights into the contribution of rare coding variants to the heritability of cancer and identify additional genes with strong evidence of susceptibility to multiple cancer types.

Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing

Next-generation sequencing (NGS) technologies have revolutionized pain research by providing comprehensive insights into genetic variation across the genome. Recent studies have expanded the known spectrum of mutations in genes such as SCN9A and NTRK1, which are commonly mutated in hereditary sensory neuropathies. NGS has uncovered critical alternative splicing events and facilitated single-cell transcriptomics, revealing cellular heterogeneity within tissues. An NGS-based classifier predicted extremely high opioid requirements with 80 % accuracy, highlighting the importance of tailoring opioid therapy based on genetic profiles. Key genes such as GDF5, COL11A1, and TRPV1 have been linked to osteoarthritis risk and pain sensitivity, while HLA-DRB1, TNF, and P2X7 play critical roles in inflammation and pain modulation in rheumatoid arthritis. Innovative tools, such as an atlas of the somatosensory system in neuropathic pain, have been developed based on NGS data, focusing on the dorsal root and trigeminal ganglia. This approach allows the analysis of cellular changes during the development of chronic pain. In the study of rare variants, NGS outperforms single nucleotide variant candidate studies and classical genome-wide association approaches. The complex data generated by NGS enables integrated multi-omics approaches, allowing deeper exploration of the molecular and cellular basis of pain perception. In addition, the characterization of non-coding RNAs has opened new therapeutic avenues. NGS-based pain research faces challenges related to complex data analysis and interpretation of rare genetic variants with unknown biological functions. Nevertheless, NGS offers significant potential for improving personalized pain management and highlights the need for interdisciplinary collaboration to translate findings into clinical practice.

Applying artificial intelligence to uncover the genetic landscape of coagulation factors

Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines its applications in the field of coagulation genetics over the past decade. We observed a significant increase in AI-related publications, with a focus on hemophilia A and B. ML approaches have shown promise in predicting the functional impact of genetic variants and establishing genotype-phenotype correlations, exemplified by tools like "Hema-Class" for factor VIII variants. However, some challenges remain, including the need to expand variant selection beyond missense mutations (which is now the standard of most studies). For the future, the integration of AI in calling, detecting, and interpreting genetic variants can significantly improve our ability to process large-scale genomic data. In this frame, we discuss various AI/ML-based tools for genetic variant detection and interpretation, highlighting their strengths and limitations. As the field evolves, the synergistic application of multiple AI models, coupled with rigorous validation strategies, will be crucial in advancing our understanding of coagulation disorders and for personalizing treatment approaches.

Aberrant gene expression prediction across human tissues

Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. To address this need, we compile an aberrant expression prediction benchmark covering 8.2 million rare variants from 633 individuals across 49 tissues. While not geared toward aberrant expression, the deleteriousness score CADD and the loss-of-function predictor LOFTEE show mild predictive ability (1-1.6% average precision). Leveraging these and further variant annotations, we next train AbExp, a model that yields 12% average precision by combining in a tissue-specific fashion expression variability with variant effects on isoforms and on aberrant splicing. Integrating expression measurements from clinically accessible tissues leads to another two-fold improvement. Furthermore, we show on UK Biobank blood traits that performing rare variant association testing using the continuous and tissue-specific AbExp variant scores instead of LOFTEE variant burden increases gene discovery sensitivity and enables improved phenotype predictions.

Advantages of updated WHO mutation catalog combined with existing whole-genome sequencing-based approaches for Mycobacterium tuberculosis resistance prediction

BACKGROUND

The WHO recently released a second edition of the mutation catalog for predicting drug resistance in Mycobacterium tuberculosis (MTB). This study evaluated its effectiveness compared to existing whole-genome sequencing (WGS)-based prediction methods and proposes a novel approach for its optimization.

METHODS

We tested the accuracy of five tools-the WHO catalog, TB Profiler, SAM-TB, GenTB, and MD-CNN-for predicting drug susceptibility on a global dataset of 36,385 MTB isolates with high-quality phenotypic drug susceptibility testing (DST) and WGS data. By integrating the genotypic DST predictions of these five tools in an ensemble machine learning framework, we developed an improved computational model for MTB drug susceptibility prediction. We then validated the ensemble model on 860 MTB isolates with phenotypic and WGS data collected in Shenzhen, China (2013-2019) and Valencia, Spain (2014-2016).

RESULTS

Among the five genotypic DST tools for predicting susceptibility to ten drugs, MD-CNN exhibited the highest overall performance (AUC 92.1%; 95% CI 89.8-94.4%). The WHO catalog demonstrated the highest specificity of 97.3% (95% CI 95.8-98.4%), while TB Profiler had the best sensitivity at 79.5% (95% CI 71.8-86.2%). The ensemble machine learning model (AUC 93.4%; 95% CI 91.4-95.4%) outperformed all of the five individual tools, with a specificity of 95.4% (95% CI 93.0-97.6%) and a sensitivity of 84.1% (95% CI 78.8-88.8%), principally due to considerable improvements in second-line drug resistance predictions (AUC 91.8%; 95% CI 89.6-94.0%).

CONCLUSIONS

The second edition of the WHO MTB mutation catalog does not, by itself, perform better than existing tools for predicting MTB drug resistance. An integrative approach combining the WHO catalog with other genotypic DST methods significantly enhances prediction accuracy.

Sex difference in genetic risk in the prevalence of atrial fibrillation

BACKGROUND

Early detection and management of atrial fibrillation (AF) are crucial. Combined models incorporating genetic risks and clinical risks have been developed to improve predictive ability. Although sex differences have been reported in many aspects of AF, sex differences in genetic risk have not been studied.

OBJECTIVE

The purpose of this study was to assess the sex difference in the effect of polygenic risk score for AF (AF-PRS) on AF prevalence using cross-sectional data from the Tohoku Medical Megabank Project Community-Based Cohort Study in Japan.

METHODS

AF-PRS and Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score were used for genetic AF risks and clinical AF risks, respectively. Sex differences in the association of AF-PRS with the prevalence of AF were evaluated.

RESULTS

Among 16,853 participants (mean age 63.4 years; 5182, 30.7% men), the prevalence of AF was 255 (4.9%) in men and 130 (1.1%) in women. In the group with high AF-PRS and high CHARGE-AF score, the odds ratio for AF was highest in men and women (8.2 in men and 9.4 in women), compared with that in the group with low AF-PRS and low CHARGE-AF score. Integrating AF-PRS into the CHARGE-AF score significantly enhanced the area under the receiver operating characteristic curve for AF in men (from 0.639 to 0.749) but not in women (from 0.710 to 0.733).

CONCLUSION

Our study is the first to show a sex difference in the association of AF-PRS and AF prevalence. AF-PRS is more closely associated with the prevalence of AF in men than in women.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans owing to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, we reveal a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (∼0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes owing to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potential for phenotypic innovation in young genes compared to older genes, a process under natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

Does the Influence of Low Body Mass Index on Diagnosis Complicate Genetic Studies of the Role of Cardiometabolic Traits in Liability to Anorexia Nervosa?

Anorexia nervosa (AN) is an eating disorder for which the underlying etiology remains mostly uncharacterized. Large-scale genetic studies of AN suggest a relationship between AN liability and cardiometabolic traits, such as lipid and glycemic biology, which may reveal novel treatment targets through pharmacological or nutritional interventions. However, the role of body mass index (BMI) in the diagnosis of AN presents a challenge in the interpretation of these genetic studies. Specifically, BMI is a heritable trait with a genetic architecture that is related to cardiometabolic traits. This becomes particularly salient with the emergence of an atypical AN diagnosis whereby individuals display behaviors consistent with AN, but their BMI remains within normal or higher ranges. In this review, we outline the evidence from genetic studies that support a role of cardiometabolic traits in risk for AN, as well as the unmet need to study cardiometabolic factors in atypical AN. We discuss the influence of selection for individuals with low BMI, particularly from large, international studies that rely on cohorts that used older diagnostic criteria together with efforts from the literature to disentangle these relationships. We conclude that there is at least some evidence that genetic susceptibility to lower BMI may impact the inferred cardiometabolic relationships with AN genetic liability; however, there remains genetic support for a role of metabolic factors in AN risk beyond what is directly attributable to weight-related diagnostic considerations alone. Finally, we provide recommendations for future genetic studies that explore cardiometabolic traits across the spectrum of eating disorders.

Leveraging functional annotations to map rare variants associated with Alzheimer's disease with gruyere

The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-coding, to complex disease. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most are restricted to per-gene models and do not fully leverage the availability of variant-level functional annotations. We propose Genome-wide Rare Variant EnRichment Evaluation (gruyere), a Bayesian probabilistic model that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply gruyere to WGS data from the Alzheimer's Disease (AD) Sequencing Project, consisting of 7,966 cases and 13,412 controls, to identify AD-associated genes and annotations. Growing evidence suggests that disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not had sufficient power to examine rare non-coding effects that incorporate such cell-type specific information. To address this gap, we 1) use predicted enhancer and promoter regions in microglia and other potentially relevant cell types (oligodendrocytes, astrocytes, and neurons) to define per-gene non-coding RV test sets and 2) include cell-type specific variant effect predictions (VEPs) as functional annotations. gruyere identifies 15 significant genetic associations not detected by other RV methods and finds deep learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a novel and robust framework incorporating functional annotations, coding RVs, and cell-type associated non-coding RVs, to perform genome-wide association tests, uncovering AD-relevant genes and annotations.

Rare variant association studies: Significance, methods, and applications in chronic pain studies

Rare genetic variants, characterized by their low frequency in a population, have emerged as essential components in the study of complex disease genetics. The biology of rare variants underscores their significance, as they can exert profound effects on phenotypic variation and disease susceptibility. Recent advancements in sequencing technologies have yielded the availability of large-scale sequencing data such as the UK Biobank whole-exome sequencing (WES) cohort empowered researchers to conduct rare variant association studies (RVASs). This review paper discusses the significance of rare variants, available methodologies, and applications. We provide an overview of RVASs, emphasizing their relevance in unraveling the genetic architecture of complex diseases with special focus on chronic pain and Arthritis. Additionally, we discuss the strengths and limitations of various rare variant association testing methods, outlining a typical pipeline for conducting rare variant association. This pipeline encompasses crucial steps such as quality control of WES data, rare variant annotation, and association testing. It serves as a comprehensive guide for researchers in the field of chronic pain diseases interested in rare variant association studies in large-scale sequencing datasets like the UK Biobank WES cohort. Lastly, we discuss how the identified variants can be further investigated through detailed experimental studies in animal models to elucidate their functional impact and underlying mechanisms.

Insights into genetic architecture and disease associations of genes associated with different human blood group systems using codon usage bias

The differential use of synonymous codons of an amino acid is an imperative evolutionary phenomenon, termed codon usage bias, that functions across various levels of organisms. It is accustomed to providing an understanding of a gene's differential architecture driven by functional regulation of gene expression. Numerous synonymous mutations are linked to various diseases, demonstrating that silent mutations can be deleterious. We employed bioinformatics methods to examine codon usage trends in 263 coding sequences of 44 blood group systems. The blood group systems were categorized into two groups based on association with a sort of neurodegenerative disorder. We performed a CUB study to investigate how multiple components, such as selection, mutation and biased nucleotide composition are accountable for the evolution of the transcripts of the blood group antigens. The compositional analysis implicated blood group genes were GC-rich. RSCU analysis showed G/C-ending codon choice among synonymous codons. Also, a distinct codon choice was found in both blood groups for serine and proline. Moreover, the leucine-coding CTG codon was found the most overrepresented in all the genes, indicating selectional pressure substantially impacts overall codon usage. This was also supported by biplot analysis. Additionally, CpC and GpG overrepresentation is in concordance with the results concerning neurodegenerative disorders where CpC has been attributed to non-CpG methylation and linked to several neurodegenerative ailments. Both the Z-test analysis and rare codon choice showed a substantial difference in codon usage by the genes of both groups.

Assessment of the functionality and usability of open-source rare variant analysis pipelines

Sequencing of increasingly larger cohorts has revealed many rare variants, presenting an opportunity to further unravel the genetic basis of complex traits. Compared with common variants, rare variants are more complex to analyze. Specialized computational tools for these analyses should be both flexible and user-friendly. However, an overview of the available rare variant analysis pipelines and their functionalities is currently lacking. Here, we provide a systematic review of the currently available rare variant analysis pipelines. We searched MEDLINE and Google Scholar until 27 November 2023, and included open-source rare variant pipelines that accepted genotype data from cohort and case-control studies and group variants into testing units. Eligible pipelines were assessed based on functionality and usability criteria. We identified 17 rare variant pipelines that collectively support various trait types, association tests, testing units, and variant weighting schemes. Currently, no single pipeline can handle all data types in a scalable and flexible manner. We recommend different tools to meet diverse analysis needs. STAARpipeline is suitable for newcomers and common applications owing to its built-in definitions for the testing units. REGENIE is highly scalable, actively maintained, regularly updated, and well documented. Ravages is suitable for analyzing multinomial variables, and OrdinalGWAS is tailored for analyzing ordinal variables. Opportunities remain for developing a user-friendly pipeline that provides high degrees of flexibility and scalability. Such a pipeline would enable researchers to exploit the potential of rare variant analyses to uncover the genetic basis of complex traits.

Comparing genomic studies in animal breeding and human genetics: focus on disease-related traits in livestock - A review

Genomic studies of diseases can be divided into two types: i) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and ii) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease based on all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.

Unraveling the Genetics of Shared Clinical and Serological Manifestations in Patients With Systemic Inflammatory Autoimmune Diseases

OBJECTIVE

Systemic inflammatory autoimmune diseases (SIADs) such as systemic lupus erythematosus (SLE), primary Sjögren disease (pSS), and idiopathic inflammatory myopathies (myositis) are complex conditions characterized by shared circulating autoantibodies and clinical manifestations, including skin rashes, among others. This study was aimed at elucidating the genetics underlying these common features.

METHODS

We performed targeted DNA sequencing of coding and regulatory regions from approximately 1,900 immune-related genes in a large cohort of 2,292 well-characterized Scandinavian patients with SIADs with SLE, pSS, and myositis as well as 1,252 controls. A gene-based functionally weighted genetic score for aggregate testing of all genetic variants, including rare variants, was complemented by in silico functional analyses and in vitro reporter experiments.

RESULTS

Case-control association analysis detected known and potentially novel genetic loci in agreement with previous genetic and transcriptomics findings linked to the SIAD autoimmune background. Intriguingly, case-case comparisons between patient subgroups with and without specific autoantibodies revealed that the subgroups defined by antinuclear antibodies and anti-double-stranded DNA antibodies have unique genetic profiles reflecting their heterogeneity. When focusing on clinical features, we overall showed that dual-specificity phosphatase 1 (DUSP1) protective genetic variants lead to increased gene expression and potentially to anti-inflammatory effects on the SIAD-associated skin phenotype. This is consistent with recent genetic findings on eczema and with the previously reported down-regulation of the MAPK signaling-related gene DUSP1 in other skin disorders.

CONCLUSION

Together, this suggests common molecular mechanisms potentially underlying overlapping clinical manifestations shared among different disorders and informs clinical heterogeneity, which could be translated to improve disease diagnostic and treatment, also in more generalized disease frameworks.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

A Genome-Wide Association Study and Rare Variant Analysis for Dupuytren Disease in a North American Population

PURPOSE

Although European genome-wide association studies (GWAS) have aided in defining genetic associations in Dupuytren disease (DD), North American populations have been infrequently analyzed. Additionally, there are a paucity of rare variant analyses (RVA) for DD, which can help define both trait variability and risk for low-frequency variants. Our purpose was to perform a GWAS and RVA for DD using a North American database.

METHODS

The study cohort (cases and controls) consisted of patients from our institutional MyCode Community Health Initiative, an unselected clinical cohort. A GWAS was performed controlling for age, sex and body mass index. For the RVA, sequence kernel association test analysis was performed on the most significant genes from the GWAS. Sequence kernel association test is a regression method to test associations between common and rare genetic variants in a defined region and a specific trait while adjusting for covariates.

RESULTS

A total of 1,123 DD cases and 130,822 controls were included. DD cases were significantly older, more likely to be male, and had higher body mass indices. The GWAS yielded variants in two genes with a statistically significant difference between cases and controls: WNT7B and EPDR1. WNT7B variants rs9330811 (odds ratio, 1.96; 95% confidence interval, 1.73-2.23) and rs10448585 (odds ratio, 1.68; 95% confidence interval, 1.44-1.96) were the top hits. Variant rs2122625 in EPDR1 also reached genome-wide significance. The RVA indicated that WNT7B, DUXA, LOXL1, CSMD2, and TACC2 were significantly associated with a diagnosis of DD.

CONCLUSIONS

In our North American population, GWAS yielded variants in two genes that were significantly associated with DD (WNT7B and EPDR), which likely contribute to abnormal proliferation of fibroblasts. Five rare variants (WNT7B, DUXA, LOXL1, CSMD2, and TACC2) were also significantly associated with DD.

CLINICAL RELEVANCE

As disease-modifying treatments are explored, these data add to a growing body of literature defining genetic variants in DD.

A case-control genome-wide association study of estimated breeding values for resistance to gastrointestinal nematodes in two local dairy sheep breeds

In sheep, gastrointestinal nematodes (GINs) can cause disease, reduced feed intake, and nutritional deficiencies. To counteract GINs, anthelmintics are widely used although it is well known they may enter the environment impacting ecosystems. In addition, anthelmintics resistance has rapidly developed and consequently, alternative approaches are crucial for profitable and sustainable sheep production. The occurrence of resistant individuals is mainly due to their intrinsic genetic diversity; therefore, the implementation of breeding plans for resistant animals may provide a promising strategy to reduce the use of anthelmintics. This study is aimed at identifying genomic regions involved in sheep resistance to GINs. To do this, faecal samples were collected from 642 Comisana and 323 Massese sheep over 3 years to assess Faecal Egg Counts, and Estimated Breeding Values (EBVs) for GIN resistance were estimated by a repeatability animal model. Then, EBVs in the 99.95th and 0.05th percentiles were used to identify the most and least "genetically resistant" individuals to GINs, using genotyped individuals with the Illumina OvineSNP50 beadchip. A genome-wide case-control analysis was performed retaining the most significant single nucleotide polymorphisms (SNPs) with a threshold of 0.005% for the false discovery rate. Genes and Quantitative Trait Loci overlapping significant SNPs were annotated and enriched respectively while genes have been also enriched for functional pathways. As a result, 13 genes on 12 chromosomes and 10 genes on 11 different chromosomes were identified in the Comisana and Massese breed, respectively. Among these, genes involved in the physiology or pathology of the gastrointestinal tract, in adaptive processes and in production traits, were detected. The enrichment analysis highlighted 36 significant pathways in the Comisana breed and 21 in the Massese breed. Many of these pathways were involved in the regulation of the immune response, drug metabolism and detoxification, and vitamin metabolism. Interestingly, pathways involved in vitamin and drug metabolism were also identified in previous research and have shown to play an active role in GIN resistance. In this study, we took advantage of the use of EBVs as a metric for GIN resistance in a case-control genome-wide framework and successfully identified several genomic regions that might be involved in the trait. The presence of overlapping functional pathways related to different genes in the two breeds seems to reinforce the idea of the polygenicity of this trait, and further studies are needed in order to make selection schemes an effective tool to contrast GINs.

NERINE reveals rare variant associations in gene networks across multiple phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease

Gene networks encapsulate biological knowledge, often linked to polygenic diseases. While model system experiments generate many plausible gene networks, validating their role in human phenotypes requires evidence from human genetics. Rare variants provide the most straightforward path for such validation. While single-gene analyses often lack power due to rare variant sparsity, expanding the unit of association to networks offers a powerful alternative, provided it integrates network connections. Here, we introduce NERINE, a hierarchical model-based association test that integrates gene interactions that integrates gene interactions while remaining robust to network inaccuracies. Applied to biobanks, NERINE uncovers compelling network associations for breast cancer, cardiovascular diseases, and type II diabetes, undetected by single-gene tests. For Parkinson's disease (PD), NERINE newly substantiates several GWAS candidate loci with rare variant signal and synergizes human genetics with experimental screens targeting cardinal PD pathologies: dopaminergic neuron survival and alpha-synuclein pathobiology. CRISPRi-screening in human neurons and NERINE converge on PRL, revealing an intraneuronal α-synuclein/prolactin stress response that may impact resilience to PD pathologies.

The Role of de novo and Ultra-Rare Variants in Hirschsprung Disease (HSCR): Extended Gene Discovery for Risk Profiling of Patients

Background

Hirschsprung disease (HSCR) is a rare neurodevelopmental disorder caused by disrupted migration and proliferation of enteric neural crest cells during enteric nervous system development. Genetic studies suggest a complex etiology involving both rare and common variants, but the contribution of ultra-rare pathogenic variants (PAs) remains poorly understood.

Methods

We perform whole-exome sequencing (WES) on 301 HSCR probands and 109 family trios, employing advanced statistical methods and gene prioritization strategies to identify genes carrying de novo and ultra-rare coding pathogenic variants. Multiple study designs, including case-control, de novo mutation analysis and joint test, are used to detect associated genes. Candidate genes are further prioritized based on their biological and functional relevance to disease associated tissues and onset period (i.e., human embryonic colon).

Results

We identify 19 risk genes enriched with ultra-rare coding pathogenic variants in HSCR probands, including four known genes (RET, EDNRB, ZEB2, SOX10) and 15 novel candidates (e.g., COLQ, NES, FAT3) functioning in neural proliferation and neuromuscular synaptic development. These genes account for 17.5% of the population-attributable risk (PAR), with novel candidates contributing 6.5%. Notably, a positive correlation between pathogenic mutational burden and disease severity is observed. Female cases exhibit at least 42% higher ultra-rare pathogenic variant burden than males (P = 0.05).

Conclusions

This first-ever genome-wide screen of ultra-rare variants in a large, phenotypically diverse HSCR cohort highlights the substantial contribution of ultra-rare pathogenic variants to the disease risk and phenotypic variability. These findings enhance our understanding of the genetic architecture of HSCR and provide potential targets for genetic screening and personalized interventions.

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants

Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.

Powerful Rare-Variant Association Analysis of Secondary Phenotypes

Most genome-wide association studies are based on case-control designs, which provide abundant resources for secondary phenotype analyses. However, such studies suffer from biased sampling of primary phenotypes, and the traditional statistical methods can lead to seriously distorted analysis results when they are applied to secondary phenotypes without accounting for the biased sampling mechanism. To our knowledge, there are no statistical methods specifically tailored for rare variant association analysis with secondary phenotypes. In this article, we proposed two novel joint test statistics for identifying secondary-phenotype-associated rare variants based on prospective likelihood and retrospective likelihood, respectively. We also exploit the assumption of gene-environment independence in retrospective likelihood to improve the statistical power and adopt a two-step strategy to balance statistical power and robustness. Simulations and a real-data application are conducted to demonstrate the superior performance of our proposed methods.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases.

The implication of ADRA2A and AVPRIB gene variants in the aetiology of stress-related bipolar disorder

OBJECTIVE

Bipolar disorder is a complex and severe mental illness characterised by manic and depressive episodes that can be triggered and exacerbated by psychosocial, environmental, and biological stressors. Genetic variations are a risk factor for bipolar disorder. However, the identification of the exact gene variants and genotypes remains complex. This study, therefore, aims to identify the potential association between genotypes of analysed single nucleotide polymorphisms and the presence of a stressor in bipolar disorder patients.

METHOD

We analysed 114 single nucleotide polymorphisms (SNPs) from bipolar and stress-related candidate genes in 550 patients with bipolar disorders (60.36 % females and 39.64 % male). We compared SNPs of patients reporting the presence (40.73 %) or absence of stressors (59.27 %) before the first episode using the Persons Chi-square test and Bayes Factor t-test. The genotyping of 114 SNPs was done using TaqMan assays. Statistical analysis was done using Statistica 13.3 software (StatSoft Poland, Krakow, Poland), R programming, and G*Power statistics.

RESULT

We found significant differences in genotype distribution (p < 0.05) in 6 polymorphisms (AVPRIB/rs28536160, FKBP4/rs2968909, ADRA2A/rs3750625, 5HTR2A/rs6311, 5HTR2A/rs6313, and GLCCI1/rs37972) when comparing BD patient with and without stressor with a small effect of d = 0.2. Of these, two gene variants (ADRA2A/rs3750625/AC and AVPRIB/rs28536160/CT) with minor alleles formed an association with the presence of a stressor prior to the disease onset and favoured the alternative hypothesis using Bayes Factor Analysis t-test for hypothesis testing.

CONCLUSION

This study presents a novel association of ADRA2A/rs3750625/AC and AVPR1B/rs28536160/CT gene variants in stress-related bipolar disorder with the AC genotype of ADRA2A/rs3750625 constituting a risk genotype and CT of AVPR1B/rs28536160 constituting a protective genotype. However, further functional analysis is required to fully understand their clinical and biological significance and interaction.

Using Family History Data to Improve the Power of Association Studies: Application to Cancer in UK Biobank

In large cohort studies the number of unaffected individuals outnumbers the number of affected individuals, and the power can be low to detect associations for outcomes with low prevalence. We consider how including recorded family history in regression models increases the power to detect associations between genetic variants and disease risk. We show theoretically and using Monte-Carlo simulations that including a family history of the disease, with a weighting of 0.5 compared with true cases, increases the power to detect associations. This is a powerful approach for detecting variants with moderate effects, but for larger effect sizes a weighting of > 0.5 can be more powerful. We illustrate this both for common variants and for exome sequencing data for over 400,000 individuals in UK Biobank to evaluate the association between the burden of protein-truncating variants in genes and risk for four cancer types.

A Novel Statistical Method for Unmasking Sex-Specific Genomics Signatures in Complex Traits

Genotype-phenotype association studies have advanced our understanding of complex traits but often overlook sex-specific genetic signals. The growing awareness of sex-specific influences on human traits and diseases necessitates tailored statistical methodologies to dissect these genetic intricacies. Rare genetic variants play a significant role in disease development, often exhibiting stronger per-allele effects than common variants. In sex-dimorphic analysis, traits are viewed as having two sex-specific subsets rather than being uniformly defined. Existing methods for gene-based analysis of rare variants across multiple traits can identify shared genetic signals but cannot reveal the specific subsets from which significant signals originate. This means that when a significant signal is detected, it remains unclear whether it arises from the male samples, female samples, or both. To address this limitation, we propose SubsetRV, a new methodology capable of identifying genes associated with specific traits or diseases in males, females, or both. SubsetRV can also be applied to broader applications in multiple traits analysis. Simulation studies have demonstrated SubsetRV's reliability, and real data analysis on bipolar disorder and schizophrenia has revealed potential sex-specific genetic signals. SubsetRV offers a valuable tool for identifying sex-specific genetic candidates, aiding in understanding disease mechanisms. An R package for SubsetRV is available on GitHub. It can be accessed directly through this link: https://github.com/Mansouri-S/SubsetRV.

Exome sequencing of UK birth cohorts

Birth cohort studies involve repeated surveys of large numbers of individuals from birth and throughout their lives. They collect information useful for a wide range of life course research domains, and biological samples which can be used to derive data from an increasing collection of omic technologies. This rich source of longitudinal data, when combined with genomic data, offers the scientific community valuable insights ranging from population genetics to applications across the social sciences. Here we present quality-controlled whole exome sequencing data from three UK birth cohorts: the Avon Longitudinal Study of Parents and Children (8,436 children and 3,215 parents), the Millenium Cohort Study (7,667 children and 6,925 parents) and Born in Bradford (8,784 children and 2,875 parents). The overall objective of this coordinated effort is to make the resulting high-quality data widely accessible to the global research community in a timely manner. We describe how the datasets were generated and subjected to quality control at the sample, variant and genotype level. We then present some preliminary analyses to illustrate the quality of the datasets and probe potential sources of bias. We introduce measures of ultra-rare variant burden to the variables available for researchers working on these cohorts, and show that the exome-wide burden of deleterious protein-truncating variants, S het burden, is associated with educational attainment and cognitive test scores. The whole exome sequence data from these birth cohorts (CRAM & VCF files) are available through the European Genome-Phenome Archive, and here we provide guidance for their use.

Whole-exome sequencing and Drosophila modelling reveal mutated genes and pathways contributing to human ovarian failure

BACKGROUND

Ovarian failure (OF) is a multifactorial, complex disease presented by up to 1% of women under 40 years of age. Despite 90% of patients being diagnosed with idiopathic OF, the underlying molecular mechanisms remain unknown, making it difficult to personalize treatments for these patients in the clinical setting. Studying the presence and/or accumulation of SNVs at the gene/pathway levels will help describe novel genes and characterize disrupted biological pathways linked with ovarian failure.

METHODS

Ad-hoc case-control SNV screening conducted from 2020 to 2023 of 150 VCF files WES data included Spanish IVF patients with (n = 118) and without (n = 32) OF (< 40 years of age; mean BMI 22.78) along with GnomAD (n = 38,947) and IGSR (n = 1,271; 258 European female VCF) data for pseudo-control female populations. SNVs were prioritized according to their predicted deleteriousness, frequency in genomic databases, and proportional differences across populations. A burden test was performed to reveal genes with a higher presence of SNVs in the OF cohort in comparison to control and pseudo-control groups. Systematic in-silico analyses were performed to assess the potential disruptions caused by the mutated genes in relevant biological pathways. Finally, genes with orthologues in Drosophila melanogaster were considered to experimentally validate the potential impediments to ovarian function and reproductive potential.

RESULTS

Eighteen genes had a higher presence of SNVs in the OF population (FDR < 0.05). AK2, CDC27, CFTR, CTBP2, KMT2C, and MTCH2 were associated with OF for the first time and their silenced/knockout forms reduced fertility in Drosophila. We also predicted the disruption of 29 sub-pathways across four signalling pathways (FDR < 0.05). These sub-pathways included the metaphase to anaphase transition during oocyte meiosis, inflammatory processes related to necroptosis, DNA repair mismatch systems and the MAPK signalling cascade.

CONCLUSIONS

This study sheds light on the underlying molecular mechanisms of OF, providing novel associations for six genes and OF-related infertility, setting a foundation for further biomarker development, and improving precision medicine in infertility.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Germline copy number variants and endometrial cancer risk

Known risk loci for endometrial cancer explain approximately one third of familial endometrial cancer. However, the association of germline copy number variants (CNVs) with endometrial cancer risk remains relatively unknown. We conducted a genome-wide analysis of rare CNVs overlapping gene regions in 4115 endometrial cancer cases and 17,818 controls to identify functionally relevant variants associated with disease. We identified a 1.22-fold greater number of CNVs in DNA samples from cases compared to DNA samples from controls (p = 4.4 × 10-63). Under three models of putative CNV impact (deletion, duplication, and loss of function), genome-wide association studies identified 141 candidate gene loci associated (p < 0.01) with endometrial cancer risk. Pathway analysis of the candidate loci revealed an enrichment of genes involved in the 16p11.2 proximal deletion syndrome, driven by a large recurrent deletion (chr16:29,595,483-30,159,693) identified in 0.15% of endometrial cancer cases and 0.02% of control participants. Together, these data provide evidence that rare copy number variants have a role in endometrial cancer susceptibility and that the proximal 16p11.2 BP4-BP5 region contains 25 candidate risk gene(s) that warrant further analysis to better understand their role in human disease.

Impact of population size on population genetic analysis of Short Tandem Repeat (STR) allelic data, forensic and paternity parameters and its effect on forensic DNA analysis

The selection of an appropriate STR allelic frequency database is the prerequisite for assessing the evidentiary value of DNA evidence. Four data sets comprising 50, 100, 200, and 500 samples were evaluated in 21 autosomal STR markers in the Indian and the Bahrain population. Allelic richness showed an increasing trend with the increase in sample size i.e., 193 and 201 (50 samples), 217 and 221 (100 samples), 255 and 238 (200 samples), and 292 and 285 (500 samples) in both the populations. TPOX and D13S317 markers did not show any increase in allele number, whereas SE33 markers showed the highest increase in both populations. With the increase in sample size, 70 (Bahrain population) and 100 (Indian population) alleles having < MAF were detected. Similarly, 37 and 47 previously undetected alleles could be detected when the sample size was increased from 50 to 500 in the Indian and Bahrain populations respectively. In the Indian population, Match probability, decreased with a 500-sample size, whereas, the PIC, PE, Heterozygosity, and PI increased with the increase in sample size. Further, database size did not show any statistical difference in the outcome of the Paternity Index value in the 50 paternity trio cases studied.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n = 227,469 UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East

Background: Colorectal cancer (CRC) displays a complex pattern of inheritance. It is postulated that much of the missing heritability of CRC is enriched in high-impact rare alleles, which might play a crucial role in the etiology and susceptibility of CRC. Methods: In this study, an exome-wide association analysis was performed in 146 patients with high-risk CRC in the Middle East and 1395 healthy controls. The aim was to identify rare germline variants in coding regions and their splicing sites associated with high-risk CRC in the Middle Eastern population. Results: Rare inactivating variants (RIVs) in APC had the strongest association with high-risk CRC (6/146 in cases vs. 1/1395 in controls, OR = 59.7, p = 5.13 × 10-12), whereas RIVs in RIMS1, an RAS superfamily member, were significantly associated with high-risk CRC (5/146 case vs. 2/1395 controls, OR = 24.7, p = 2.03 × 10-8). Rare damaging variants in 17 genes were associated with high-risk CRC at the exome-wide threshold (p < 2.5 × 10-6). Based on the sequence kernel association test, nonsynonymous variants in six genes (TNXB, TAP2, GPSM3, ADGRG4, TMEM229A, and ANKRD33B) had a significant association with high-risk CRC. RIVs in APC-the most common high-penetrance genetic factor-were associated with patients with high-risk CRC in the Middle East. Individuals who inherited APC RIVs had an approximate 60-fold increased risk of developing CRC and were likely to develop the disease earlier. Conclusions: We identified new potential CRC predisposition variants in other genes that could play a role in CRC inheritance. However, large collaborative studies are needed to confirm the association of these variants with high-risk CRC. These results provide information for counseling patients with high-risk CRC and their families in our population.

Gene-based burden tests of rare germline variants identify six cancer susceptibility genes

Discovery of cancer risk variants in the sequence of the germline genome can shed light on carcinogenesis. Here we describe gene burden association analyses, aggregating rare missense and loss of function variants, at 22 cancer sites, including 130,991 cancer cases and 733,486 controls from Iceland, Norway and the United Kingdom. We identified four genes associated with increased cancer risk; the pro-apoptotic BIK for prostate cancer, the autophagy involved ATG12 for colorectal cancer, TG for thyroid cancer and CMTR2 for both lung cancer and cutaneous melanoma. Further, we found genes with rare variants that associate with decreased risk of cancer; AURKB for any cancer, irrespective of site, and PPP1R15A for breast cancer, suggesting that inhibition of PPP1R15A may be a preventive strategy for breast cancer. Our findings pinpoint several new cancer risk genes and emphasize autophagy, apoptosis and cell stress response as a focus point for developing new therapeutics.

The Role of Genetics in Managing Peripheral Arterial Disease

BACKGROUND

Genome wide association studies (GWAS) have allowed for a rapid increase in our understanding of the underlying genetics and biology of many diseases. By capitalizing on common genetic variation between individuals, GWAS can identify DNA variants associated with diseases of interest. A variety of statistical methods can be applied to GWAS results which allows for risk factor identification, stratification, and to identify potential treatments. Peripheral artery disease (PAD) is a common vascular disease that has been shown to have a strong genetic component. This article provides a review of the modern literature and our current understanding of the role of genetics in PAD.

METHODS

All available GWAS studies on PAD were reviewed. A literature search involving these studies was conducted and relevant articles applying the available GWAS data were summarized to provide a comprehensive review of our current understanding of the genetic component in PAD.

RESULTS

The largest available GWAS on PAD has identified 19 genome wide significant loci, with factor V Leiden and genes responsible for circulating lipoproteins being implicated in the development of PAD. Mendelian randomization (MR) studies have identified risk factors and causal associations with smoking, diabetes, and obesity and many other traits; protein-based MR has also identified circulating lipid and clotting factor levels associated with the incidence of PAD. Polygenic risk scores may allow for improved prediction of disease incidence and allow for early identification of at-risk patients but more work needs to be done to validate this approach.

CONCLUSIONS

Genetic epidemiology has allowed for an increased understanding of PAD in the past decade. Genome-wide association studies have led to improved detection of genetic contributions to PAD, and further genetic analyses have validated risk factors and may provide options for improved screening in at-risk populations. Ongoing biobank studies of chronic limb threatening ischemia patients and the increasing ancestral diversity in biobank enrollment will allow for even further exploration into the pathogenesis and progression of PAD.

Association of genetic variants in CYP3A5, DRD2 and NK1R with opioid overdose

In 2023, 3651 Ohioans died because of an opioid overdose. Of those opioid overdoses, 3579 (98%) of which were attributed to fentanyl. We evaluated the association between 180 candidate single nucleotide polymorphisms (SNPs) and self-reported, nonfatal opioid overdose history from a prospective sample of 1301 adult patients (≥18 years of age) seen in three urban emergency departments in Ohio. Candidate SNPs included 120 related to the dopamine reward pathway and 60 related to pharmacokinetics. Of the 821 patients who reported having been exposed to opioids in their lifetime, 95 (11.6%) also reported having experienced an opioid-related overdose. Logistic regression, adjusting for age and biologic sex, was used to characterize the association between each SNP and opioid overdose, correcting for multiple comparisons. Three SNPs, located in three different genes were associated with opioid overdose: increased odds with CYP3A5 (rs776746) and DRD2 (rs4436578), and decreased odds with NKIR (rs6715729). Homozygotic CYP3A5 (rs776746) had the highest adjusted odds ratio (OR) of 6.96 (95% CI [2.45, 29.23]) and homozygotic NK1R (rs6715729) had the lowest OR of 0.28 (95% CI [0.14, 0.54). Given that CYP3A5 (rs776746) has been associated with increased plasma concentrations of fentanyl, rs776746 could potentially be utilized as a prognostic risk indicator for the potential of an opioid overdose. NK1R regulates the expression of the neurokinin-1 receptor, a regulator of respiration and NK1R (rs6715729) represents a novel genetic marker for a decreased risk of opioid overdose risk.

Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α = 0.001. Notably, when 5 % or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31 % of African Ancestry, mean age of 62, with 58 % women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on both pooled samples and within each ancestry group. Our results suggest that mtDNA-encoded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the RNR1 and RNR2 genes (p < 0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations (p < 0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

Depletion of loss-of-function germline mutations in centenarians reveals longevity genes

While previous studies identified common genetic variants associated with longevity in centenarians, the role of the rare loss-of-function (LOF) mutation burden remains largely unexplored. Here, we investigated the burden of rare LOF mutations in Ashkenazi Jewish individuals from the Longevity Genes Project and LonGenity study cohorts using whole-exome sequencing data. We found that centenarians had a significantly lower burden (11-22%) of LOF mutations compared to controls. Similar effects were also observed in their offspring. Gene-level burden analysis identified 35 genes with depleted LOF mutations in centenarians, with 14 of these validated in the UK Biobank. Mendelian randomization and multi-omic analyses on these genes identified RGP1, PCNX2, and ANO9 as longevity genes with consistent causal effects on multiple aging-related traits and altered expression during aging. Our findings suggest that a protective genetic background, characterized by a reduced burden of damaging variants, contributes to exceptional longevity, likely acting in concert with specific protective variants to promote healthy aging.

Mitochondrial haplogroup R offers protection against obesity in Kuwaiti and Qatari populations

Background

The Kuwaiti and Qatari populations have a high prevalence of obesity, a major risk factor for various metabolic disorders. Previous studies have independently explored mitochondrial DNA (mtDNA) variations and their association with obesity in these populations. This study aims to investigate the role of mtDNA haplogroups and variants in obesity risk among these Gulf populations.

Methods

Whole exome sequencing data from 1,112 participants (348 Kuwaitis and 764 Qataris) were analyzed for mtDNA variants. Participants were classified as obese or non-obese based on body mass index (BMI). Association analyses were performed to examine the relationship between mtDNA haplogroups and obesity, adjusting for covariates such as age and sex.

Results

Haplogroup R was found to be protective against obesity, with an odds ratio (OR) of 0.69 (p = 0.045). This association remained significant after adjusting for age and sex (OR = 0.694; 95% CI: 0.482-0.997; p = 0.048). Several mtDNA variants, particularly those involved in mitochondrial energy metabolism, showed nominal associations with obesity, but these did not remain significant after correcting for multiple testing.

Conclusion

Haplogroup R consistently demonstrates a protective association against obesity in both Kuwaiti and Qatari populations, highlighting its potential as a biomarker for obesity risk in the Gulf region. However, further research with larger sample sizes is needed to validate these findings and clarify the role of mtDNA variants in obesity.

The performance of AlphaMissense to identify genes influencing disease

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.

Multicategory Survival Outcomes Classification via Overlapping Group Screening Process Based on Multinomial Logistic Regression Model With Application to TCGA Transcriptomic Data

Objectives

Under the classification of multicategory survival outcomes of cancer patients, it is crucial to identify biomarkers that affect specific outcome categories. The classification of multicategory survival outcomes from transcriptomic data has been thoroughly investigated in computational biology. Nevertheless, several challenges must be addressed, including the ultra-high-dimensional feature space, feature contamination, and data imbalance, all of which contribute to the instability of the diagnostic model. Furthermore, although most methods achieve accurate predicted performance for binary classification with high-dimensional transcriptomic data, their extension to multi-class classification is not straightforward.

Methods

We employ the One-versus-One strategy to transform multi-class classification into multiple binary classification, and utilize the overlapping group screening procedure with binary logistic regression to include pathway information for identifying important genes and gene-gene interactions for multicategory survival outcomes.

Results

A series of simulation studies are conducted to compare the classification accuracy of our proposed approach with some existing machine learning methods. In practical data applications, we utilize the random oversampling procedure to tackle class imbalance issues. We then apply the proposed method to analyze transcriptomic data from various cancers in The Cancer Genome Atlas, such as kidney renal papillary cell carcinoma, lung adenocarcinoma, and head and neck squamous cell carcinoma. Our aim is to establish an accurate microarray-based multicategory cancer diagnosis model. The numerical results illustrate that the new proposal effectively enhances cancer diagnosis compared to approaches that neglect pathway information.

Conclusions

We showcase the effectiveness of the proposed method in terms of class prediction accuracy through evaluations on simulated synthetic datasets as well as real dataset applications. We also identified the cancer-related gene-gene interaction biomarkers and reported the corresponding network structure. According to the identified major genes and gene-gene interactions, we can predict for each patient the probabilities that he/she belongs to each of the survival outcome classes.