Rare Variant Association Research Articles

Unified meta regression models for rare variant association studies.

Rare variant association studies (RVAS) of complex traits have emerged as a powerful approach to advance drug discovery and diagnostics. Missense pathogenicity predictions from AlphaMissense based on structural context and protein language models improve the differentiation between benign and deleterious variants. Constraint metrics, on the other hand, allow researchers to pinpoint genomic regions under selective pressure that may not directly impact protein structure, but are more likely to contain functionally important mutations. Loss-of-function (LoF) variants, which result in the complete or partial loss of protein function, are particularly informative, as it is more straightforward to assess their downstream functional consequences. In this study, we present a unified meta regression model approach that incorporates the probability of pathogenicity, probability of constraint, and indicator whether a variant is a predicted loss-of-function or missense variant as features to model the observed effect size and uncertainty of effect size obtained from single-variant genetic analysis. We applied the unified meta regression model to 1,144 continuous phenotypes from UK Biobank using single variant summary statistics obtained from Genebass. We replicated our findings using the AllofUS cohort. For each gene discovery, we make available a characterization of whether constrained sites are associated with the phenotype, whether pathogenic sites determined by structural based predictions are associated with phenotype, and whether broader loss-of-function or missense variant annotation better explains the summary statistics observed. Our results are publicly available at Global Biobank Engine ( https://biobankengine.shinyapps.io/phenome-wide-unified-model/ ).

Genome Wide and Rare Variant Association Studies of Amblyopia in the All of Us Research Program.

Amblyopia is characterized by decreased visual acuity due to abnormal visual experience during development. It affects approximately three percent of the population and is associated with abnormal development of the visual cortex. Despite treatment, many patients have residual visual acuity deficits. This study aimed to explore the genetic contributions to amblyopia. Case-control. The All of Us Research Program includes genotypic and phenotypic data from a diverse population of adults (age ≥ 18 years) across the United States. 764 subjects with amblyopia (based on ICD and SNOMED codes) and 122,305 controls with no record of amblyopia and whole genome sequencing were compared. Only participants of European genetic ancestry were included due to small numbers of affected participants in other ancestral groups. Genome wide association study (GWAS) of common variants (minor allele frequency >1%) and rare variant association study (RVAS) at the gene level for amblyopia of participants in the All of Us Research Program. Individual single-nucleotide polymorphisms (SNPs) significantly associated with amblyopia and genes with significant burden of rare variants in amblyopia RESULTS: The GWAS revealed 4 loci that approached statistical significance defined as p = 5e-8: rs56105618, rs1349660, rs7958343, and rs138693522. Each of the variants is an expression quantitative trait locus (eQTL) for a gene expressed in the brain or related to neural development. RVAS revealed 15 genes with a statistically significant (p-value = 5e-05) different burden of variants: DCP1B, OR12D2, PCDHA4, ALKBH8, NMUR2, OR52P1P, NEU1, CACNB2, PSMA7, LRR1, ZNF831, FSIP2, ZNF654, CES5A, and MPV17, several of which have known roles in neurodevelopment. The identification of genes linked to amblyopia with roles in neurodevelopment suggests that the neurodevelopmental changes in amblyopia are not only secondary to abnormal visual experience but may result from the interaction of primary neurodevelopmental deficits with abnormal experience. This potentially explains why some children develop amblyopia and others do not with the same ocular risk factors, may explain differences in treatment outcomes, and suggests new avenues for amblyopia treatment.

Disease-associated Kv1.3 variants are energy compromised with impaired nascent chain folding.

Human Kv1.3, encoded by KCNA3 , is expressed in neuronal and immune cells. Its impaired expression or function produces chronic inflammatory disease and autoimmune disorders, the severity of which correlates with Kv1.3 protein expression. The intersubunit recognition domain, T1, at the cytosolic N-terminus of Kv1.3, acquires secondary, tertiary, and quaternary structures during early biogenesis while the nascent protein is attached to the ribosome and/or the ER membrane. In this study, we ask whether native KCNA3 gene variants in T1 are associated with human disease and whether they manifest early-stage folding defects, energetic instabilities, and conformational distortion of subunits. We use three approaches: first, the unbiased "genome-first" approach to determine phenotype associations of specific KCNA3 rare variants. Second, we use biochemical assays to assess early-stage tertiary and quaternary folding and membrane association of these variants during early biogenesis. Third, we use all-atom molecular dynamics simulations of the T1 tetramer to assess structural macroscopic and energetic stability differences between wildtype (WT) Kv1.3 and a single-point variant, R114G. Measured folding probabilities and membrane associations are dramatically reduced in several of the native variants compared to WT. Simulations strikingly show that the R114G variant produces more energetically unstable and dynamic T1 domains, concomitant with tertiary unwinding and impaired formation of symmetrical tetramers. Our findings identify molecular mechanisms by which rare variants influence channel assembly, potentially contributing to diverse clinical phenotypes underlying human disease.

Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants

BackgroundWith the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address this, we previously proposed a set of burden- and kernel-based rare variant tests for count data following zero-inflated Poisson (ZIP) distributions, referred to as ZIP-b and ZIP-k tests. We sought to extend the methods to accommodate negative binomial distribution and implemented these tests in a new R package.ResultsWe introduce ZIM4rv, an R package designed to analyze the association of rare variants with zero-inflated counts outcomes. Our package offers two novel models developed by our team: our previously proposed ZIP-b and ZIP-k tests, and the newly derived Negative Binomial Burden and Kernel Test (ZINB-b, ZINB-k). Additionally, we include an ad-hoc two-stage analysis, testing zero and non-zero as a binary outcome and non-zero as a continuous outcome, respectively. To showcase the utility of our platform, we applied this program to analyze neuritic plaque count data from the ROSMAP cohort.ConclusionThe R package ZIM4rv presents an integrated workflow for conducting association tests on a set of rare variants with zero-inflated counts data.

Rare and common genetic variants underlying the risk of Hirschsprung's disease.

Hirschsprung's disease (HSCR) is a congenital enteric neuropathic disorder characterized by high heritability (>80%) and polygenic inheritance (>20 genes). The previous genome-wide association studies (GWAS) identified several common variants associated with HSCR and demonstrated increased predictive performance for HSCR risk in Europeans using a genetic risk score, there remains a notable gap in knowledge regarding Chinese populations. We conducted whole exome sequencing in a HSCR case cohort in Chinese. By using the common controls (505 controls from 1KG EAS and 10 588 controls from ChinaMAP), we conducted GWAS for the common variants in the exome and gene-based association for rare variants. We further validated the associated variants and genes in replicated samples and in vitro and vivo experiments. We identified one novel gene PLK5 by GWAS and suggested 45 novel putative genes based the gene-based test. By using genetic variant at RET and PLK5, we constructed a genetic risk score that could identify the individuals with very high genetic risk for HSCR. Compared with patients with zero or one risk allele from the three variants, the risk for HSCR was 36.61 times higher with six alleles. In addition, we delineated a HSCR risk gene landscape that encompasses 57 genes, which explains 88.5% and 54.5% of HSCR in Chinese and European, respectively. In summary, this study improved the understanding of genetic architecture of HSCR and provided a risk prediction approach for HSCR in the Chinese.

Rare variant associations with birth weight identify genes involved in adipose tissue regulation, placental function and insulin-like growth factor signalling

Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. We report analyses of rare variants that impact birth weight when carried by either fetus or mother, using whole exome sequencing data in up to 234,675 participants. Rare protein-truncating and deleterious missense variants are collapsed to perform gene burden tests. We identify 9 genes; 5 with fetal-only effects on birth weight, 1 with maternal-only effects, 3 with both, and observe directionally concordant associations in an independent sample. Four of the genes were previously implicated by GWAS of birth weight. IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (fetal-acting) are involved in adipose tissue regulation, and the latter two also show associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG (fetal-acting) in adipocyte differentiation and placental angiogenesis. NOS3 (fetal and maternal-acting), NRK (fetal), and ADAMTS8 (maternal-acting) have been implicated in placental function and hypertension. To conclude, our analysis of rare coding variants identifies regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, and further evidence for the role of insulin-like growth factors.

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.

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.

Study design and the sampling of deleterious rare variants in biobank-scale datasets.

One key component of study design in population genetics is the "geographic breadth" of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number variants as compared geographically narrow samples (an effect we label "discovery"); though the variants will be detected at lower average frequency than in narrow samples (e.g. as singletons, an effect we label "dilution"). Importantly, these effects are amplified for larger sample sizes and moderated by the magnitude of fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale.

Genetic variants associated with age-related episodic memory decline implicate distinct memory pathologies.

Approximately 40% of people aged ≥ 65 experience memory loss, particularly in episodic memory. Identifying the genetic basis of episodic memory decline is crucial for uncovering its underlying causes. We investigated common and rare genetic variants associated with episodic memory decline in 742 (632 for rare variants) Ashkenazi Jewish individuals (mean age 75) from the LonGenity study. All-atom molecular dynamics simulations were performed to uncover mechanistic insights underlying rare variants associated with episodic memory decline. In addition to the common polygenic risk of Alzheimer's disease, we identified and replicated rare variant associations in ITSN1 and CRHR2. Structural analyses revealed distinct memory pathologies mediated by interfacial rare coding variants such as impaired receptor activation of corticotropin releasing hormone and dysregulated L-serine synthesis. Our study uncovers novel risk loci for episodic memory decline. The identified underlying mechanisms point toward heterogenous memory pathologies mediated by rare coding variants. We demonstrated the contribution of the common polygenic risk of Alzheimer's disease to episodic memory decline. We discovered and replicated two risk genes associated with episodic memory declineimplicated by rare variants, were discovered and replicated. We demonstrated molecular mechanisms and potential novel memory pathologies underlying interfacial rare coding variants. Molecular dynamics simulations were performed to understand the downstream effects of risk rare coding variants.

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Abstract Background Conventional analysis of the electrocardiogram (ECG) is based on the extraction of human-defined, visually recognisable features. However, these are not optimised to capture all the information content in the ECG. The variational autoencoder (VAE), a form of unsupervised machine learning, can address this shortcoming by computationally extracting comprehensive and interpretable new ECG features, called latent factors. These latent factors provide a low-dimensional representation of the ECG that maximises capture of data content. This approach could expand our understanding of electrophysiology by identifying novel genetic and phenotypic traits associations with the ECG. Purpose This study aims to uncover genetic determinants of VAE latent factors, comparing them to determinants of conventional, human-derived ECG parameters to gain novel insights into cardiac electrophysiology and related diseases. Methods Over one million median beat ECGs derived from a United States (US) based secondary care centre were used to train a machine learning VAE model, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes and echocardiographic traits. Results Utilising median beat ECGs, the VAE model identified 20 features, with partial correlations to traditional ECG traits. A genome-wide association study (GWAS) identified 105 associated genomic regions, compared with 62 regions identified with conventional ECG traits on the same dataset. Most discovered loci have been previously associated with ECG features in larger GWAS, providing a positive control validation. We also discovered and validated five genomic regions not previously linked to the ECG (mapped to TRIOBP, EFEMP1, NEDD9, ATP10A and P2RX1). We found rare variant associations for seven genes (NEK6, IL17RA, NME7, MYBPC3, CCT8, ADAMTS6 and SCN5A). The latent factors uncovered associations with 158 phenotypic traits (2093 phenotypes tested) and 147 disease codes (2074 disease phecodes tested) which were not identified by ECG traits. Out of 38 echocardiographic traits, 35 showed higher correlations with the latent factors, while six exhibited significant associations exclusively with the latent factors. Conclusion Our study demonstrates that VAE-derived latent factors outperform conventional ECG parameters in uncovering novel genetic and phenotypic associations. Our findings underscore the opportunity for optimised data-driven feature extraction to enhance our understanding of cardiac electrophysiology.

Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data

Abstract Background Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterised by left-ventricular dilatation and reduced contractility, often associated with arrhythmia and conduction disease. DCM often has a genetic component, putting family members of diagnosed patients at risk for the disease. Stratification tools to provide personalized screening advice are currently lacking. Artificial intelligence (AI) based analysis of the electrocardiogram (ECG) offers a potential solution to this challenge. Purpose Our study aims to develop and validate a novel AI-ECG model capable of prediction and early detection of DCM. Methods An AI-ECG model was developed on a United States (US) based secondary care centre dataset including 337,463 ECGs from 50,932 patients within 4 months of diagnostic echocardiography and 265,576 ECGs from 51,086 patients prior to the first diagnostic echo. The data was split 50/10/40% for training, validation, and testing, with each subset containing 9-12% DCM positive labels. The model architecture was based on a convolutional neural network with residual blocks with a modified final layer for a discrete-time survival model. The AI-ECG score was linked to changes in the median ECG within the test set to provide explainability. External validation was performed on 68,885 diagnostic ECGs (0.08% DCM positive) from the UK biobank (UKB) and a Chinese general hospital dataset with 317,854 diagnostic ECGs (2.2% DCM positive) and 76,936 predictive ECGs (1.7% DCM positive). We performed common and rare variant association studies for the AI ECG scores on quality-controlled UKB data. Results The AI-ECG score for predicted future DCM was associated with diverse ECG features (Fig. 1). In the US cohort test set, the model achieved high diagnostic accuracy with an AUC of 0.9 (95% CI 0.89-0.90) overall, and 0.89 (0.85-0.92) in a subset excluding ischemic or valvular heart disease. External datasets showed AUCs of 0.86 (0.81-0.91) in the UKB and 0.97 (96-0.97) in the Chinese dataset. The predictive performance of the AI-ECG score, adjusted for age and sex, closely approximated echocardiographic traits (LVEF and LVEDD, Fig. 2) with a concordance index (CI) of 0.79 (0.76-0.83) and 0.81 (0.77-0.85), respectively, at 10 years of follow up (FU). Combining AI-ECG scores with echocardiographic traits enhanced the CI to 0.84 (0.8-0.87). Similar findings were observed in the Chinese dataset, with a CI of 0.87 (0.85-0.89) for the AI-ECG score and 0.9 (0.88-0.92) for echocardiographic traits. Genetic analyses in UKB identified associations between the AI-ECG score and rare TTN variants as well as 25 common variant loci. Conclusion Our explainable AI ECG model for future DCM prediction demonstrates robust performance across datasets, providing comparable predictive accuracy to echocardiographic traits up to 10 years pre-diagnosis. Genetic associations support the model's validity and provide new tools for DCM genetic discovery.

Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum.

Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, which affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (P-value < 5×10 -8 ). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes ( MYBPC3 , BAG3 ) and in regulators of lipoprotein ( LPL ) and glucose metabolism ( GIPR , GLP1R ). These signals are enriched in myocyte and adipocyte cell types and can be clustered into 5 broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity, and arrhythmias. Gene burden studies across three biobanks (PMBB, UKB, AOU), including 27,208 individuals with HF and 349,126 without, uncover exome-wide significant (P-value < 1.57×10 -6 ) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN , MYBPC3 , FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common variant heritability (4.3%, 95% CI 3.9-4.7%) is more diffusely spread throughout the genome. Finally, we show that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. Together, these findings provide a genetic link between dysregulated metabolism and HF, and suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.

Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project.

Alzheimer's disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. We investigated the association of AD with both common variants and aggregates of rare coding and non-coding variants in 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data. Pooled-population analyses of all individuals identified genetic variants at apolipoprotein E (APOE) and BIN1 associated with AD (p<5×10-8). Subgroup-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and non-coding variants in the region. Common variants in LINC00320 were observed associated with AD in Black individuals (p=1.9×10-9). Finally, we observed rare non-coding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). We observed that complementary pooled-population and subgroup-specific analyses offered unique insights into the genetic architecture of AD. We determine the association of genetic variants with Alzheimer's disease (AD) using 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data. We identified genetic variants at apolipoprotein E (APOE), BIN1, PSEN1, and LINC00320 associated with AD. We observed rare non-coding variants in the promoter of TOMM40 distinct of APOE.

Relation of mitochondrial DNA copy number and variants with the clinical characteristics of polycystic ovary syndrome

Mounting evidences suggests mitochondrial dysfunction as a novel contributor in the pathogenesis of PCOS. Herein, we analyzed mtDNA copy number, a biomarker of mitochondrial function in women with PCOS and non-PCOS participants and study its correlation with their clinical characteristics. In this study, we further analyzed association of 383 mtDNA variants, as reported previously by us, with characteristic traits of PCOS and perform structural analysis of mutated protein. Our results indicate relative mitochondrial DNA (mtDNA) copy number to be significantly reduced in women with PCOS compared to non-PCOS group and significantly inversely related to waist to hip ratio (WHR), triglycerides and positively related to high density lipoprotein-cholesterol (HDL-C). After adjustment of the age in the PCOS group, significantly negative correlation of mtDNA copy number with WHR was observed. Unsupervised hierarchical clustering analysis revealed rare, low heteroplasmic mtDNA variants such as 12556G, 1488T, 9200G, 9670G, 3308G, 14480G, 15914T and 5426G to be strongly associated with PCOS related traits. Among these variants, variant 12256G in ND5 gene affected both the flexibility and overall stability of the protein structure. This study is first to reveal significant correlation of mtDNA copy number with WHR in women with PCOS indicating link between mitochondrial dysfunction with central obesity in PCOS. we also first time showed association of rare mtDNA variants with characteristics traits of PCOS highlighting the clinical significance of rare mtDNA variants, which may cumulatively act as early predictors of risk of PCOS and its related comorbidities which may help in the management of PCOS.

Cross-ancestry analysis identifies genes associated with obesity risk and protection.

Gene discoveries in obesity have largely been based on European cohorts, leading to an ancestral bias, that limits their generalizability across populations. We performed a gene-based rare variant association study of 721,941 individuals and identified 116 novel BMI-associated genes with consistent effects across ancestries, including 50 risk-conferring and 66 protective genes against obesity. Protective genes such as DCUN1D3 and NEUROD6 had effect sizes comparable to high-risk genes such as MC4R and BSN, and nearly twice that of known protective genes such as GPR75, which, along with five other genes, showed strong European bias. Notably, 82 of the 116 genes showed functional relevance to obesity including adiposity, energy homeostasis, and glucose metabolism. While polygenic risks or an obesogenic lifestyle amplified the effect of 15 genes on BMI, including the combination of low physical activity and MACROD1, 23 genes including VIRMA, AQP3, and PML retained protective effects even at high polygenic scores. Our findings provide further insights into the genetic basis of obesity that is conserved across ancestries and their interactions with obesogenic factors.

8235 Mutations in EMX2, a Homeodomain Transcription Factor, Cause Idiopathic Hypogonadotropic Hypogonadism

Abstract Disclosure: M. Stamou: None. M. Tompkin: None. H. Bow: None. H. Brand: None. L. Plummer: None. M. Talkowski: None. Y. Shen: None. D. Wu: None. R. Balasubramanian: None. S. Wray: None. S.B. Seminara: None. The genetic etiology of infertility remains largely unknown. To identify novel human infertility genes, we applied a de novo rare variant analysis in 142 parent-proband trios with idiopathic hypogonadotropic hypogonadism (IHH), a Mendelian disease caused by Gonadotropin Releasing Hormone (GnRH) deficiency. The discovery pipeline identified 43 rare de novo copy number variants (CNVs-deletions) spanning 263 genes and 213 rare de novo single nucleotide variants (SNVs) in 191 genes. Four genes were found to be affected by both rare de novo CNVs and SNVs among unrelated sporadic IHH probands. A rare variant association testing for protein truncating variants (PTVs) in the IHH cohort (N=1,394) compared to gnomAD controls (N=125,784) revealed that 3/4 genes demonstrated enrichment for PTVs in the IHH cohort compared to controls: the 2 known for IHH genes (ANOS1 and FGFR1) and one novel candidate gene, EMX2. IHH probands with de novo EMX2 variants demonstrated developmental delay and hearing loss. Common EMX2 variants were linked to delayed puberty/infertility, hearing loss and Parkinson’s disease in a phenome-wide association study of the Massachusetts General Brigham Biobank (N=65,253). Emx2 lineage tracing was conducted on Emx2Cre/+; RosatdT/+ and Emx2Cre/-; RosatdT/+ mouse embryos at embryonic day(E) 13.5. The tdT reporter was co-expressed in neuroendocrine GnRH (nGnRH) cells as they migrated from the olfactory pit to the nasal forebrain junction. WT and KO embryos from Emx2+/- matings were immunostained for GnRH across E12.5, E13.5, E14.5 and E16. The total number of nGnRH cells was greater in the Emx2 KO mice compared to WT littermates. However, by E16.5, fewer nGnRH cells were detected in the forebrain of Emx2 KO, compared to WT (WT= 496±28, KO=384±147, p=0.0125). Thus, failure of nGnRH neurons to enter the forebrain could contribute to the IHH pathogenesis. In conclusion, by utilizing a de novo variant analysis and a mouse model, EMX2 was uncovered as a novel gene for human infertility. Presentation: 6/1/2024

8235 Mutations in EMX2, a Homeodomain Transcription Factor, Cause Idiopathic Hypogonadotropic Hypogonadism

Abstract Disclosure: M. Stamou: None. M. Tompkin: None. H. Bow: None. H. Brand: None. L. Plummer: None. M. Talkowski: None. Y. Shen: None. D. Wu: None. R. Balasubramanian: None. S. Wray: None. S.B. Seminara: None. The genetic etiology of infertility remains largely unknown. To identify novel human infertility genes, we applied a de novo rare variant analysis in 142 parent-proband trios with idiopathic hypogonadotropic hypogonadism (IHH), a Mendelian disease caused by Gonadotropin Releasing Hormone (GnRH) deficiency. The discovery pipeline identified 43 rare de novo copy number variants (CNVs-deletions) spanning 263 genes and 213 rare de novo single nucleotide variants (SNVs) in 191 genes. Four genes were found to be affected by both rare de novo CNVs and SNVs among unrelated sporadic IHH probands. A rare variant association testing for protein truncating variants (PTVs) in the IHH cohort (N=1,394) compared to gnomAD controls (N=125,784) revealed that 3/4 genes demonstrated enrichment for PTVs in the IHH cohort compared to controls: the 2 known for IHH genes (ANOS1 and FGFR1) and one novel candidate gene, EMX2. IHH probands with de novo EMX2 variants demonstrated developmental delay and hearing loss. Common EMX2 variants were linked to delayed puberty/infertility, hearing loss and Parkinson’s disease in a phenome-wide association study of the Massachusetts General Brigham Biobank (N=65,253). Emx2 lineage tracing was conducted on Emx2Cre/+; RosatdT/+ and Emx2Cre/-; RosatdT/+ mouse embryos at embryonic day(E) 13.5. The tdT reporter was co-expressed in neuroendocrine GnRH (nGnRH) cells as they migrated from the olfactory pit to the nasal forebrain junction. WT and KO embryos from Emx2+/- matings were immunostained for GnRH across E12.5, E13.5, E14.5 and E16. The total number of nGnRH cells was greater in the Emx2 KO mice compared to WT littermates. However, by E16.5, fewer nGnRH cells were detected in the forebrain of Emx2 KO, compared to WT (WT= 496±28, KO=384±147, p=0.0125). Thus, failure of nGnRH neurons to enter the forebrain could contribute to the IHH pathogenesis. In conclusion, by utilizing a de novo variant analysis and a mouse model, EMX2 was uncovered as a novel gene for human infertility. Presentation: 6/1/2024

9242 SOX11 mutations cause hypogonadotropic hypogonadism through both hypothalamic and pituitary level deficits

Abstract Disclosure: B. Sun: None. S. Stockman: None. M. Stamou: None. L. Plummer: None. K. Salnikov: None. D. Kotan: None. K. Topaloglu: None. S.B. Seminara: None. R. Balasubramanian: None. AbstractPurpose: Human SOX11 mutations cause Coffin-Siris Syndrome (CSS) which is clinically characterized by aplasia or hypoplasia of the distal phalanx/nail of the fifth digit, developmental delay, cognitive impairment, and distinctive facial features. Recent studies show hypogonadotropic hypogonadism (HH) occurs in ∼20% of patients with SOX11 mutations. The genotypic-phenotypic spectrum of SOX11 mutations in patients presenting with Idiopathic Hypogonadotropic Hypogonadism (IHH) is unknown. Methods: Exome sequencing data from 1810 unrelated IHH probands (anosmic and normosmic forms) were reviewed for SOX11 rare sequence variants (RSVs) [minor allele frequency &amp;lt;0.1%]. Rare variant association analysis (RVAS) and domain-based enrichment for SOX11 RSVs was performed between the IHH and gnomAD population cohort. RSVs predicted as pathogenic/likely pathogenic by ACMG criteria were deemed as pathogenic RSVs and detailed phenotypic analysis was undertaken for these variants. Results: A total of 4 pathogenic SOX11 RSVs were identified in 5 IHH cases. RVAS analysis showed that SOX11 inactivating RSVs (frameshift/non-sense) across the entire protein was enriched in the IHH cohort (2 RSVs in 3 IHH cases (p.S303X [de-novo]; p p.S345Afs*13); p&amp;lt;0.0001) while SOX11 missense RSVs was enriched only within the SOX11-HMG domain (2 RSVs in 2 IHH cases (p.G84D [de-novo]; p.P114S; p=0.003). Phenotypic review of individuals with pathogenic SOX11-RSVs revealed a spectrum of endocrine defects including: anosmic and normosmic forms of IHH, growth-hormone (GH) deficiency, neuroimaging defects (pituitary microadenoma and hypothalamic mass), and hypothyroidism. Additionally, we also identified a pathogenic SOX11 HMG domain RSV in a patient with functional HH (p.R100Q). CSS-associated features were present in almost all individuals with pathogenic SOX11 RSVs. Conclusions: This study validates SOX11 mutations as causal for IHH and reiterates the critical importance of the SOX11-HMG domain in disease pathogenesis. The spectrum of endocrine abnormalities in SOX11-related human disorders includes IHH, functional HH, GH deficiency, pituitary lesions, and hypothyroidism. The association of congenital anosmic and normosmic forms of IHH, functional HH, and anatomic abnormalities of the pituitary gland suggest that the pathogenesis of HH in SOX11-disorder may stem from both hypothalamic and pituitary level deficits. Presentation: 6/1/2024

8474 SOX11 mutations cause hypogonadotropic hypogonadism through both hypothalamic and pituitary level deficits

Abstract Disclosure: B. Sun: None. S. Stockman: None. M. Stamou: None. L. Plummer: None. K. Salnikov: None. D. Kotan: None. K. Topaloglu: None. S.B. Seminara: None. R. Balasubramanian: None. AbstractPurpose: Human SOX11 mutations cause Coffin-Siris Syndrome (CSS) which is clinically characterized by aplasia or hypoplasia of the distal phalanx/nail of the fifth digit, developmental delay, cognitive impairment, and distinctive facial features. Recent studies show hypogonadotropic hypogonadism (HH) occurs in ∼20% of patients with SOX11 mutations. The genotypic-phenotypic spectrum of SOX11 mutations in patients presenting with Idiopathic Hypogonadotropic Hypogonadism (IHH) is unknown. Methods: Exome sequencing data from 1810 unrelated IHH probands (anosmic and normosmic forms) were reviewed for SOX11 rare sequence variants (RSVs) [minor allele frequency &amp;lt;0.1%]. Rare variant association analysis (RVAS) and domain-based enrichment for SOX11 RSVs was performed between the IHH and gnomAD population cohort. RSVs predicted as pathogenic/likely pathogenic by ACMG criteria were deemed as pathogenic RSVs and detailed phenotypic analysis was undertaken for these variants. Results: A total of 4 pathogenic SOX11 RSVs were identified in 5 IHH cases. RVAS analysis showed that SOX11 inactivating RSVs (frameshift/non-sense) across the entire protein was enriched in the IHH cohort (2 RSVs in 3 IHH cases (p.S303X [de-novo]; p p.S345Afs*13); p&amp;lt;0.0001) while SOX11 missense RSVs was enriched only within the SOX11-HMG domain (2 RSVs in 2 IHH cases (p.G84D [de-novo]; p.P114S; p=0.003). Phenotypic review of individuals with pathogenic SOX11-RSVs revealed a spectrum of endocrine defects including: anosmic and normosmic forms of IHH, growth-hormone (GH) deficiency, neuroimaging defects (pituitary microadenoma and hypothalamic mass), and hypothyroidism. Additionally, we also identified a pathogenic SOX11 HMG domain RSV in a patient with functional HH (p.R100Q). CSS-associated features were present in almost all individuals with pathogenic SOX11 RSVs. Conclusions: This study validates SOX11 mutations as causal for IHH and reiterates the critical importance of the SOX11-HMG domain in disease pathogenesis. The spectrum of endocrine abnormalities in SOX11-related human disorders includes IHH, functional HH, GH deficiency, pituitary lesions, and hypothyroidism. The association of congenital anosmic and normosmic forms of IHH, functional HH, and anatomic abnormalities of the pituitary gland suggest that the pathogenesis of HH in SOX11-disorder may stem from both hypothalamic and pituitary level deficits. Presentation: 6/1/2024