Common variants increase risk for congenital diaphragmatic hernia within the context of de novo variants.

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly often accompanied by other structural anomalies and/or neurobehavioral manifestations. Rare de novo protein-coding variants and copy-number variations contribute to CDH in the population. However, most individuals with CDH remain genetically undiagnosed. Here, we perform integrated de novo and common-variant analyses using 1,469 CDH individuals, including 1,064 child-parent trios and 6,133 ancestry-matched, unaffected controls for the genome-wide association study. We identify candidate CDH variants in 15 genes, including eight novel genes, through deleterious de novo variants. We further identify two genomic loci contributing to CDH risk through common variants with similar effect sizes among Europeans and Latinx. Both loci are in putative transcriptional regulatory regions of developmental patterning genes. Estimated heritability in common variants is ∼19%. Strikingly, there is no significant difference in estimated polygenic risk scores between isolated and complex CDH or between individuals harboring deleterious de novo variants and individuals without these variants. The data support a polygenic model as part of the CDH genetic architecture.

Gaining insight into metabolic diseases from human genetic discoveries.

Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.

Rare-variant and polygenic analyses of amyotrophic lateral sclerosis in the French-Canadian genome.

PURPOSE: The genetic etiology of amyotrophic lateral sclerosis (ALS) includes few rare, large-effect variants and potentially many common, small-effect variants per case. The genetic risk liability for ALS might require a threshold comprised of a certain amount of variants. Here, we tested the degree to which risk for ALS was affected by rare variants in ALS genes, polygenic risk score, or both. METHODS: 335 ALS cases and 356 controls from Québec, Canada were concurrently tested by microarray genotyping and targeted sequencing of ALS genes known at the time of study inception. ALS genome-wide association studies summary statistics were used to estimate an ALS polygenic risk score (PRS). Cases and controls were subdivided into rare-variant heterozygotes and non-heterozygotes. RESULTS: Risk for ALS was significantly associated with PRS and rare variants independently in a logistic regression model. Although ALS PRS predicted a small amount of ALS risk overall, the effect was most pronounced between ALS cases and controls that were not heterozygous for a rare variant in the ALS genes surveyed. CONCLUSION: Both PRS and rare variants in ALS genes impact risk for ALS. PRS for ALS is most informative when rare variants are not observed in ALS genes.

In silico prioritisation of microRNA-associated common variants in multiple sclerosis.

BACKGROUND: Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. METHODS: We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3'UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3'UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. RESULTS: We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3'UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. CONCLUSIONS: We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3'UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3'UTR target-binding site variation in multiple sclerosis using GWAS summary statistics.

Common epilepsy variants from the general population are not associated with epilepsy among individuals with tuberous sclerosis complex.

Common genetic variants identified in the general population have been found to increase phenotypic risks among individuals with certain genetic conditions. Up to 90% of individuals with tuberous sclerosis complex (TSC) are affected by some type of epilepsy, yet the common variants contributing to epilepsy risk in the general population have not been evaluated in the context of TSC-associated epilepsy. Such knowledge is important to help uncover the underlying pathogenesis of epilepsy in TSC which is not fully understood, and critical as uncontrolled epilepsy is a major problem in this population. To evaluate common genetic modifiers of epilepsy, our study pooled phenotypic and genotypic data from 369 individuals with TSC to evaluate known and novel epilepsy common variants. We did not find evidence of enhanced genetic penetrance for known epilepsy variants identified across the largest genome-wide association studies of epilepsy in the general population, but identified support for novel common epilepsy variants in the context of TSC. Specifically, we have identified a novel signal in SLC7A1 that may be functionally involved in pathways relevant to TSC and epilepsy. Our study highlights the need for further evaluation of genetic modifiers in TSC to aid in further understanding of epilepsy in TSC and improve outcomes.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia.

INTRODUCTION: The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability. METHODS: Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank. RESULTS: Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets. DISCUSSION: Our study contributes to the current body of evidence on the genetic etiology of ADRD. HIGHLIGHTS: Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Genetic Variation across Phenotypic Severity of Autism.

It is still unclear how genetic factors of autism spectrum disorder (ASD) are implicated in the significant clinical heterogeneity ranging from intellectual disability (ID) to high-functioning profiles. Here, evidence from recent genetic studies encompassing common and rare variants are combined to suggest a genetic model that may explain the broad gradient of phenotypic severity observed in ASD.

Returning integrated genomic risk and clinical recommendations: The eMERGE study.

PURPOSE: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

MicroRNA binding site variation is enriched in psychiatric disorders.

Psychiatric disorders have a polygenic architecture, often associated with dozens or hundreds of independent genomic loci. Most associated loci impact noncoding regions of the genome, suggesting that the majority of disease heritability originates from the disruption of regulatory sequences. While most research has focused on variants that modify regulatory DNA elements, those affecting cis-acting RNA sequences, such as miRNA binding sites, are also likely to have a significant impact. We intersected genome-wide association study (GWAS) summary statistics with the dbMTS database of predictions for miRNA binding site variants (MBSVs). We compared the distributions of MBSV association statistics to non-MBSVs within brain-expressed 3'UTR regions. We aggregated GWAS p values at the gene, pathway, and miRNA family levels to investigate cellular functions and miRNA families strongly associated with each trait. We performed these analyses in several psychiatric disorders as well as nonpsychiatric traits for comparison. We observed significant enrichment of MBSVs in schizophrenia, depression, bipolar disorder, and anorexia nervosa, particularly in genes targeted by several miRNA families, including miR-335-5p, miR-21-5p/590-5p, miR-361-5p, and miR-557, and a nominally significant association between miR-323b-3p MBSVs and schizophrenia risk. We identified evidence for the association between MBSVs in synaptic gene sets in schizophrenia and bipolar disorder. We also observed a significant association of MBSVs in other complex traits including type 2 diabetes. These observations support the role of miRNA in the pathophysiology of psychiatric disorders and suggest that MBSVs are an important class of regulatory variants that have functional implications for many disorders, as well as other complex human traits.

MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data.

With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared with methods that only use a single marker or trait, the joint analysis of multiple genetic variants and multiple traits is more powerful since such an analysis can fully incorporate the correlation structure of genetic variants and/or traits and their mutual dependence patterns. However, most of existing methods that simultaneously analyze multiple genetic variants and multiple traits are only applicable to unrelated samples. We develop a new method called MF-TOWmuT to detect association of multiple phenotypes and multiple genetic variants in a genomic region with family samples. MF-TOWmuT is based on an optimally weighted combination of variants. Our method can be applied to both rare and common variants and both qualitative and quantitative traits. Our simulation results show that (1) the type I error of MF-TOWmuT is preserved; (2) MF-TOWmuT outperforms two existing methods such as Multiple Family-based Quasi-Likelihood Score Test and Multivariate Family-based Rare Variant Association Test in terms of power. We also illustrate the usefulness of MF-TOWmuT by analyzing genotypic and phenotipic data from the Genetics of Kidneys in Diabetes study. R program is available at https://github.com/gaochengPRC/MF-TOWmuT.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Global spectrum of population-specific common missense variation in cytochrome P450 pharmacogenes.

Next-generation sequencing technology has afforded the discovery of many novel variants that are of significance to inheritable pharmacogenomics (PGx) traits but a large proportion of them have unknown consequences. These include missense variants resulting in single amino acid substitutions in cytochrome P450 (CYP) proteins that can impair enzyme function, leading to altered drug efficacy and toxicity. While most unknown variants are rare, an overlooked minority are variants that are collectively rare but enriched in specific populations. Here, we analyzed sequence variation data in 141,456 individuals from across eight study populations in gnomAD for 38 CYP genes to identify such variants in addition to common variants. By further comparison with data from two PGx-specific databases (PharmVar and PharmGKB) and ClinVar, we identified 234 missense variants in 35 CYP genes, of which 107 were unknown to these databases. Most unknown variants (n = 83) were population-specific common variants and several (n = 7) were found in important CYP pharmacogenes (CYP2D6, CYP4F2, and CYP2C19). Overall, 29% (n = 31) of 107 unknown variants were predicted to affect CYP enzyme function although further biochemical characterization is necessary. These variants may elucidate part of the unexplained interpopulation differences observed in drug response.

Translating genetic association of lipid levels for biological and clinical application.

PURPOSE OF REVIEW: This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction. RECENT FINDINGS: Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes-PCSK9, LDLR, and APOB-with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction. CONCLUSIONS: Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.

APOC3 genetic variation, serum triglycerides, and risk of coronary artery disease in Asian Indians, Europeans, and other ethnic groups.

BACKGROUND: Hypertriglyceridemia has emerged as a critical coronary artery disease (CAD) risk factor. Rare loss-of-function (LoF) variants in apolipoprotein C-III have been reported to reduce triglycerides (TG) and are cardioprotective in American Indians and Europeans. However, there is a lack of data in other Europeans and non-Europeans. Also, whether genetically increased plasma TG due to ApoC-III is causally associated with increased CAD risk is still unclear and inconsistent. The objectives of this study were to verify the cardioprotective role of earlier reported six LoF variants of APOC3 in South Asians and other multi-ethnic cohorts and to evaluate the causal association of TG raising common variants for increasing CAD risk. METHODS: We performed gene-centric and Mendelian randomization analyses and evaluated the role of genetic variation encompassing APOC3 for affecting circulating TG and the risk for developing CAD. RESULTS: One rare LoF variant (rs138326449) with a 37% reduction in TG was associated with lowered risk for CAD in Europeans (p = 0.007), but we could not confirm this association in Asian Indians (p = 0.641). Our data could not validate the cardioprotective role of other five LoF variants analysed. A common variant rs5128 in the APOC3 was strongly associated with elevated TG levels showing a p-value 2.8 × 10- 424. Measures of plasma ApoC-III in a small subset of Sikhs revealed a 37% increase in ApoC-III concentrations among homozygous mutant carriers than the wild-type carriers of rs5128. A genetically instrumented per 1SD increment of plasma TG level of 15 mg/dL would cause a mild increase (3%) in the risk for CAD (p = 0.042). CONCLUSIONS: Our results highlight the challenges of inclusion of rare variant information in clinical risk assessment and the generalizability of implementation of ApoC-III inhibition for treating atherosclerotic disease. More studies would be needed to confirm whether genetically raised TG and ApoC-III concentrations would increase CAD risk.

Genome-wide association study across pediatric central nervous system tumors implicates shared predisposition and points to 1q25.2 (PAPPA2) and 11p12 (LRRC4C) as novel candidate susceptibility loci.

INTRODUCTION: Central nervous system (CNS) tumors constitute the most common form of solid neoplasms in children, but knowledge on genetic predisposition is sparse. In particular, whether susceptibility attributable to common variants is shared across CNS tumor types in children has not been investigated. The purpose of this study was to explore potential common genetic risk variants exhibiting pleiotropic effects across pediatric CNS tumors. We also investigated whether such susceptibility differs between early and late onset of disease. METHOD: A Danish nationwide genome-wide association study (GWAS) of 1,097 consecutive patients (< 15 years of age) with CNS tumors and a cohort of 4,745 population-based controls. RESULTS: For both the overall cohort and patients diagnosed after the age of four, the strongest association was rs12064625 which maps to PAPPA2 at 1q25.2 (p = 3.400 × 10-7 and 9.668 × 10-8, respectively). PAPPA2 regulates local bioavailability of insulin-like growth factor I (IGF-I). IGF-I is fundamental to CNS development and is involved in tumorigenesis across a wide range of different cancers. For the younger children, the strongest association was provided by rs11036373 mapping to LRRC4C at 11p12 (p = 7.620 × 10-7), which encoded protein acts as an axon guidance molecule during CNS development and has not formerly been associated with brain tumors. DISCUSSION: This GWAS indicates shared susceptibility attributable to common variants across pediatric CNS tumor types. Variations in genetic loci with roles in CNS development appear to be involved, possibly via altered IGF-I related pathways.

Genome-Wide Meta-Analysis Identifies Three Novel Susceptibility Loci and Reveals Ethnic Heterogeneity of Genetic Susceptibility for IgA Nephropathy.

BACKGROUND: Eighteen known susceptibility loci for IgAN account for only a small proportion of IgAN risk. METHODS: Genome-wide meta-analysis was performed in 2628 patients and 11,563 controls of Chinese ancestry, and a replication analysis was conducted in 6879 patients and 9019 controls of Chinese descent and 1039 patients and 1289 controls of European ancestry. The data were used to assess the association of susceptibility loci with clinical phenotypes for IgAN, and to investigate genetic heterogeneity of IgAN susceptibility between the two populations. Imputation-based analysis of the MHC/HLA region extended the scrutiny. RESULTS: Identification of three novel loci (rs6427389 on 1q23.1 [P=8.18×10-9, OR=1.132], rs6942325 on 6p25.3 [P=1.62×10-11, OR=1.165], and rs2240335 on 1p36.13 [P=5.10×10-9, OR=1.114]), implicates FCRL3, DUSP22.IRF4, and PADI4 as susceptibility genes for IgAN. Rs2240335 is associated with the expression level of PADI4, and rs6427389 is in high linkage disequilibrium with rs11264799, which showed a strong expression quantitative trail loci effect on FCRL3. Of the 24 confirmed risk SNPs, six showed significant heterogeneity of genetic effects and DEFA showed clear evidence of allelic heterogeneity between the populations. Imputation-based analysis of the MHC region revealed significant associations at three HLA polymorphisms (HLA allele DPB1*02, AA_DRB1_140_32657458_T, and AA_DQA1_34_32717152) and two SNPs (rs9275464 and rs2295119). CONCLUSIONS: A meta-analysis of GWAS data revealed three novel genetic risk loci for IgAN, and three HLA polymorphisms and two SNPs within the MHC region, and demonstrated the genetic heterogeneity of seven loci out of 24 confirmed risk SNPs.  These variants may explain susceptibility differences between Chinese and European populations.

A general statistic to test an optimally weighted combination of common and/or rare variants.

Both genome-wide association study and next-generation sequencing data analyses are widely employed to identify disease susceptible common and/or rare genetic variants. Rare variants generally have large effects though they are hard to detect due to their low frequencies. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some adhoc assumptions (e.g., ignoring dependence between rare variants). In this study, we analytically derived optimal weights for both common and rare variants and proposed a general and novel approach to test association between an optimally weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. Additionally, we applied G-TOW and other competitive methods to test disease-associated genes in real data of schizophrenia. The G-TOW has successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic and the sequence kernel association test. Simulation study and real data analysis indicate that G-TOW is a powerful test.

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

The Yin and Yang of Autism Genetics: How Rare De Novo and Common Variations Affect Liability.

The etiology of autism spectrum disorder (ASD) is complex, involving both genetic and environmental contributions to individual and population-level liability. Early researchers hypothesized that ASD arises from polygenic inheritance, but later results, such as the identification of mutations in certain genes that are responsible for syndromes associated with ASD, led others to propose that de novo mutations of major effect would account for most cases. This yin and yang of monogenic causes and polygenic inheritance continues to this day. The development of genome-wide genotyping and sequencing techniques has resulted in remarkable advances in our understanding of the genetic architecture of risk for ASD. The combined research findings provide solid evidence that ASD is a complex polygenic disorder. Rare de novo and inherited variations act within the context of a common-variant genetic load, and this load accounts for the largest portion of ASD liability.