Genome-wide study of gene-by-sex interactions identifies risks for cleft palate.

Structural birth defects affect 3-4% of all live births and, depending on the type, tend to manifest in a sex-biased manner. Orofacial clefts (OFCs) are the most common craniofacial structural birth defects and are often divided into cleft lip with or without cleft palate (CL/P) and cleft palate only (CP). Previous studies have found sex-specific risks for CL/P, but these risks have yet to be evaluated in CP. CL/P is more common in males and CP is more frequently observed in females, so we hypothesized there would also be sex-specific differences for CP. Using a trio-based cohort, we performed sex-stratified genome-wide association studies (GWAS) based on proband sex followed by a genome-wide gene-by-sex (GxS) interaction testing. There were 13 loci significant for GxS interactions, with the top finding in LTBP1 (RR=3.37 [2.04 - 5.56], p=1.93x10 -6 ). LTBP1 plays a role in regulating TGF-B bioavailability, and knockdown in both mice and zebrafish lead to craniofacial anomalies. Further, there is evidence for differential expression of LTBP1 between males and females in both mice and humans. Therefore, we tested the association between the imputed genetically regulated gene expression of genes with significant GxS interactions and the CP phenotype. We found significant association for LTBP1 in cell cultured fibroblasts in female probands (p=0.0013) but not in males. Taken altogether, we show there are sex-specific risks for CP that are otherwise undetectable in a combined sex cohort, and LTBP1 is a candidate risk gene, particularly in females.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia.

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

Familial aggregation of seizure outcomes in four familial epilepsy cohorts.

OBJECTIVE: To assess the possible effects of genetics on seizure outcome by estimating the familial aggregation of three outcome measures: seizure remission, history of ≥4 tonic-clonic seizures, and seizure control for individuals taking antiseizure medication. METHODS: We analyzed families containing multiple persons with epilepsy in four previously collected retrospective cohorts. Seizure remission was defined as being 5 and 10 years seizure-free at last observation. Total number of tonic-clonic seizures was dichotomized at <4 and ≥4 seizures. Seizure control in patients taking antiseizure medication was defined as no seizures for 1, 2, and 3 years. We used Bayesian generalized linear mixed-effects model (GLMM) to estimate the intraclass correlation coefficient (ICC) of the family-specific random effect, controlling for epilepsy type, age at epilepsy onset, and age at last data collection as fixed effects. We analyzed each cohort separately and performed meta-analysis using GLMMs. RESULTS: The combined cohorts included 3644 individuals with epilepsy from 1463 families. A history of ≥4 tonic-clonic seizures showed strong familial aggregation in three separate cohorts and meta-analysis (ICC .28, 95% confidence interval [CI] .21-.35, Bayes factor 8 × 1016). Meta-analyses did not reveal significant familial aggregation of seizure remission (ICC .08, 95% CI .01-.17, Bayes factor 1.46) or seizure control for individuals taking antiseizure medication (ICC .13, 95% CI 0-.35, Bayes factor 0.94), with heterogeneity among cohorts. SIGNIFICANCE: A history of ≥4 tonic-clonic seizures aggregated strongly in families, suggesting a genetic influence, whereas seizure remission and seizure control for individuals taking antiseizure medications did not aggregate consistently in families. Different seizure outcomes may have different underlying biology and risk factors. These findings should inform the future molecular genetic studies of seizure outcomes.

Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease.

Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia.

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

The Quantitative Genetics of Human Disease: 1 Foundations.

In this the first of an anticipated four paper series, fundamental results of quantitative genetics are presented from a first principles approach. While none of these results are in any sense new, they are presented in extended detail to precisely distinguish between definition and assumption, with a further emphasis on distinguishing quantities from their usual approximations. Terminology frequently encountered in the field of human genetic disease studies will be defined in terms of their quantitive genetics form. Methods for estimation of both quantitative genetics and the related human genetics quantities will be demonstrated. While practitioners in the field of human quantitative disease studies may find this work pedantic in detail, the principle target audience for this work is trainees reasonably familiar with population genetics theory, but with less experience in its application to human disease studies. We introduce much of this formalism because in later papers in this series, we demonstrate that common areas of confusion in human disease studies can be resolved be appealing directly to these formal definitions. The second paper in this series will discuss polygenic risk scores. The third paper will concern the question of "missing" heritability and the role interactions may play. The fourth paper will discuss sexually dimorphic disease and the potential role of the X chromosome.

Trio-based GWAS identifies novel associations and subtype-specific risk factors for cleft palate.

Cleft palate (CP) is one of the most common craniofacial birth defects; however, there are relatively few established genetic risk factors associated with its occurrence despite high heritability. Historically, CP has been studied as a single phenotype, although it manifests across a spectrum of defects involving the hard and/or soft palate. We performed a genome-wide association study using transmission disequilibrium tests of 435 case-parent trios to evaluate broad risks for any cleft palate (ACP) (n = 435), and subtype-specific risks for any cleft soft palate (CSP), (n = 259) and any cleft hard palate (CHP) (n = 125). We identified a single genome-wide significant locus at 9q33.3 (lead SNP rs7035976, p = 4.24 × 10-8) associated with CHP. One gene at this locus, angiopoietin-like 2 (ANGPTL2), plays a role in osteoblast differentiation. It is expressed both in craniofacial tissue of human embryos and developing mouse palatal shelves. We found 19 additional loci reaching suggestive significance (p < 5 × 10-6), of which only one overlapped between groups (chromosome 17q24.2, ACP and CSP). Odds ratios for the 20 loci were most similar across all 3 groups for SNPs associated with the ACP group, but more distinct when comparing SNPs associated with either subtype. We also found nominal evidence of replication (p < 0.05) for 22 SNPs previously associated with orofacial clefts. Our study to evaluate CP risks in the context of its subtypes and we provide newly reported associations affecting the broad risk for CP as well as evidence of subtype-specific risks.

Rare variant modifier analysis identifies variants in SEC24D associated with orofacial cleft subtypes.

As one of the most common structural birth defects, orofacial clefts (OFCs) have been studied for decades, and recent studies have demonstrated that there are genetic differences between the different phenotypic presentations of OFCs. However, the contribution of rare genetic variation genome-wide to different subtypes of OFCs has been understudied, with most studies focusing on common genetic variation or rare variation within targeted regions of the genome. Therefore, we used whole-genome sequencing data from the Gabriella Miller Kids First Pediatric Research Program to conduct a gene-based burden analysis to test for genetic modifiers of cleft lip (CL) vs cleft lip and palate (CLP). We found that there was a significantly increased burden of rare variants in SEC24D in CL cases compared to CLP cases (p = 6.86 [Formula: see text] 10-7). Of the 15 variants within SEC24D, 53.3% were synonymous, but overlapped a known craniofacial enhancer. We then tested whether these variants could alter predicted transcription factor binding sites (TFBS), and found that the rare alleles destroyed binding sites for 9 transcription factors (TFs), including Pax1 (p = 0.0009), and created binding sites for 23 TFs, including Pax6 (p = 6.12 [Formula: see text] 10-5) and Pax9 (p = 0.0001), which are known to be involved in normal craniofacial development, suggesting a potential mechanism by which these synonymous variants could have a functional impact. Overall, this study indicates that rare genetic variation may contribute to the phenotypic heterogeneity of OFCs and suggests that regulatory variation may also contribute and warrant further investigation in future studies of genetic variants controlling risk to OFC.

Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer's disease.

Alzheimer's disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes-aggregation of the amyloid-ß (Aß) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)-are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aß plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aß plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aß and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aß and tau.

Association of inflammation-related exposures and ovarian cancer survival in a multi-site cohort study of Black women.

BACKGROUND: An association was observed between an inflammation-related risk score (IRRS) and worse overall survival (OS) among a cohort of mostly White women with invasive epithelial ovarian cancer (EOC). Herein, we evaluated the association between the IRRS and OS among Black women with EOC, a population with higher frequencies of pro-inflammatory exposures and worse survival. METHODS: The analysis included 592 Black women diagnosed with EOC from the African American Cancer Epidemiology Study (AACES). Cox proportional hazards models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of the IRRS and OS, adjusting for relevant covariates. Additional inflammation-related exposures, including the energy-adjusted Dietary Inflammatory Index (E-DIITM), were evaluated. RESULTS: A dose-response trend was observed showing higher IRRS was associated with worse OS (per quartile HR: 1.11, 95% CI: 1.01-1.22). Adding the E-DII to the model attenuated the association of IRRS with OS, and increasing E-DII, indicating a more pro-inflammatory diet, was associated with shorter OS (per quartile HR: 1.12, 95% CI: 1.02-1.24). Scoring high on both indices was associated with shorter OS (HR: 1.54, 95% CI: 1.16-2.06). CONCLUSION: Higher levels of inflammation-related exposures were associated with decreased EOC OS among Black women.

Sex differences in brain protein expression and disease.

Most complex human traits differ by sex, but we have limited insight into the underlying mechanisms. Here, we investigated the influence of biological sex on protein expression and its genetic regulation in 1,277 human brain proteomes. We found that 13.2% (1,354) of brain proteins had sex-differentiated abundance and 1.5% (150) of proteins had sex-biased protein quantitative trait loci (sb-pQTLs). Among genes with sex-biased expression, we found 67% concordance between sex-differentiated protein and transcript levels; however, sex effects on the genetic regulation of expression were more evident at the protein level. Considering 24 psychiatric, neurologic and brain morphologic traits, we found that an average of 25% of their putatively causal genes had sex-differentiated protein abundance and 12 putatively causal proteins had sb-pQTLs. Furthermore, integrating sex-specific pQTLs with sex-stratified genome-wide association studies of six psychiatric and neurologic conditions, we uncovered another 23 proteins contributing to these traits in one sex but not the other. Together, these findings begin to provide insights into mechanisms underlying sex differences in brain protein expression and disease.

Rare variants found in clinical gene panels illuminate the genetic and allelic architecture of orofacial clefting.

PURPOSE: Orofacial clefts (OFCs) are common birth defects including cleft lip, cleft lip and palate, and cleft palate. OFCs have heterogeneous etiologies, complicating clinical diagnostics because it is not always apparent if the cause is Mendelian, environmental, or multifactorial. Sequencing is not currently performed for isolated or sporadic OFCs; therefore, we estimated the diagnostic yield for 418 genes in 841 cases and 294 controls. METHODS: We evaluated 418 genes using genome sequencing and curated variants to assess their pathogenicity using American College of Medical Genetics criteria. RESULTS: 9.04% of cases and 1.02% of controls had "likely pathogenic" variants (P < .0001), which was almost exclusively driven by heterozygous variants in autosomal genes. Cleft palate (17.6%) and cleft lip and palate (9.09%) cases had the highest yield, whereas cleft lip cases had a 2.80% yield. Out of 39 genes with likely pathogenic variants, 9 genes, including CTNND1 and IRF6, accounted for more than half of the yield (4.64% of cases). Most variants (61.8%) were "variants of uncertain significance", occurring more frequently in cases (P = .004), but no individual gene showed a significant excess of variants of uncertain significance. CONCLUSION: These results underscore the etiological heterogeneity of OFCs and suggest sequencing could reduce the diagnostic gap in OFCs.

Rare variants found in multiplex families with orofacial clefts: Does expanding the phenotype make a difference?

Exome sequencing (ES) is now a relatively straightforward process to identify causal variants in Mendelian disorders. However, the same is not true for ES in families where the inheritance patterns are less clear, and a complex etiology is suspected. Orofacial clefts (OFCs) are highly heritable birth defects with both Mendelian and complex etiologies. The phenotypic spectrum of OFCs may include overt clefts and several subclinical phenotypes, such as discontinuities in the orbicularis oris muscle (OOM) in the upper lip, velopharyngeal insufficiency (VPI), microform clefts or bifid uvulas. We hypothesize that expanding the OFC phenotype to include these phenotypes can clarify inheritance patterns in multiplex families, making them appear more Mendelian. We performed exome sequencing to find rare, likely causal genetic variants in 31 multiplex OFC families, which included families with multiple individuals with OFCs and individuals with subclinical phenotypes. We identified likely causal variants in COL11A2, IRF6, SHROOM3, SMC3, TBX3, and TP63 in six families. Although we did not find clear evidence supporting the subclinical phenotype hypothesis, our findings support a role for rare variants in the etiology of OFCs.

Trio-based GWAS identifies novel associations and subtype-specific risk factors for cleft palate.

Orofacial clefts (OFCs) are the most common craniofacial birth defects and are often categorized into two etiologically distinct groups: cleft lip with or without cleft palate (CL/P) and isolated cleft palate (CP). CP is highly heritable, but there are still relatively few established genetic risk factors associated with its occurrence compared to CL/P. Historically, CP has been studied as a single phenotype despite manifesting across a spectrum of defects involving the hard and/or soft palate. We performed GWAS using transmission disequilibrium tests using 435 case-parent trios to evaluate broad risks for any cleft palate (ACP, n=435), as well as subtype-specific risks for any cleft soft palate (CSP, n=259) and any cleft hard palate (CHP, n=125). We identified a single genome-wide significant locus at 9q33.3 (lead SNP rs7035976, p=4.24×10 -8 ) associated with CHP. One gene at this locus, angiopoietin-like 2 ( ANGPTL2 ), plays a role in osteoblast differentiation. It is expressed in craniofacial tissue of human embryos, as well as in the developing mouse palatal shelves. We found 19 additional loci reaching suggestive significance (p<5×10 -6 ), of which only one overlapped between groups (chromosome 17q24.2, ACP and CSP). Odds ratios (ORs) for each of the 20 loci were most similar across all three groups for SNPs associated with the ACP group, but more distinct when comparing SNPs associated with either the CSP or CHP groups. We also found nominal evidence of replication (p<0.05) for 22 SNPs previously associated with cleft palate (including CL/P). Interestingly, most SNPs associated with CL/P cases were found to convey the opposite effect in those replicated in our dataset for CP only. Ours is the first study to evaluate CP risks in the context of its subtypes and we provide newly reported associations affecting the broad risk for CP as well as evidence of subtype-specific risks.

POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes.

MOTIVATION: There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits. RESULTS: Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation. AVAILABILITY AND IMPLEMENTATION: The code for this method is available at https://github.com/staylorhead/POIROT-POE.

Rare genetic variants in SEC24D modify orofacial cleft phenotypes.

As one of the most common structural birth defects, orofacial clefts (OFCs) have been studied for decades, and recent studies have demonstrated that there are genetic differences between the different phenotypic presentations of OFCs. However, the contribution of rare genetic variation genome-wide to different subtypes of OFCs has been understudied, with most studies focusing on common genetic variation or rare variation within targeted regions of the genome. Therefore, we used whole-genome sequencing data from the Gabriella Miller Kids First Pediatric Research Program to conduct a gene-based burden analysis to test for genetic modifiers of cleft lip (CL) vs cleft lip and palate (CLP). We found that there was a significantly increased burden of rare variants in SEC24D in CL cases compared to CLP cases (p=6.86×10-7). Of the 15 variants within SEC24D, 53.3% were synonymous, but overlapped a known craniofacial enhancer. We then tested whether these variants could alter predicted transcription factor binding sites (TFBS), and found that the rare alleles destroyed binding sites for 9 transcription factors (TFs), including Pax1 (p=0.0009), and created binding sites for 23 TFs, including Pax6 (p=6.12×10-5) and Pax9 (p= 0.0001), which are known to be involved in normal craniofacial development, suggesting a potential mechanism by which these synonymous variants could have a functional impact. Overall, this study demonstrates that rare genetic variation contributes to the phenotypic heterogeneity of OFCs and suggests that regulatory variation may also contribute and warrant further investigation in future studies of genetic variants controlling risk to OFC.

OTTERS: a powerful TWAS framework leveraging summary-level reference data.

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.