Genetic alterations influence biological function through a variety of molecular mechanisms. While common functional descriptions (such as loss of function and gain of function) are useful, they may fail to capture mechanistic complexity, particularly in cases of pleiotropy and context-dependent variant effects. To improve variant interpretation and classification, we propose a framework incorporating "context qualifiers" to address mechanistic specificity. This perspective explores the limitations of common functional descriptors and discusses the criteria needed to implement context qualifiers in variant interpretation frameworks to enhance precision medicine applications.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTLs) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we conducted analyses of emulated trials for 11 drugs across 290 comparisons and identified three drugs significantly associated with reduced colorectal cancer risk: caffeine vs. paroxetine (hazard ratio [HR], 0.51; 95% confidence interval [CI], 0.41-0.64), haloperidol vs. prochlorperazine (HR, 0.47; 95% CI, 0.33-0.68), and trazodone hydrochloride vs. paroxetine (HR, 0.49; 95% CI, 0.38-0.63). Conversely, caffeine was associated with increased cancer risk in comparison with finasteride (colorectal cancer) and fluoxetine (breast cancer). Meta-analysis identified six drugs significantly associated with cancer risk, including acetazolamide, which was associated with reduced colorectal cancer risk (HR, 0.79; 95% CI, 0.72-0.87). This study identifies previously unreported protein biomarkers and candidate drug targets across six major cancer types and highlights several approved drugs with potential chemopreventive effects.

The liver is a central regulator of cardiometabolic physiology, coordinating processes such as lipid and glucose metabolism, protein synthesis, and detoxification. Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with cardiometabolic traits, yet their molecular mechanisms in liver cell types remain unclear. Using multiome gene expression and accessible chromatin sequencing on liver samples from 39 individuals, we profiled gene expression and chromatin accessibility in 68,398 nuclei across six primary liver cell types. We identified 306,706 accessible chromatin regions, including 70,884 regions that were undetected in bulk tissue analyses and predominantly represent less abundant cell types. To identify genetic effects on gene regulation in liver cell types, we mapped quantitative trait loci (QTLs) and detected 1,885 chromatin accessibility QTLs (caQTLs) and 67 expression QTLs (eQTLs). We integrated cell-type QTLs with GWAS signals and revealed cell types, genes, and chromatin regulatory elements involved in cardiometabolic traits, such as liver enzyme and cholesterol levels. Non-hepatocyte cell-type QTL analyses exposed previously obscured mechanisms, such as an eQTL for ADAMTS12 in liver sinusoidal endothelial cells potentially involved in liver fibrosis, demonstrating that single-nucleus approaches can capture regulatory events missed in bulk analyses. Furthermore, we predicted the cell type of action for bulk liver caQTLs colocalized with GWAS signals, enhancing mechanistic insights for complex trait associations. Our findings provide a high-resolution map of the hepatic regulatory landscape and advance the understanding of cellular contexts and molecular mechanisms underlying cardiometabolic traits.

Paleogenomics is an increasingly data-rich research discipline that has become hugely influential in our understanding of the population history of humans and many other species. Given its reliance on destructive sampling to extract ancient DNA (aDNA) from skeletal remains and other organic sources, minimum reporting standards are crucial to inform the review process, improve transparency and reproducibility, increase the usability of research findings, and maximize the value gained from finite and unique samples. Additionally, paleogenomics researchers routinely face choices that can meaningfully impact results and influence conclusions, including decisions regarding sample usage and parameter options for data processing and analyses. From our collective experience as a paleogenomics research group extensively interacting with other researchers in the field, we identified critical information for key analytical areas required for reproducible research. Our recommendations are compatible with findable, accessible, interoperable, and reusable (FAIR) and collective benefit, authority to control, responsibility, and ethics (CARE) frameworks for data management and include the use of standardized data formats, amendments to standard metadata formats, and the provision of a reporting checklist detailing sample preparation and analytical workflows. Developing detailed documentation and clear reporting of analytical workflows will ensure that transparent, robust, and reproducible conclusions are routinely achieved to warrant confidence in paleogenomics research outcomes.

Genome-wide association studies (GWASs) have identified thousands of putative disease-causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show that current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a pipeline for sQTL discovery. MAJIQTL includes two statistical methods: a weighted multiple-testing approach for sGene discovery and a model for sQTL effect-size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the variant rs528823 in Alzheimer disease. Using antisense oligonucleotides, we test this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in MS4A3.

Circulating proteins play essential roles in complex diseases, yet protein quantitative trait locus (pQTL) studies in non-European, isolated populations remain limited. We analyzed genotypes and plasma proteomics data (Olink Target 96 Inflammation and Cardiovascular II panels) from 3,707 Greenlandic individuals (mean age: 47.9 years; 54.5% female), using linear mixed models to account for relatedness and population structure. Among 177 proteins, we identified 251 primary pQTLs-235 additive (84 cis, 8 semi-cis, 12 semi-trans, and 131 trans) and 16 recessive (1 cis, 2 semi-trans, and 13 trans)-48 secondary pQTLs, and 70 (28%) novel associations. Several common pQTLs in Greenlanders explained a substantial proportion of variance in protein levels (>30% for interleukin [IL]-27, IgG Fc receptor II-b, IL-16, and Galectin-9) compared to Europeans. A novel cis pQTL for IL-6 (rs7802307) was associated with increased cardiovascular disease risk based on registry data. Associations between Arctic-enriched variants in CPT1A (rs80356779), HNF1A (rs2135845768), TBC1D4 (rs61736969), LDLR (rs730882082), and PCSK9 (rs4609471) and altered protein abundance provide mechanistic insights into cardiometabolic disease in this population. These findings underscore the importance of pQTL studies in genetically diverse populations.

Protein arginine methyltransferase 9 (PRMT9) is part of the PRMT family, and it is suspected to function in pathways relevant to neurodevelopment. It is thought to participate in alternative splicing through interactions with the splicing factor SF3B2 (SAP145). In this study, we report 26 families (35 individuals) with bi-allelic loss-of-function variants in PRMT9, implicating PRMT9 in an autosomal-recessive human disease. Individuals primarily present with a neurodevelopmental disorder characterized by global developmental delay, learning disabilities, mild to severe intellectual disability, autism spectrum disorder, epilepsy, and hypotonia. The mutation spectrum includes 26 different variants such as frameshifting indels, nonsense variants, missense variants, and two copy-number variants. Mapping of the disease-causing missense variants onto the crystal structure of PRMT9 revealed that several of the variants reside within the catalytically active module of PRMT9, likely impairing its methyltransferase activity and resulting in a loss of function. In skin fibroblasts derived from affected individuals, we observed reduced expression at the RNA and/or protein level and subsequent aberrant methylation activity. Moreover, transcriptomic analysis of fibroblasts from affected individuals indicated differential expression of genes related to intellectual disability, autism, and cilia, suggesting a role of PRMT9 during ciliogenesis. Under ciliogenesis conditions, the skin-derived fibroblasts exhibited anomalies in the length of primary cilia but normal amounts of cilia. In addition, a prmt9 knockout zebrafish model displayed abnormal social preference in adult animals. Altogether, our findings implicate bi-allelic PRMT9 loss-of-function variants as causal for neurodevelopmental disorders.

The orphan nuclear hormone receptor estrogen-related receptor γ (ESRRG, also known as ERRγ) functions as an inducible transcription factor, regulating the expression of endocrine and metabolic genes. Among its ESRR paralogs, ESRRG exhibits the highest mutational constraint, yet it remains unlinked to a defined disease phenotype. We clinically describe eight individuals from seven unrelated families having heterozygous, mostly de novo variants in ESRRG: c.410G>A (p.Gly137Glu), c.446A>G (p.Lys149Arg), c.539G>A (p.Cys180Tyr), c.550C>T (p.Arg184Cys), c.1346T>G (p.Leu449Arg), and c.1352dup (p.Leu451Phefs38). Cell proliferation and ERR response element (ERRE) reporter gene expression have been examined in transient transfected ESRRG-knockout HEK293T cells for each variant compared to wild-type ESRRG. Immunofluorescence was performed to inspect the protein's subcellular localization. All identified variants are absent in gnomAD (v.4.1), are in silico deleterious, and are located in intolerant segments of the protein. All individuals have motor developmental delay, muscular hypotonia, and eye movement disorders, as well as congenital ataxia or gait imbalance. Other symptoms include joint hyperflexibility, dysarthria, myopia, and growth delay. Molecular modeling of the identified variants suggests a reduction in protein function. ESRRG knockout resulted in a significantly increased proliferation rate in ESRRG-knockout HEK293T cells. Overexpression of wild-type ESRRG restored cell proliferation, while overexpression of the identified variants did not. Despite the correct nuclear localization of all identified variants, the reporter gene assay revealed a significant reduction in transcriptional activity for all identified variants. Given the acquired clinical, molecular, and functional data, we implicate ESRRG in the etiology of a non-progressive congenital movement disorder with ataxia.

Public health genetics training programs must evolve to meet the changing public health landscape and ever-growing data availability. Yet, the field's interdisciplinary nature poses challenges to training program development. Here, we conducted focus groups (n = 7) with public-health-genetics-related professionals (n = 55) to learn about the skills and knowledge needed for a career in public health genetics. Additional discussion topics included perspectives on curriculum structure, analytic skills, and unneeded content. Focus group transcripts underwent thematic analysis, from which competencies and pedagogical recommendations were derived. Informants across academia, government, and industry represented specialties in bioethics, policy, genetic epidemiology, medical genetics, statistical genetics, and law. Discussions led to 17 competencies that group into four learning goals: (1) foundational knowledge in genetics and public health (e.g., principles of population genetics and history of unethical genomic practices), (2) analytical proficiency (e.g., managing qualitative and quantitative datasets), (3) application of interdisciplinary methods to research design and practice (e.g., developing research questions at the intersection of genomics and health services), and (4) cross-disciplinary collaboration (e.g., cultural and disciplinary humility in communication). Informants recommended pedagogical priorities including strong mentorship, broad core requirements plus specialization tracks, and hands-on, interdisciplinary projects. Conceptual knowledge and critical thinking were emphasized over technical proficiency in specific methods. We translated the recommendations from current experts into guidance for training programs that will best meet the evolving needs of the public health genetics field. Training should emphasize broad foundational knowledge and team science skills to facilitate cross-disciplinary communication and meet emerging challenges.

Large-scale data sharing is indispensable for advancing human genetics and genomics (HGG) research and medicine. The willingness of study participants and researchers to share data has been the foundation of rapid advancements of the HGG field for decades. If the potential benefits of HGG research are to be fully realized, maintaining a broad data-sharing ethos and policy environment is vital. ASHG reasserts the HGG community's commitment to responsible data stewardship and emphasizes the role of researchers in working alongside other interested parties to create a secure research enterprise without imposing undue restrictions on researchers' ability to collaborate, share data, and advance knowledge while also protecting the will and privacy of participants. This balance is key to building sustainable partnerships and a cohesive information ecosystem.

Loss-of-function variants (LoFs) can result in severe clinical phenotypes, including both autosomal-recessive and -dominant Mendelian diseases. Except for a handful of unusually common variants, however, their lifetime risk for disease expression is unknown. This is particularly true for LoFs in genes linked to autosomal-dominant diseases driven by haploinsufficiency, which represent some of the most common monogenic disorders. Here, we investigate the disease-expression rates for >6,000 predicted LoFs (pLoFs) linked to 91 haploinsufficient diseases using the electronic health records (EHRs) of ∼24,000 pLoF heterozygotes isolated from two population-scale biobanks (the UK Biobank and the All of Us Research Program). Consistent with prior analyses, most pLoF heterozygotes displayed no evidence for disease expression, a phenomenon that persisted after accounting for variant annotation artifacts, missed diagnoses, and incomplete clinical data. While it is infeasible to completely remove all the artifacts and biases from EHR data, we hypothesized that many of these pLoFs have intrinsically low or even no penetrance, which may be driven by residual allelic activity. To test this, we trained machine-learning models to predict disease-expression risk for pLoFs using only their genomic features. In validation experiments, the models were predictive of pLoF disease-expression rates across a range of diseases and variants, including those previously annotated as pathogenic by diagnostic-testing laboratories. This suggests that many pLoFs have intrinsically incomplete or even no penetrance (i.e., are benign) due to residual allelic activity, complicating prognostication in asymptomatic individuals.

Intron retention is a type of alternative splicing in which introns remain unspliced in mature RNA transcripts. In order to explore the landscape and consequences of genetically regulated intron retention, we perform an intron retention quantitative trait locus (irQTL) analysis in 49 human tissues across 838 individuals. We identify 8,624 unique intron retention events associated with genetic polymorphisms. 1,369 irQTLs (16%) are also associated with genome-wide association study (GWAS) traits. 1,999 irQTLs (23%) colocalize with expression QTLs (eQTLs) to their respective gene. We demonstrate that irQTLs are sufficient to generate eQTLs when one of the alternatively spliced transcripts is preferentially targeted by the nonsense-mediated decay (NMD) pathway. Surprisingly, for intron retention events whose potential NMD effects can be confidently predicted based on their positions within known gene annotations, we find that 58.8% (923/1,570) of the colocalized irQTL and eQTL pairs show effect-size directions that are discordant with the NMD model. Moreover, we find that irQTLs are significantly more likely to occur in the same gene with the same effect-size direction as compared to exon-skipping QTLs. Through mathematical modeling and analysis of experimental perturbation data, we provide evidence that eQTLs are able to generate irQTLs by altering the steady-state ratios of spliced and unspliced transcripts, and we postulate that this mechanism may partially underlie the widespread intron retention observed previously in various biological conditions. Taken together, these results show that intron retention and steady-state gene expression levels are closely intertwined to regulate phenotypic traits.

Understanding genetic architecture across ancestries through genetic correlation analysis is critical for determining the degree to which genetic factors underlying diseases or complex traits are shared or differ among populations. Current methods for genetic correlation analysis primarily rely on method of moments approaches and focus on estimating the global genetic correlation across the entire genome. However, these methods often overlook important local genomic complexities and inadequately model the intricate linkage disequilibrium (LD) structures that vary substantially across ancestries. Here, we present Logica (local genetic correlation across ancestries), a method specifically designed to estimate local genetic correlations across ancestries and in admixed populations. Logica employs a bivariate linear mixed model that explicitly accounts for diverse LD patterns across ancestries, operates on genome-wide association study summary statistics, and utilizes a maximum-likelihood framework for robust inference. An important by-product of Logica is a joint heritability test across ancestries that yields well-calibrated p values-an aspect that existing approaches often struggle with. We conducted comprehensive evaluations of Logica through realistic simulations and analyses of 13 complex traits from multiple biobanks. Simulations showed that Logica achieves improved accuracy in local genetic correlation estimation (with mean squared errors 2.23-4.13 times lower) and enhanced power for detecting genetically correlated regions (8%-40% increase with controlled false discovery rate [FDR] at 5%). In real data, Logica produced valid genetic correlation estimates across all genomic regions, whereas existing methods failed in 23%-39% of regions. Additionally, Logica exhibited better FDR control (14%-58% improvement), identifying genetically correlated regions with greater functional relevance.

The ribosomal protein S6 kinase family members play essential biological functions in disease, from cancer to intellectual disability. Little is known about ribosomal proteins S6 kinase C1 (RPS6KC1), aside from its lack of phosphorylation capacity and its roles in sphingosine-1-phosphate signaling and peroxiredoxin-3 (PRDX3) transport to mitochondria. Through whole-exome sequencing, we identified bi-allelic RPS6KC1 variants in 13 individuals from 8 independent families. Phenotypic manifestations included neurodevelopmental delay, hypotonia, spastic paraplegia, brain white matter loss, and dysmorphic features overlapping with Coffin-Lowry syndrome caused by RPS6KA3 mutations. Functional studies on peripheral blood mononuclear cells (PBMCs) from the different individuals indicated diminished expression and phosphorylation of RPS6, impacting ribosomal protein synthesis, and a decrease in the known interactors PRDX3 and sphingosine kinase 1 (SPHK1), accompanied by marked repression of the mammalian target of rapamycin (mTOR)/phosphatidylinositol 3-kinase (PI3K) pathway. We detected a dysregulation of phosphoinositides and sphingoid base levels in plasma samples from the different individuals. Further studies in HAP1 RPS6KC1-knockdown cells suggested that RPS6KC1 may regulate PRDX3 and SPHK1 activities by facilitating their endosome anchoring. In Drosophila melanogaster, the knockdown of CG7156, the RPS6KC1 ortholog, resulted in locomotor dysfunction, defective neuromuscular junctions, reduced lifespan, and decreased mTOR activity. Overexpression of mTOR in this model improved motor function and lifespan. These findings underscore the crucial roles of RPS6KC1 in neurodevelopment by controlling ribosomal protein synthesis, lipid signaling, and the mTOR pathway.

Most Alzheimer disease (AD) susceptibility genes have poorly understood roles in the central nervous system (CNS). To address this gap, we systematically characterized 100 conserved candidate AD risk genes using a cross-species strategy in the fruit fly, Drosophila melanogaster. Genes were prioritized based primarily on human functional genomic evidence. We generated custom loss-of-function alleles for each of the conserved fly orthologs. Most of the genes are expressed in the adult brain, including 24 neuron- and 13 glia-specific expression patterns. Overall, we identify 50 candidate AD risk gene homologs with requirements for CNS structure or function, including 18 whose loss of function causes neurodegeneration (e.g., Snx6/SNX32 and ClC-a/CLCN1), 35 required for neurophysiology (e.g., Arr1/ARRB2 and stai/STMN4), and eight with diminished CNS resilience following a thermal or mechanical stress (e.g., cindr/CD2AP and Amph/BIN1). In a parallel screen, we found 28 AD risk gene homologs (e.g., Ets98B/SPI1 and Yod1/YOD1) that modify the neurotoxicity of either amyloid-β peptide or tau protein, which aggregate to form AD pathology. To translate our findings back to human AD, we used oligogenic risk scores based on gene clusters with shared nervous system phenotypes in flies, pinpointing functional pathways that differentially drive AD risk. Our results-available online via the Alzheimer's Locus Integrative Cross-species Explorer portal-reveal nervous system requirements for dozens of AD risk genes and may enable dissection of causal heterogeneity in AD.

Gene genealogies represent the shared ancestry of a sample and are often encoded as ancestral recombination graphs (ARGs). It has recently become possible to infer these gene genealogies from sequencing or genotyping data and use them for many evolutionary and statistical genetics applications. Here, we use the ARG inference software ARG-needle and the pedigree imputation software ISGen to impute and trace the transmission of disease variants in founder populations where long shared haplotypes allow for accurate timing of relatedness. We applied these methods to the population of Quebec, where multiple founder events led to an uneven distribution of pathogenic variants across regions and where extensive population pedigrees are available via the BALSAC project. We validated this approach with nine founder mutations for the Saguenay-Lac-Saint-Jean region, demonstrating high accuracy for mutation age, imputation, and regional frequency estimation. We used imputed carrier status in a longitudinal cohort to highlight heterozygote effects for known recessive alleles. These heterozygote effects, together with regional frequency estimates, can inform the design of screening programs.

The recessive Fanconi anemia (FA) phenotype is used to classify BRCA1 (FANCS), BRCA2 (FANCD1), and PALB2 (FANCN) variants with respect to dominant hereditary breast-ovarian cancer syndrome. We assessed its utility by examining the phenotypic spectrum observed in individuals with bi-allelic BRCA1, BRCA2, or PALB2 pathogenic variants and exploring the relationship between cancer presentation and allele severity score based on variant molecular features. A data collection instrument comprising 158 Human Phenotype Ontology (HPO) terms was used to document clinical features for individuals with FA from published and/or prospectively collected sources (total n = 172, 43 previously unpublished). Distinct FA-related variants (15 BRCA1, 123 BRCA2, and 22 PALB2) were annotated for predicted molecular impact, location, observed splicing or functional impact, and potential in-frame splicing rescue and used to assign different permutations of allele severity scores, which were assessed for correlation with FA presentation features. The association of BRCA1 and BRCA2 allele severity score with the magnitude of breast cancer risk in heterozygotes was evaluated using case-control analysis. Clinical features extended beyond the HPO list, including 84 terms related by hierarchy and 94 additional terms. The BRCA2 genotype severity score was associated with age at cancer diagnosis in individuals with FA (p = 1.8 × 10). A similar permutation approach revealed significant differences in the magnitude of breast cancer risk according to the BRCA1 and BRCA2 allele severity score in heterozygotes. Our findings indicate the potential to redefine FA ORPHA:84 HPO terms and to use an allele severity scoring approach to predict cancer risk in individuals with bi-allelic or heterozygous BRCA1 or BRCA2 variants.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) is dedicated to integrating pharmacogenetic testing into clinical practice by developing and disseminating peer-reviewed, evidence-based gene-drug clinical practice guidelines. A critical component of this effort is the assignment of clinical function to pharmacogene alleles, which informs the translation of genetic test results into actionable prescribing decisions. This technology review outlines the standardized procedures and framework used by CPIC to assign allele clinical functional status through the work of Pharmacogene Curation Expert Panels (PCEPs). These panels, comprising multidisciplinary experts, systematically review and evaluate evidence to assign functional status to pharmacogenetic haplotypes. The process includes rigorous evidence review, use of standardized terminology, and consensus-driven functional assignments. The resulting allele functionality tables and phenotype mapping tables are essential for standardized interpretation of pharmacogenetic test results and the development of CPIC guidelines. This review of the framework used to assign clinical allele function provides transparency and encourages global participation and feedback from the pharmacogenomics community to promote the adoption of CPIC guidelines in clinical practice.

How do we go from a single individual receiving a personalized gene-editing therapy to a future of "interventional genetics" in which such therapies are the standard of care? First and foremost: regulatory innovation.