Rare Variant Association Tests Research Articles

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

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Combining evidence from human genetic and functional screens to identify pathways altering obesity and fat distribution.

Overall adiposity and body fat distribution are heritable traits associated with altered risk of cardiometabolic disease and mortality. Performing rare variant (minor allele frequency<1%) association testing using exome-sequencing data from 402,375 participants in the UK Biobank (UKB) for nine overall and tissue-specific fat distribution traits, we identified 19 genes where putatively damaging rare variation associated with at least one trait (Bonferroni-adjusted P<1.58×10-7) and 52 additional genes at FDR≤1% (P≤4.37×10-5). These 71 genes exhibited higher (P=3.58×10-18) common variant prioritisation scores than genes not significantly enriched for rare putatively damaging variation, with evidence of monotonic allelic series (dose-response relationships) among ultra-rare variants (minor allele count≤10) in 22 genes. Five of the 71 genes have cognate protein UKB Olink data available; all five associated (P<3.80×10-6) with three or more analysed traits. Combining rare and common variation evidence, allelic series and proteomics, we selected 17 genes for CRISPR knockout in human white adipose tissue cell lines. In three previously uncharacterised target genes, knockout increased (two-sided t-test P<0.05) lipid accumulation, a cellular phenotype relevant for fat mass traits, compared to Cas9-empty negative controls: COL5A3 (fold change [FC]=1.72, P=0.0028), EXOC7 (FC=1.35, P=0.0096), and TRIP10 (FC=1.39, P=0.0157); furthermore, knockout of SLTM resulted in reduced lipid accumulation (FC=0.51, P=1.91×10-4). Integrating across population-based genetic and in vitro functional evidence, we highlight therapeutic avenues for altering obesity and body fat distribution by modulating lipid accumulation.

Expanding the Spectrum of Endocrine Abnormalities Associated with SOX11-related Disorders.

SOX11 variants cause Coffin-Siris Syndrome (CSS), characterized by developmental delay, hypogonadotropic hypogonadism (HH), skeletal and facial defects. To examine the contribution of SOX11 variants to the pathogenesis of Idiopathic Hypogonadotropic Hypogonadism (IHH), a disorder caused by hypothalamic GnRH deficiency. The Reproductive Endocrine Unit and the Pediatric Endocrinology Division, Massachusetts General Hospital. A cohort of 1810 unrelated IHH probands. Exome sequencing data from the entire cohort were examined for SOX11 rare single nucleotide variants (SNVs) [minor allele frequency in the gnomAD database <0.1%]. Rare SOX11 variant association testing was performed between the IHH and gnomAD population. Phenotyping of individuals harboring pathogenic/likely pathogenic SNVs (determined by the ACMG criteria) was performed. Four pathogenic SOX11 SNVs were identified in 5 IHH probands. The IHH cohort was enriched for SOX11 protein truncating SNVs (frameshift/nonsense) across the entire protein (2 SNVs in 3 IHH cases [p.S303X (de-novo); p.S345Afs*13]; p 0.0004981) and for SOX11 missense SNVs within the SOX11-high-mobility group (HMG) domain (2 SNVs in 2 IHH cases p.G84D[de-novo]; p.P114S; p=0.00313922). The phenotypic spectrum of SOX11 variant carriers revealed additional endocrine defects including anosmic and normosmic forms of IHH, growth-hormone deficiency, pituitary and hypothalamic structural defects, and hypothyroidism. A pathogenic SOX11 SNV was also identified in a patient with functional HH (FHH, p.R100Q). CSS-associated features were present in 4/5 probands. Deleterious SOX11 variants cause IHH and other pituitary hormone deficiencies, suggesting that the human SOX11-associated disorder may stem from both hypothalamic and pituitary level defects.

Lack of a genetic risk continuum between pubertal timing in the general population and idiopathic hypogonadotropic hypogonadism.

Pubertal timing is a highly heritable trait in the general population. Recently, a large-scale exome-wide association study has implicated rare variants in six genes (KDM4C, MC3R, MKRN3, PDE10A, TACR3, and ZNF483) as genetic determinants of pubertal timing within the general population. Two of the genes (TACR3, MKRN3) are already implicated in extreme disorders of pubertal timing. This observation suggests that there may be a pervasive "genetic risk continuum" wherein genes that govern pubertal timing in the general population, by extension, may also be causal for rare Mendelian disorders of pubertal timing. Hence, we hypothesized that the four novel genes linked to pubertal timing in the population will also contribute to idiopathic hypogonadotropic hypogonadism (IHH), a genetic disorder characterized by absent puberty. Exome sequencing data from 1322 unrelated IHH probands were reviewed for rare sequence variants (RSVs) (minor allele frequency bins: <1%; <0.1%; <0.01%) in the six genes linked to puberty in the general population. A gene-based rare variant association testing (RVAT) was performed between the IHH cohort and a reference public genomic sequences repository-the Genome Aggregation Database (gnomAD). As expected, RVAT analysis showed that RSVs in TACR3, a known IHH gene, were significantly enriched in the IHH cohort compared to gnomAD cohort across all three MAF bins. However, RVAT analysis of the remaining five genes failed to show any RSV enrichment in the IHH cohort across all MAF bins. Our findings argue strongly against a pervasive genetic risk continuum between pubertal timing in the general population and extreme pubertal phenotypes. The biologic basis of such distinct genetic architectures' merits further evaluation.

Using gene and gene-set association tests to identify lethal prostate cancer genes.

Recent advances in the detection and treatment of prostate cancer (PCa) have reduced morbidity and mortality from this common cancer. Despite these improvements, PCa remains the second leading cause of cancer death in men in the United States. Further understanding of the genetic underpinnings of lethal PCa is required to drive risk detection and prevention and ultimately reduce mortality. We therefore set out to identify germline variants associated with cases of lethal prostate cancer (LPCa). Using a two-stage study design, we compared whole-exome sequencing data of 550 LPCa patients to 488 healthy male controls. Men were classified as having LPCa based on medical record review. Candidate genes were identified using gene- and gene-set-based rare truncating variant association tests. Case-control burden testing through Firth's penalized logistic regression and case-gnomAD allelic burden testing through a one-sided mid-p Fisher's exact test were conducted. Each gene's p-values from these tests were combined into an omnibus p-value for candidate gene selection. In the subsequent validation stage, genes were assessed using the UK Biobank and Firth's penalized logistic regression for each ancestry, combined through meta-analysis. Gene-based rare variant association tests identified 12 genes nominally associated with LPCa. Rare-variant association tests identified a gene set with a significantly higher burden of truncating germline mutations in LPCa patients than controls. Combining gene- and gene-set test results, four nominally significant genes (PPP1R3A, TG, PPFIBP2, and BTN3A3) were selected as candidates. Subsequent validation using the UK Biobank found that PPP1R3A was significantly associated with LPCa risk (odds ratio 2.34, CI 1.20-4.59). Specifically, pGln662ArgfsTer7 was identified as the predominant variant in PPP1R3A among LPCa patients in our dataset. Both individual gene and gene-set analyses identified candidates associated with LPCa. The novel association of PPP1R3A and LPCa risk merits further investigation.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes.

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods. In this study, we use a recently developed Bayesian rare variant association method that is powerful for detecting sparse rare risk variants that implicates 88 new candidate risk genes associated with schizophrenia from the SCHEMA case-control sample. These newly identified genes are significantly enriched in autism risk genes and GO enrichment analysis indicates that new candidate risk genes are involved in mechanosensory behavior, regulation of cell size, neuron projection morphogenesis, and plasma-membrane-bounded cell projection morphogenesis, that may provide new insights on the etiology of schizophrenia.

Deciphering the genomic insights into the coexistence of congenital scoliosis and congenital anomalies of the kidney and urinary tract.

Congenital scoliosis and congenital anomalies of the kidney and urinary tract are distinct genetic disorders with differing clinical manifestations. Clinically, their coexistence is not rare, but the etiologies of these complex diseases remain largely unknown, especially their shared genetic basis. We sequenced the genomes of 40 individuals diagnosed with both CS and CAKUT, alongside 2,764 controls from a Chinese Han population cohort. Our analyses encompassed gene-based and pathway-based weighted rare variant association tests, complemented by copy number variant association analyses, aiming to unravel the shared genomic etiology underlying these congenital conditions. Gene-based analysis identified PTPN11 as a pivotal gene influencing both skeletal and urinary system development (P = 1.95E-21), participating in metabolic pathways, especially the MAPK/ERK pathway known to regulate skeletal and urinary system development. Pathway-based enrichment showed a significant signal in the MAPK/ERK pathway (P = 3E-04), reinforcing the potential role of PTPN11 and MAPK/ERK pathway in both conditions. Additionally, CNV analysis pinpointed IGFLR1 haploinsufficiency as a potential influential factor in the combined CS-CAKUT phenotypic spectrum. This study enriches our understanding of the intricate genomic interplay underlying congenital scoliosis and kidney and urinary tract anomalies, emphasizing the shared genetic foundations between these two disorders.

Enrichment of Rare Variants of Hemophagocytic Lymphohistiocytosis Genes in Systemic Juvenile Idiopathic Arthritis.

Our objective was to evaluate whether there is an enrichment of rare variants in familial hemophagocytic lymphohistiocytosis (HLH)-associated genes among patients with systemic juvenile idiopathic arthritis (sJIA) with or without macrophage activation syndrome (MAS). Targeted sequencing of HLH genes (LYST, PRF1, RAB27A, STX11, STXBP2, UNC13D) was performed in patients with sJIA from an established cohort. Sequence data from control participants were obtained in silico (database of Genotypes and Phenotypes: phs000280.v8.p2). Rare variant association testing (RVT) was performed with sequence kernel association test package. Significance was defined as P < 0.05 after 100,000 permutations. Sequencing data from 524 sJIA cases were jointly called and harmonized with exome-derived target data from 3,000 controls. Quality control operations produced a set of 480 cases and 2,924 ancestrally matched control participants. RVT of cases and controls revealed a significant association with rare protein-altering variants (minor allele frequency [MAF] < 0.01) of STXBP2 (P = 0.020) and ultrarare variants (MAF < 0.001) of STXBP2 (P = 0.006) and UNC13D (P = 0.046). A subanalysis of 32 cases with known MAS and 90 without revealed a significant difference in the distribution of rare UNC13D variants (P = 0.0047) between the groups. Additionally, patients with sJIA more often carried two or more HLH variants than did controls (P = 0.007), driven largely by digenic combinations involving LYST. We identified an enrichment of rare HLH variants in patients with sJIA compared with controls, driven by STXBP2 and UNC13D. Biallelic variation in HLH genes was associated with sJIA, driven by LYST. Only UNC13D displayed enrichment in patients with MAS. This suggests that HLH variants may contribute to the pathophysiology of sJIA, even without MAS.

Rare variant association analyses reveal the significant contribution of carbohydrate metabolic disturbance in severe adolescent idiopathic scoliosis

BackgroundAdolescent idiopathic scoliosis (AIS), the predominant genetic-influenced scoliosis, results in spinal deformities without vertebral malformations. However, the molecular aetiology of AIS remains unclear.MethodsUsing genome/exome sequencing, we studied 368 patients with...

A novel rare variants association test for binary traits in family-based designs via copulas.

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.

Learning the kernel for rare variant genetic association test.

Introduction: Compared to Genome-Wide Association Studies (GWAS) for common variants, single-marker association analysis for rare variants is underpowered. Set-based association analyses for rare variants are powerful tools that capture some of the missing heritability in trait association studies. Methods: We extend the convex-optimized SKAT (cSKAT) test set procedure which learns from data the optimal convex combination of kernels, to the full Generalised Linear Model (GLM) setting with arbitrary non-genetic covariates. We call this extended cSKAT (ecSKAT) and show that the resulting optimization problem is a quadratic programming problem that can be solved with no additional cost compared to cSKAT. Results: We show that a modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimizing this objective function is a principled way of learning the combination of kernels. We evaluate the performance of the proposed method on continuous and binary traits using simulation studies and illustrate its application using UK Biobank Whole Exome Sequencing data on hand grip strength and systemic lupus erythematosus rare variant association analysis. Discussion: Our proposed ecSKAT method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. Compared to the burden test and SKAT method, ecSKAT gives a lower p-value for the genes tested in both quantitative and binary traits in the UKBiobank cohort.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

An allelic-series rare-variant association test for candidate-gene discovery

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n= 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Toward Identification of Functional Sequences and Variants in Noncoding DNA.

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes.

Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages.

Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank

The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.

Adjusting for common variant polygenic scores improves yield in rare variant association analyses.

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10-6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.

CRELD1 variants are associated with bicuspid aortic valve in Turner syndrome

Turner syndrome (TS) is a chromosomal disorder caused by complete or partial loss of the second sex chromosome and exhibits phenotypic heterogeneity, even after accounting for mosaicism and karyotypic variation. Congenital heart defects (CHD) are found in up to 45 percent of girls with TS and span a phenotypic continuum of obstructive left-sided lesions, with bicuspid aortic valve (BAV) being the most common. Several recent studies have demonstrated a genome-wide impact of X chromosome haploinsufficiency, including global hypomethylation and altered RNA expression. The presence of such broad changes to the TS epigenome and transcriptome led others to hypothesize that X chromosome haploinsufficiency sensitizes the TS genome, and several studies have demonstrated that a second genetic hit can modify disease susceptibility in TS. The objective of this study was to determine whether genetic variants in known heart developmental pathways act synergistically in this setting to increase the risk for CHD, specifically BAV, in TS. We analyzed 208 whole exomes from girls and women with TS and performed gene-based variant enrichment analysis and rare-variant association testing to identify variants associated with BAV in TS. Notably, rare variants in CRELD1 were significantly enriched in individuals with TS who had BAV compared to those with structurally normal hearts. CRELD1 is a protein that functions as a regulator of calcineurin/NFAT signaling, and rare variants in CRELD1 have been associated with both syndromic and non-syndromic CHD. This observation supports the hypothesis that genetic modifiers outside the X chromosome that lie in known heart development pathways may influence CHD risk in TS.