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.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias.

Polygenic risk scores (PRSs) are an important tool for understanding the role of common genetic variants in human disease. Standard best practices recommend that PRSs be analyzed in cohorts that are independent of the genome-wide association study (GWAS) used to derive the scores without sample overlap or relatedness between the two cohorts. However, identifying sample overlap and relatedness can be challenging in an era of GWASs performed by large biobanks and international research consortia. Although most genomics researchers are aware of best practices and theoretical concerns about sample overlap and relatedness between GWAS and PRS cohorts, the prevailing assumption is that the risk of bias is small for very large GWASs. Here, we present two real-world examples demonstrating that sample overlap and relatedness is not a minor or theoretical concern but an important potential source of bias in PRS studies. Using a recently developed statistical adjustment tool, we found that excluding overlapping and related samples was equal to or more powerful than adjusting for overlap bias. Our goal is to make genomics researchers aware of the magnitude of risk of bias from sample overlap and relatedness and to highlight the need for mitigation tools, including independent validation cohorts in PRS studies, continued development of statistical adjustment methods, and tools for researchers to test their cohorts for overlap and relatedness with GWAS cohorts without sharing individual-level data.

Loss of function of OTUD7A in the schizophrenia- associated 15q13.3 deletion impairs synapse development and function in human neurons.

Identifying causative gene(s) within disease-associated large genomic regions of copy-number variants (CNVs) is challenging. Here, by targeted sequencing of genes within schizophrenia (SZ)-associated CNVs in 1,779 SZ cases and 1,418 controls, we identified three rare putative loss-of-function (LoF) mutations in OTU deubiquitinase 7A (OTUD7A) within the 15q13.3 deletion in cases but none in controls. To tie OTUD7A LoF with any SZ-relevant cellular phenotypes, we modeled the OTUD7A LoF mutation, rs757148409, in human induced pluripotent stem cell (hiPSC)-derived induced excitatory neurons (iNs) by CRISPR-Cas9 engineering. The mutant iNs showed a ∼50% decrease in OTUD7A expression without undergoing nonsense-mediated mRNA decay. The mutant iNs also exhibited marked reduction of dendritic complexity, density of synaptic proteins GluA1 and PSD-95, and neuronal network activity. Congruent with the neuronal phenotypes in mutant iNs, our transcriptomic analysis showed that the set of OTUD7A LoF-downregulated genes was enriched for those relating to synapse development and function and was associated with SZ and other neuropsychiatric disorders. These results suggest that OTUD7A LoF impairs synapse development and neuronal function in human neurons, providing mechanistic insight into the possible role of OTUD7A in driving neuropsychiatric phenotypes associated with the 15q13.3 deletion.

Identification of PSMB5 as a genetic modifier of fragile X-associated tremor/ataxia syndrome.

Fragile X­associated tremor/ataxia syndrome (FXTAS) is a debilitating late-onset neurodegenerative disease in premutation carriers of the expanded CGG repeat in FMR1 that presents with a spectrum of neurological manifestations, such as gait ataxia, intention tremor, and parkinsonism [P. J. Hagerman, R. J. Hagerman, Ann. N. Y. Acad. Sci. 1338, 58­70 (2015); S. Jacquemont et al., JAMA 291, 460­469 (2004)]. Here, we performed whole-genome sequencing (WGS) on male premutation carriers (CGG55­200) and prioritized candidate variants to screen for candidate genetic modifiers using a Drosophila model of FXTAS. We found 18 genes that genetically modulate CGG-associated neurotoxicity in Drosophila, such as Prosbeta5 (PSMB5), pAbp (PABPC1L), e(y)1 (TAF9), and CG14231 (OSGEPL1). Among them, knockdown of Prosbeta5 (PSMB5) suppressed CGG-associated neurodegeneration in the fly as well as in N2A cells. Interestingly, an expression quantitative trait locus variant in PSMB5, PSMB5rs11543947-A, was found to be associated with decreased expression of PSMB5 and delayed onset of FXTAS in human FMR1 premutation carriers. Finally, we demonstrate evidence that PSMB5 knockdown results in suppression of CGG neurotoxicity via both the RAN translation and RNA-mediated toxicity mechanisms, thereby presenting a therapeutic strategy for FXTAS.

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.

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.

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia.

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

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.

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.

Genome-wide Enrichment of De Novo Coding Mutations in Orofacial Cleft Trios.

Although de novo mutations (DNMs) are known to increase an individual's risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 child-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to an individual's OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

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.

FAT4 identified as a potential modifier of orofacial cleft laterality.

Orofacial clefts (OFCs) are common (1 in 700 births) congenital malformations that include a cleft lip (CL) and cleft lip and palate (CLP). These OFC subtypes are also heterogeneous themselves, with the CL occurring on the left, right, or both sides of the upper lip. Unilateral CL and CLP have a 2:1 bias towards left-sided clefts, suggesting a nonrandom process. Here, we performed a study of left- and right-sided clefts within the CL and CLP subtypes to better understand the genetic factors controlling cleft laterality. We conducted genome-wide modifier analyses by comparing cases that had right unilateral CL (RCL; N = 130), left unilateral CL (LCL; N = 216), right unilateral CLP (RCLP; N = 416), or left unilateral CLP (LCLP; N = 638), and identified a candidate region on 4q28, 400 kb downstream from FAT4, that approached genome-wide significance for LCL versus RCL (p = 8.4 × 10-8 ). Consistent with its potential involvement as a genetic modifier of CL, we found that Fat4 exhibits a specific domain of expression in the mesenchyme of the medial nasal processes that form the median upper lip. Overall, these results suggest that the epidemiological similarities in left- to right-sided clefts in CL and CLP are not reflected in the genetic association results.

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits.

The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan when ≥1% cis-SNPs co-regulate gene expression and gene expression heritability ≤0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

Behavioral changes and growth deficits in a CRISPR engineered mouse model of the schizophrenia-associated 3q29 deletion.

The 3q29 deletion confers increased risk for neuropsychiatric phenotypes including intellectual disability, autism spectrum disorder, generalized anxiety disorder, and a >40-fold increased risk for schizophrenia. To investigate consequences of the 3q29 deletion in an experimental system, we used CRISPR/Cas9 technology to introduce a heterozygous deletion into the syntenic interval on C57BL/6 mouse chromosome 16. mRNA abundance for 20 of the 21 genes in the interval was reduced by ~50%, while protein levels were reduced for only a subset of these, suggesting a compensatory mechanism. Mice harboring the deletion manifested behavioral impairments in multiple domains including social interaction, cognitive function, acoustic startle, and amphetamine sensitivity, with some sex-dependent manifestations. In addition, 3q29 deletion mice showed reduced body weight throughout development consistent with the phenotype of 3q29 deletion syndrome patients. Of the genes within the interval, DLG1 has been hypothesized as a contributor to the neuropsychiatric phenotypes. However, we show that Dlg1+/- mice did not exhibit the behavioral deficits seen in mice harboring the full 3q29 deletion. These data demonstrate the following: the 3q29 deletion mice are a valuable experimental system that can be used to interrogate the biology of 3q29 deletion syndrome; behavioral manifestations of the 3q29 deletion may have sex-dependent effects; and mouse-specific behavior phenotypes associated with the 3q29 deletion are not solely due to haploinsufficiency of Dlg1.

Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

Rare variants in MYH15 modify amyotrophic lateral sclerosis risk.

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder characterized by progressive muscular atrophy and respiratory failure. The G4C2 repeat expansion in the C9orf72 gene is the most prevalent genetic risk for ALS. Mutation carriers (C9ALS) display variability in phenotypes such as age-at-onset and duration, suggesting the existence of additional genetic factors. Here we introduce a three-step gene discovery strategy to identify genetic factors modifying the risk of both C9ALS and sporadic ALS (sALS) using limited samples. We first identified 135 candidate genetic modifiers of C9ALS using whole-genome sequencing (WGS) of extreme C9ALS cases diagnosed ~30 years apart. We then performed an unbiased genetic screen using a Drosophila model of the G4C2 repeat expansion with the genes identified from WGS analysis. This genetic screen identified the novel genetic interaction between G4C2 repeat-associated toxicity and 18 genetic factors, suggesting their potential association with C9ALS risk. We went on to test if 14 out of the 18 genes, those which were not known to be risk factors for ALS previously, are also associated with ALS risk in sALS cases. Gene-based-statistical analyses of targeted resequencing and WGS were performed. These analyses together reveal that rare variants in MYH15 represent a likely genetic risk factor for ALS. Furthermore, we show that MYH15 could modulate the toxicity of dipeptides produced from expanded G4C2 repeat. Our study presented here demonstrates the power of combining WGS with fly genetics to facilitate the discovery of fundamental genetic components of complex traits with a limited number of samples.

The "maternal effect" on epilepsy risk: Analysis of familial epilepsies and reassessment of prior evidence.

OBJECTIVE: Previous studies have observed that epilepsy risk is higher among offspring of affected women than offspring of affected men. We tested whether this "maternal effect" was present in familial epilepsies, which are enriched for genetic factors that contribute to epilepsy risk. METHODS: We assessed evidence of a maternal effect in a cohort of families containing ≥3 persons with epilepsy using 3 methods: (1) "downward-looking" analysis, comparing the rate of epilepsy in offspring of affected women versus men; (2) "upward-looking" analysis, comparing the rate of epilepsy among mothers versus fathers of affected individuals; and (3) lineage analysis, comparing the proportion of affected individuals with family history of epilepsy on the maternal versus paternal side. RESULTS: Downward-looking analysis revealed no difference in epilepsy rates among offspring of affected mothers versus fathers (prevalence ratio = 1.0, 95% confidence interval [CI] = 0.8-1.2). Upward-looking analysis revealed more affected mothers than affected fathers; this effect was similar for affected and unaffected sibships (odds ratio = 0.8, 95% CI = 0.5-1.2) and was explained by a combination of differential fertility and participation rates. Lineage analysis revealed no significant difference in the likelihood of maternal versus paternal family history of epilepsy. INTERPRETATION: We found no evidence of a maternal effect on epilepsy risk in this familial epilepsy cohort. Confounding sex imbalances can create the appearance of a maternal effect in upward-looking analyses and may have impacted prior studies. We discuss possible explanations for the lack of evidence, in familial epilepsies, of the maternal effect observed in population-based studies. ANN NEUROL 2020;87:132-138.

Epilepsy in families: Age at onset is a familial trait, independent of syndrome.

OBJECTIVE: We tested 2 hypotheses regarding age at onset within familial epilepsies: (1) family members with epilepsy tend to have similar ages at onset, independent of epilepsy syndrome; and (2) age at onset is younger in successive generations after controlling for sampling bias. METHODS: We analyzed clinical data collected by the Epi4K Consortium (303 multiplex families, 1,120 individuals). To test hypothesis 1, we used both linear mixed models commonly used for heritability analysis and Cox regression models with frailty terms to assess clustering of onset within families after controlling for other predictors. To test hypothesis 2, we used mixed effects models, pairwise analyses, and survival analysis to address sampling-related bias that may mimic anticipation. RESULTS: Regarding hypothesis 1, age at seizure onset was significantly heritable (intraclass correlation coefficient = 0.17, p < 0.001) after adjusting for epilepsy type, sex, site, history of febrile seizure, and age at last observation. This finding remained significant after adjusting for epilepsy syndromes, and was robust across statistical methods in all families and in generalized families. Regarding hypothesis 2, the mean age at onset decreased in successive generations (p < 0.001). After adjusting for age at last observation, this effect was not significant in mixed effects models (p = 0.14), but remained significant in pairwise (p = 0.0003) and survival analyses (p = 0.02). INTERPRETATION: Age at seizure onset is an independent familial trait, and may have genetic determinants distinct from the determinants of particular epilepsy syndromes. Younger onsets in successive generations can be explained in part by sampling bias, but the presence of genetic anticipation cannot be excluded. ANN NEUROL 2019.