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Genetic Epidemiology and Insights into Interactive Genetic and Environmental Effects in Autism Spectrum Disorders

Published:November 04, 2014DOI:https://doi.org/10.1016/j.biopsych.2014.11.001

      Abstract

      Understanding the pathogenesis of neurodevelopmental disorders has proven to be challenging. Using autism spectrum disorder (ASD) as a paradigmatic neurodevelopmental disorder, this article reviews the existing literature on the etiological substrates of ASD and explores how genetic epidemiology approaches including gene-environment interactions (G×E) can play a role in identifying factors associated with ASD etiology. New genetic and bioinformatics strategies have yielded important clues to ASD genetic substrates. The next steps for understanding ASD pathogenesis require significant effort to focus on how genes and environment interact with one another in typical development and its perturbations. Along with larger sample sizes, future study designs should include sample ascertainment that is epidemiologic and population-based to capture the entire ASD spectrum with both categorical and dimensional phenotypic characterization; environmental measurements with accuracy, validity, and biomarkers; statistical methods to address population stratification, multiple comparisons, and G×E of rare variants; animal models to test hypotheses; and new methods to broaden the capacity to search for G×E, including genome-wide and environment-wide association studies, precise estimation of heritability using dense genetic markers, and consideration of G×E both as the disease cause and a disease course modifier. Although examination of G×E appears to be a daunting task, tremendous recent progress in gene discovery has opened new horizons for advancing our understanding of the role of G×E in the pathogenesis of ASD and ultimately identifying the causes, treatments, and even preventive measures for ASD and other neurodevelopmental disorders.

      Keywords

      Pathogenesis of psychiatric disorders is complex, but gene discovery has provided insight into biological mechanisms underlying neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Gene discovery has led to only a modest understanding of ASD biological pathways; however, recent work with rare, de novo mutations is pointing the field in new directions, including converging actions of ASD-associated mutations and the rapidly evolving progress in the use of translational strategies (in animals and humans) to understand the effects of replicated ASD-associated mutations (
      • Willsey A.J.
      • Sanders S.J.
      • Li M.
      • Dong S.
      • Tebbenkamp A.T.
      • Muhle R.A.
      • et al.
      Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism.
      ). Understanding roles of rare variants in ASD has highlighted the multifactorial etiology of NDDs characterized by pleiotropy (diverse phenotypes from identical genetic factors), genetic heterogeneity (different genes causing same phenotypes), and interactions: epistasis (between genes) and gene-environment interactions (G×E) (
      • Kim Y.S.
      • State M.W.
      Recent challenges to the psychiatric diagnostic nosology: A focus on the genetics and genomics of neurodevelopmental disorders.
      ,
      • Malhotra D.
      • Sebat J.
      CNVs: Harbingers of a rare variant revolution in psychiatric genetics.
      ,
      • Bijlsma E.K.
      • Gijsbers A.C.
      • Schuurs-Hoeijmakers J.H.
      • van Haeringen A.
      • Fransen van de Putte D.E.
      • Anderlid B.M.
      • et al.
      Extending the phenotype of recurrent rearrangements of 16p11.2: Deletions in mentally retarded patients without autism and in normal individuals.
      ,
      • Mefford H.C.
      • Cooper G.M.
      • Zerr T.
      • Smith J.D.
      • Baker C.
      • Shafer N.
      • et al.
      A method for rapid, targeted CNV genotyping identifies rare variants associated with neurocognitive disease.
      ,
      • Zufferey F.
      • Sherr E.H.
      • Beckmann N.D.
      • Hanson E.
      • Maillard A.M.
      • Hippolyte L.
      • et al.
      A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders.
      ,
      • Sanders S.J.
      • Ercan-Sencicek A.G.
      • Hus V.
      • Luo R.
      • Murtha M.T.
      • Moreno-De-Luca D.
      • et al.
      Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.
      ,
      • McCarthy S.E.
      • Makarov V.
      • Kirov G.
      • Addington A.M.
      • McClellan J.
      • Yoon S.
      • et al.
      Microduplications of 16p11.2 are associated with schizophrenia.
      ,
      • Ahn K.
      • Gotay N.
      • Andersen T.M.
      • Anvari A.A.
      • Gochman P.
      • Lee Y.
      • et al.
      High rate of disease-related copy number variations in childhood onset schizophrenia.
      ,
      • Chaste P.
      • Leboyer M.
      Autism risk factors: Genes, environment, and gene-environment interactions.
      ). Evidence also suggests that environmental factors lead to diverse phenotypes, depending on the developmental timing of exposure (
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      Maternal pre-pregnancy risk drinking and toddler behavior problems: The Norwegian Mother and Child Cohort Study.
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      Maternal alcohol consumption producing fetal alcohol spectrum disorders (FASD): Quantity, frequency, and timing of drinking.
      ). Research to disentangle this complexity requires strategies that specifically incorporate both genetic and environmental factors. This article provides 1) an overview of the role of genetic epidemiology in identifying both genetic and environmental factors and their joint roles in NDD etiology, 2) evidence regarding genetic and environmental influences on NDDs, and 3) research strategies to advance our understanding of NDD etiology. We use ASD as an exemplar of the broader group of NDDs.

      Genetic Epidemiology in ASD

      Genetic epidemiology uses disparate data from bioinformatics, population genetics, epidemiology, and molecular genetics to elucidate roles for genes and their interactions with environment in the occurrence of disease in populations (
      • Khoury M.J.
      • Beaty T.H.
      • Cohen B.H.
      ). Genetic epidemiology 1) focuses on systematic sampling to enhance generalizability of research findings, 2) studies joint effects of genes and environment, and 3) incorporates disease biology into conceptual models (
      • Thomas D.C.
      Genetic epidemiology with a capital “E.”.
      ). Twin, family, linkage, and association studies are among study designs that allow examination of genetic or environmental factors in diseases. With increasing evidence for NDD heterogeneity, genetic epidemiologic studies must attend to ASD phenotypic variability in both sampling and phenotype definition.

      Genetics

      Twin studies with sample sizes of 11–67 monozygotic (MZ) and 9–210 dizygotic (DZ) twin pairs yield 47%–96% ASD concordance rates in MZ twins and 0–36% in DZ twins for autism and broader ASD phenotypes, suggesting strong heritability associated with ASD (
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      • Kaufmann W.E.
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      Characteristics and concordance of autism spectrum disorders among 277 twin pairs.
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      • Stewart M.G.
      Brief report: On the concordance percentages for autistic spectrum disorder of twins.
      ,
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      • Cohen B.
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      Genetic heritability and shared environmental factors among twin pairs with autism.
      ,
      • Nordenbaek C.
      • Jorgensen M.
      • Kyvik K.O.
      • Bilenberg N.
      A Danish population-based twin study on autism spectrum disorders.
      ). Sibling relative risk (λs) (ratio of ASD prevalence among siblings of individuals with ASD to general population) ranges from 1.5–19.4 (
      • Ozonoff S.
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      • Zwaigenbaum L.
      • et al.
      Recurrence risk for autism spectrum disorders: A Baby Siblings Research Consortium study.
      ,
      • Georgiades S.
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      • Bryson S.
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      • et al.
      A prospective study of autistic-like traits in unaffected siblings of probands with autism spectrum disorder.
      ,
      • Gronborg T.K.
      • Schendel D.E.
      • Parner E.T.
      Recurrence of autism spectrum disorders in full- and half-siblings and trends over time: A population-based cohort study.
      ,
      • Constantino J.N.
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      • Hilton C.
      • Law P.
      • Zhang Y.
      • Molloy E.
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      Autism recurrence in half siblings: Strong support for genetic mechanisms of transmission in ASD.
      ,
      • Sandin S.
      • Lichtenstein P.
      • Kuja-Halkola R.
      • Larsson H.
      • Hultman C.M.
      • Reichenberg A.
      The familial risk of autism.
      ).
      Initial genome-wide linkage studies were underpowered for detecting genes of small effect, leading to inconsistent findings across the samples and resistance to replication. Linkage analyses using larger samples, endophenotypes, and quantitative traits yielded positive findings in specific chromosomal regions: 2q, 5, 7q, 15q, and 16p (
      • Veenstra-VanderWeele J.
      • Cook Jr, E.H.
      Molecular genetics of autism spectrum disorder.
      ,
      • Li X.
      • Zou H.
      • Brown W.T.
      Genes associated with autism spectrum disorder.
      ).
      Candidate gene association studies are used for ASD because they are more powerful than linkage, at a given locus, allowing detection of genes of weaker effect. Because of limited knowledge about ASD pathophysiology and gene functions, only a few genes, including SLC6A4, GABR, RELN, NLGN, MET, or EN2, have been examined with infrequent and inconsistent replications (
      • Veenstra-VanderWeele J.
      • Cook Jr, E.H.
      Molecular genetics of autism spectrum disorder.
      ,
      • Veenstra-VanderWeele J.
      • Christina S.L.
      • Cook E.H.J.
      Autism as a paragmatic complex genetic disorder.
      ). Meta-analysis of 14 family-based association studies of 5HTT, reported findings from each study as inconsistent, with main analyses showing no association (
      • Huang C.H.
      • Santangelo S.L.
      Autism and serotonin transporter gene polymorphisms: A systematic review and meta-analysis.
      ).
      In contrast, genome-wide association studies (GWAS) exploit strengths of association studies without guessing the identity of causal genes a priori (hypothesis-free approach); this leads to unbiased, comprehensive searches for susceptibility alleles (
      • Hirschhorn J.N.
      • Daly M.J.
      Genome-wide association studies for common diseases and complex traits.
      ). Although GWAS have yielded successes in medicine (e.g., age-related macular degeneration, obesity, hypertension, diabetes) (
      • Klein R.J.
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      Complement factor H polymorphism in age-related macular degeneration.
      ,
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      Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture.
      ,
      • Saxena R.
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      • Lyssenko V.
      • et al.
      Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research
      Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.
      ,
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      • Johnson A.D.
      • et al.
      Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.
      ), GWAS with samples >2000 failed to identify replicable common variants for ASD (
      • Wang K.
      • Zhang H.
      • Ma D.
      • Bucan M.
      • Glessner J.T.
      • Abrahams B.S.
      • et al.
      Common genetic variants on 5p14.1 associate with autism spectrum disorders.
      ,
      • Weiss L.A.
      • Arking D.E.
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      A genome-wide linkage and association scan reveals novel loci for autism.
      ,
      • Anney R.
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      Individual common variants exert weak effects on the risk for autism spectrum disorderspi.
      ,
      • Anney R.
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      • Regan R.
      • Conroy J.
      • Magalhaes T.R.
      • et al.
      A genome-wide scan for common alleles affecting risk for autism.
      ). Scarce replications in searches for ASD risk alleles result from 1) sample sizes insufficient to detect modest effect sizes; 2) poor control for population stratification in case-control studies; 3) overly permissive approaches in multiple comparison corrections, especially in early candidate gene studies; 4) varied ASD phenotype definitions; and 5) diverse samples primarily selected from nonrepresentative sources (
      • Rutter M.
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      • Simonoff E.
      Genetics and child psychiatry: I. Advances in quantitative and molecular genetics.
      ,
      • Moldin S.O.
      The maddening hunt for madness genes.
      ).
      Significant increases in copy number variations (CNVs), which are submicroscopic variations in chromosomal structure, especially de novo CNVs, in simplex ASD families (i.e., only one affected individual) have been identified (
      • Sebat J.
      • Lakshmi B.
      • Malhotra D.
      • Troge J.
      • Lese-Martin C.
      • Walsh T.
      • et al.
      Strong association of de novo copy number mutations with autism.
      ). Several investigators reported structural variations on the short arm of chromosome 16 associated with idiopathic ASD (
      • Sebat J.
      • Lakshmi B.
      • Malhotra D.
      • Troge J.
      • Lese-Martin C.
      • Walsh T.
      • et al.
      Strong association of de novo copy number mutations with autism.
      ,
      • Weiss L.A.
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      Association between microdeletion and microduplication at 16p11.2 and autism.
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      • et al.
      Recurrent 16p11.2 microdeletions in autism.
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      • Noor A.
      • Vincent J.B.
      • Lionel A.C.
      • Feuk L.
      • Skaug J.
      • et al.
      Structural variation of chromosomes in autism spectrum disorder.
      ). This 16p11.2 CNV includes ~600 kilobases and ~29 genes, 22 of which are expressed in human fetal brain (
      Simons VIP Consortium
      Simons Variation in Individuals Project (Simons VIP): A genetics-first approach to studying autism spectrum and related neurodevelopmental disorders.
      ). Studies have demonstrated 16p11.2 deletions in ~.1%–.7% of ASD and 16p11.2 duplications in ~.1%–.5%, a rate 10 times greater than base rates for this CNV in the general population (
      • Sanders S.J.
      • Ercan-Sencicek A.G.
      • Hus V.
      • Luo R.
      • Murtha M.T.
      • Moreno-De-Luca D.
      • et al.
      Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.
      ,
      • Shen Y.
      • Dies K.A.
      • Holm I.A.
      • Bridgemohan C.
      • Sobeih M.M.
      • Caronna E.B.
      • et al.
      Clinical genetic testing for patients with autism spectrum disorders.
      ,
      • Jacquemont S.
      • Reymond A.
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      • Walters R.G.
      • Kutalik Z.
      • et al.
      Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus.
      ,
      • Guha S.
      • Rees E.
      • Darvasi A.
      • Ivanov D.
      • Ikeda M.
      • Bergen S.E.
      • et al.
      Implication of a rare deletion at distal 16p11.2 in schizophrenia.
      ).
      The 16p11.2 CNV is associated with the following phenotypes: intellectual disabilities, developmental delay, speech problems, schizophrenia, seizures, increased body weight or obesity, and increased head circumference (
      • Bijlsma E.K.
      • Gijsbers A.C.
      • Schuurs-Hoeijmakers J.H.
      • van Haeringen A.
      • Fransen van de Putte D.E.
      • Anderlid B.M.
      • et al.
      Extending the phenotype of recurrent rearrangements of 16p11.2: Deletions in mentally retarded patients without autism and in normal individuals.
      ,
      • Mefford H.C.
      • Cooper G.M.
      • Zerr T.
      • Smith J.D.
      • Baker C.
      • Shafer N.
      • et al.
      A method for rapid, targeted CNV genotyping identifies rare variants associated with neurocognitive disease.
      ,
      • Zufferey F.
      • Sherr E.H.
      • Beckmann N.D.
      • Hanson E.
      • Maillard A.M.
      • Hippolyte L.
      • et al.
      A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders.
      ,
      • Sanders S.J.
      • Ercan-Sencicek A.G.
      • Hus V.
      • Luo R.
      • Murtha M.T.
      • Moreno-De-Luca D.
      • et al.
      Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.
      ,
      • McCarthy S.E.
      • Makarov V.
      • Kirov G.
      • Addington A.M.
      • McClellan J.
      • Yoon S.
      • et al.
      Microduplications of 16p11.2 are associated with schizophrenia.
      ,
      • Ahn K.
      • Gotay N.
      • Andersen T.M.
      • Anvari A.A.
      • Gochman P.
      • Lee Y.
      • et al.
      High rate of disease-related copy number variations in childhood onset schizophrenia.
      ,
      • Jacquemont S.
      • Reymond A.
      • Zufferey F.
      • Harewood L.
      • Walters R.G.
      • Kutalik Z.
      • et al.
      Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus.
      ,
      • Guha S.
      • Rees E.
      • Darvasi A.
      • Ivanov D.
      • Ikeda M.
      • Bergen S.E.
      • et al.
      Implication of a rare deletion at distal 16p11.2 in schizophrenia.
      ,
      • Bochukova E.G.
      • Huang N.
      • Keogh J.
      • Henning E.
      • Purmann C.
      • Blaszczyk K.
      • et al.
      Large, rare chromosomal deletions associated with severe early-onset obesity.
      ,
      • Walters R.G.
      • Coin L.J.
      • Ruokonen A.
      • de Smith A.J.
      • El-Sayed Moustafa J.S.
      • Jacquemont S.
      • et al.
      Rare genomic structural variants in complex disease: Lessons from the replication of associations with obesity.
      ,
      • Walters R.G.
      • Jacquemont S.
      • Valsesia A.
      • de Smith A.J.
      • Martinet D.
      • Andersson J.
      • et al.
      A new highly penetrant form of obesity due to deletions on chromosome 16p11.2.
      ,
      • Steinberg S.
      • de Jong S.
      • Mattheisen M.
      • Costas J.
      • Demontis D.
      • Jamain S.
      • et al.
      Common variant at 16p11.2 conferring risk of psychosis.
      ,
      • Grozeva D.
      • Conrad D.F.
      • Barnes C.P.
      • Hurles M.
      • Owen M.J.
      • O’Donovan M.C.
      • et al.
      Independent estimation of the frequency of rare CNVs in the UK population confirms their role in schizophrenia.
      ). Similar phenomena have been reported for a large number of ASD-associated CNVs, including CNVs on 1q21.2, 3q29, 7q11.23, 7q36.3, 15q11.2, 15q13.3, 16p13.11, 17p12, 17q12, and 22q11.21 (
      • Malhotra D.
      • Sebat J.
      CNVs: Harbingers of a rare variant revolution in psychiatric genetics.
      ).
      Excess de novo single nucleotide variant burden has been observed only for loss of function (i.e., nonsense, canonical splice site, and frameshift mutations) (
      • Sanders S.J.
      • Murtha M.T.
      • Gupta A.R.
      • Murdoch J.D.
      • Raubeson M.J.
      • Willsey A.J.
      • et al.
      De novo mutations revealed by whole-exome sequencing are strongly associated with autism.
      ,
      • O’Roak B.J.
      • Vives L.
      • Girirajan S.
      • Karakoc E.
      • Krumm N.
      • Coe B.P.
      • et al.
      Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations.
      ,
      • Neale B.M.
      • Kou Y.
      • Liu L.
      • Ma’ayan A.
      • Samocha K.E.
      • Sabo A.
      • et al.
      Patterns and rates of exonic de novo mutations in autism spectrum disorders.
      ,
      • Iossifov I.
      • Ronemus M.
      • Levy D.
      • Wang Z.
      • Hakker I.
      • Rosenbaum J.
      • et al.
      De novo gene disruptions in children on the autistic spectrum.
      ,
      • Michaelson J.J.
      • Shi Y.
      • Gujral M.
      • Zheng H.
      • Malhotra D.
      • Jin X.
      • et al.
      Whole-genome sequencing in autism identifies hot spots for de novo germline mutation.
      ,
      • Kong A.
      • Frigge M.L.
      • Masson G.
      • Besenbacher S.
      • Sulem P.
      • Magnusson G.
      • et al.
      Rate of de novo mutations and the importance of father’s age to disease risk.
      ). Multiple de novo single nucleotide variants at the same locus, compared with the null distribution in control subjects, allowed identification of genome-wide significant loci, including loss of function mutations in SCN2A, CHD8, DYRK1A, GRIN2B, KATNAL2, POGZ, CUL3, and TBR1 (
      • Sanders S.J.
      • Murtha M.T.
      • Gupta A.R.
      • Murdoch J.D.
      • Raubeson M.J.
      • Willsey A.J.
      • et al.
      De novo mutations revealed by whole-exome sequencing are strongly associated with autism.
      ,
      • O’Roak B.J.
      • Vives L.
      • Girirajan S.
      • Karakoc E.
      • Krumm N.
      • Coe B.P.
      • et al.
      Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations.
      ,
      • Neale B.M.
      • Kou Y.
      • Liu L.
      • Ma’ayan A.
      • Samocha K.E.
      • Sabo A.
      • et al.
      Patterns and rates of exonic de novo mutations in autism spectrum disorders.
      ,
      • Iossifov I.
      • Ronemus M.
      • Levy D.
      • Wang Z.
      • Hakker I.
      • Rosenbaum J.
      • et al.
      De novo gene disruptions in children on the autistic spectrum.
      ,
      • O’Roak B.J.
      • Vives L.
      • Fu W.
      • Egertson J.D.
      • Stanaway I.B.
      • Phelps I.G.
      • et al.
      Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders.
      ). Similar to CNVs, some single nucleotide variants initially associated with single disorders are now associated with other disorders (e.g., SCN2A in ASD and epilepsy) (
      • Sanders S.J.
      • Murtha M.T.
      • Gupta A.R.
      • Murdoch J.D.
      • Raubeson M.J.
      • Willsey A.J.
      • et al.
      De novo mutations revealed by whole-exome sequencing are strongly associated with autism.
      ,
      • Neale B.M.
      • Kou Y.
      • Liu L.
      • Ma’ayan A.
      • Samocha K.E.
      • Sabo A.
      • et al.
      Patterns and rates of exonic de novo mutations in autism spectrum disorders.
      ,
      • Allen A.S.
      • Berkovic S.F.
      • Cossette P.
      • Delanty N.
      • et al.
      Epi4K Consortium; Epilepsy Phenome/Genome Project
      De novo mutations in epileptic encephalopathies.
      ).
      More recent studies confirm ASD-related genetic heterogeneity found in earlier twin, family, and linkage studies. The ASD-related genes seem to converge on a few pathophysiologic pathways related to synaptic function and plasticity, GTPase and Ras signaling, and neurogenesis (
      • Marshall C.R.
      • Noor A.
      • Vincent J.B.
      • Lionel A.C.
      • Feuk L.
      • Skaug J.
      • et al.
      Structural variation of chromosomes in autism spectrum disorder.
      ,
      • Berkel S.
      • Marshall C.R.
      • Weiss B.
      • Howe J.
      • Roeth R.
      • Moog U.
      • et al.
      Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation.
      ,
      • Pinto D.
      • Pagnamenta A.T.
      • Klei L.
      • Anney R.
      • Merico D.
      • Regan R.
      • et al.
      Functional impact of global rare copy number variation in autism spectrum disorders.
      ,
      • Gilman S.R.
      • Iossifov I.
      • Levy D.
      • Ronemus M.
      • Wigler M.
      • Vitkup D.
      Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses.
      ,
      • State M.W.
      • Sestan N.
      Neuroscience. The emerging biology of autism spectrum disorders.
      ,
      • Ben-David E.
      • Shifman S.
      Networks of neuronal genes affected by common and rare variants in autism spectrum disorders.
      ). Phenotype pleiotropy suggests interaction with additional genetic and nongenetic factors, acting at various developmental time points and resulting in divergent phenotype manifestations of a single genetic variant (
      • Robinson M.R.
      • Wray N.R.
      • Visscher P.M.
      Explaining additional genetic variation in complex traits.
      ).

      Environmental Factors

      Twin studies provide strong evidence equally for genetics and environmental factors in ASD risk. High levels of heritability (phenotypic variance owing to genetic factors), in the range of ~90%, were reported in early twin studies; a more recent twin study found larger environmental influences on ASD risk—37% heritability and 55% shared environmental liability (
      • Hallmayer J.
      • Cleveland S.
      • Torres A.
      • Phillips J.
      • Cohen B.
      • Torigoe T.
      • et al.
      Genetic heritability and shared environmental factors among twin pairs with autism.
      ,
      • Folstein S.
      • Rutter M.
      Infantile autism: A genetic study of 21 twin pairs.
      ,
      • Ritvo E.R.
      • Freeman B.J.
      • Mason-Brothers A.
      • Mo A.
      • Ritvo A.M.
      Concordance for the syndrome of autism in 40 pairs of afflicted twins.
      ). These findings have been replicated in a large independent population-based Swedish National Registry study of 2,049,973 siblings including DZ and MZ twins, yielding 50% heritability and 50% nonshared environmental influence for ASD (
      • Sandin S.
      • Lichtenstein P.
      • Kuja-Halkola R.
      • Larsson H.
      • Hultman C.M.
      • Reichenberg A.
      The familial risk of autism.
      ). Diagnostic disparities in some MZ twin pairs also suggest that environmental factors contribute to both liability for and expression of autism-related traits (
      • Muhle R.
      • Trentacoste S.V.
      • Rapin I.
      The genetics of autism.
      ).
      Progressively higher ASD prevalence estimates (.07%–2.6%) suggest that most of the increase is attributable to greater public awareness, better case ascertainment, broadening of ASD diagnostic construct, and diagnostic substitution (
      • Gillberg C.
      Infantile autism and other childhood psychoses in a Swedish urban region. Epidemiological aspects.
      ,
      • Kim Y.S.
      • Leventhal B.L.
      • Koh Y.J.
      • Fombonne E.
      • Laska E.
      • Lim E.C.
      • et al.
      Prevalence of autism spectrum disorders in a total population sample.
      ,
      • Fombonne E.
      Epidemiology of pervasive developmental disorders.
      ). If increasing ASD prevalence is even partly caused by increasing incidence, environmental factors or their interactions with as-yet-unknown genetic vulnerability may represent other ASD risk mechanisms.
      Nutrients, smoking, alcohol, medications, and pesticides are the most commonly examined exposures during pregnancy because of their known neurotoxicity or specific adverse or protective impacts on developing brains (
      • LaSalle J.
      Epigenomic strategies at the interface of genetic and environmental risk factors for autism.
      ). Epigenetics (long-term or heritable changes in function of a locus or chromosome without alteration of underlying DNA) may represent one pathway for G×E (
      • Goldberg A.D.
      • Allis C.D.
      • Bernstein E.
      Epigenetics: A landscape takes shape.
      ). There is an association of ASD with fragile X, Rett, and Angelman syndromes, each of which involves epigenetic mechanisms (
      • Belmonte M.K.
      • Bourgeron T.
      Fragile X syndrome and autism at the intersection of genetic and neural networks.
      ,
      • Percy A.K.
      Rett syndrome: exploring the autism link.
      ,
      • Bonati M.T.
      • Russo S.
      • Finelli P.
      • Valsecchi M.R.
      • Cogliati F.
      • Cavalleri F.
      • et al.
      Evaluation of autism traits in Angelman syndrome: A resource to unfold autism genes.
      ). Associations have been also reported between ASD and parent-of-origin syndromes (e.g., 15q11.3, Turner syndrome) (
      • Chaste P.
      • Leboyer M.
      Autism risk factors: Genes, environment, and gene-environment interactions.
      ,
      • LaSalle J.
      Epigenomic strategies at the interface of genetic and environmental risk factors for autism.
      ,
      • Ladd-Acosta C.
      • Hansen K.D.
      • Briem E.
      • Fallin M.D.
      • Kaufmann W.E.
      • Feinberg A.P.
      Common DNA methylation alterations in multiple brain regions in autism.
      ). Two recent studies report differences in DNA methylation profiles between individuals with and without ASD; one group studied 20 postmortem brains of individuals with ASD and 21 of control subjects, finding differentially methylated regions in or near genes implicated in cell signaling, synaptic function, plasticity, imprinting, and metabolism (
      • Ladd-Acosta C.
      • Hansen K.D.
      • Briem E.
      • Fallin M.D.
      • Kaufmann W.E.
      • Feinberg A.P.
      Common DNA methylation alterations in multiple brain regions in autism.
      ). Analyses of 6 discordant and 44 concordant MZ twins found numerous differentially methylated regions associated with ASD in within-twin and between-group analyses. Significant correlations between DNA methylation and quantitative autistic traits were also found (
      • Wong C.C.Y.
      • Meabum E.L.
      • Ronald A.
      • Price T.S.
      • Jeffries A.R.
      • Schalkwyk L.C.
      • et al.
      Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits.
      ); these findings should be interpreted with caution because of methodologicl limitations, including small nonrepresentative samples, inadequate case-control sample comparability, multiple comparisons, and cross-sectional design that cannot discern causal relationship. The substrate for epigenetic dysregulation is unknown; however, environmental factors are associated with epigenetic changes, including correlations between folate levels and global DNA methylation (
      • LaSalle J.
      Epigenomic strategies at the interface of genetic and environmental risk factors for autism.
      ). These correlations provide evidence that environmental factors may contribute to ASD etiology through epigenetic processes (
      • Shi M.
      • Christensen K.
      • Weinberg C.R.
      • Romitti P.
      • Bathum L.
      • Lozada A.
      • et al.
      Orofacial cleft risk is increased with maternal smoking and specific detoxification-gene variants.
      ,
      • Kohlmeier M.
      • Dda Costa K.A.
      • Fischer L.M.
      • Zeisel S.H.
      Genetic variation of folate-mediated one-carbon transfer pathway predicts susceptibility to choline deficiency in humans.
      ).
      Studies report associations between ASD and prenatal toxic exposure, ranging from thalidomide to maternal, intrapartum rubella (
      • Chess S.
      Autism in children with congenital rubella.
      ,
      • Moore S.J.
      • Turnpenny P.
      • Quinn A.
      • Glover S.
      • Lloyd D.J.
      • Montgomery T.
      • et al.
      A clinical study of 57 children with fetal anticonvulsant syndromes.
      ,
      • Bescoby-Chambers N.
      • Forster P.
      • Bates G.
      “Foetal valproate syndrome and autism: additional evidence of an association.”.
      ,
      • Lee L.C.
      • Newschaffer C.J.
      • Lessler J.T.
      • Lee B.K.
      • Shah R.
      • Zimmerman A.W.
      Variation in season of birth in singleton and multiple births concordant for autism spectrum disorders.
      ,
      • Williams K.
      • Helmer M.
      • Duncan G.W.
      • Peat J.K.
      • Mellis C.M.
      Perinatal and maternal risk factors for autism spectrum disorders in New South Wales, Australia.
      ), suggesting that environmental exposures during critical periods contribute to ASD susceptibility. How this occurs, the timing, and direct effects causing ASD remain unknown.
      Two meta-analyses of 24 studies examined associations between ASD and perinatal complications (
      • Gardener H.
      • Speigelman D.
      • Burka S.
      Perinatal risk factors for autism: Comprehensive meta-analysis.
      ,
      • Gardener H.
      • Spiegelman D.
      • Buka S.L.
      Perinatal and neonatal risk factors for autism: A comprehensive meta-analysis.
      ), finding modest increases in ASD risk. Odds ratios (OR) from 1.4–1.8 were associated with abnormal fetal presentation, umbilical cord complications, fetal distress, multiple birth, maternal hemorrhage, summer birth, small size for gestational age, congenital malformation, meconium aspiration, ABO or Rh incompatibility, and hyperbilirubinemia, with the largest ORs for birth injury (OR = 4.9), low birth weight (<1500 g) (OR = 3.0), and neonatal anemia (OR = 7.87). Although disaggregating perinatal complications from ASD is challenging because susceptibility might have led to perinatal complications and ASD, two hypotheses exist:
      • Hypoxia: Obstetric complications related to hypoxia pose significant risk in meta-analyses (
        • Gardener H.
        • Spiegelman D.
        • Buka S.L.
        Perinatal and neonatal risk factors for autism: A comprehensive meta-analysis.
        ). This hypothesis is supported by a recent twin study indicating that respiratory distress and other markers of hypoxia increased ASD risk (
        • Froehlich-Santino W.
        • Tobon A.L.
        • Cleveland S.
        • Torres A.
        • Phillips J.
        • Cohen B.
        • et al.
        Prenatal and perinatal risk factors in a twin study of autism spectrum disorders.
        ). Relationships between brain hypoxia and social behavior deficits warrant further attention.
      • Immunologic: Studies link maternal infections during pregnancy to ASD in offspring (
        • Gardener H.
        • Speigelman D.
        • Burka S.
        Perinatal risk factors for autism: Comprehensive meta-analysis.
        ,
        • Gardener H.
        • Spiegelman D.
        • Buka S.L.
        Perinatal and neonatal risk factors for autism: A comprehensive meta-analysis.
        ). Although not well established, this association may be a direct effect of the infectious agent or the resulting activation of the maternal immune system leading to increased ASD risk (
        • Zerbo O.
        • Iosif A.M.
        • Walker C.
        • Ozonoff S.
        • Hansen R.L.
        • Hertz-Picciotto I.
        Is maternal influenza or fever during pregnancy associated with autism or developmental delays? Results from the CHARGE (CHildhood Autism Risks from Genetics and Environment) study.
        ,
        • Atladottir H.O.
        • Henriksen T.B.
        • Schendel D.E.
        • Parner E.T.
        Autism after infection, febrile episodes, and antibiotic use during pregnancy: An exploratory study.
        ,
        • Atladóttir H.O.
        • Parner E.T.
        • Schendel D.
        • Dalsgaard S.
        • Thomsen P.H.
        • Thorsen P.
        Time trends in reported diagnoses of childhood neuropsychiatric disorders: A Danish cohort study.
        ,
        • Patterson P.H.
        Maternal infection and autism.
        ). Approximately 10% of mothers of children with ASD harbor antibrain antibodies that bind to fetal brain (
        • Croen L.A.
        • Braunschweig D.
        • Haapanen L.
        • Yoshida C.K.
        • Fireman B.
        • Grether J.K.
        • et al.
        Maternal mid-pregnancy autoantibodies to fetal brain protein: The early markers for autism study.
        ,
        • Singer H.S.
        • Morris C.M.
        • Gause C.D.
        • Gillin P.K.
        • Crawford S.
        • Zimmerman A.W.
        Antibodies against fetal brain in sera of mothers with autistic children.
        ); antibrain antibody assays were conducted only in mothers of case children and not in mothers of control children. When these antibodies were administered to gestating mice and monkeys, the offspring exhibited abnormal behaviors, including social deficits (
        • Enstrom A.M.
        • Van de Water J.A.
        • Ashwood P.
        Autoimmunity in autism.
        ,
        • Dalton P.
        • Deacon R.
        • Blamire A.
        • Pike M.
        • McKinlay I.
        • Stein J.
        • et al.
        Maternal neuronal antibodies associated with autism and a language disorder.
        ,
        • Singer H.S.
        • Morris C.
        • Gause C.
        • Pollard M.
        • Zimmerman A.W.
        • Pletnikov M.
        Prenatal exposure to antibodies from mothers of children with autism produces neurobehavioral alterations: A pregnant dam mouse model.
        ,
        • Martin L.A.
        • Ashwood P.
        • Braunschweig D.
        • Cabanlit M.
        • Van de Water J.
        • Amaral D.G.
        Stereotypies and hyperactivity in rhesus monkeys exposed to IgG from mothers of children with autism.
        ). These studies suggest that maternal antibodies can alter fetal development resulting in ASD-like behaviors.
      Immunologic hypotheses are appealing when combined with transcriptomic analyses of postmortem brain of individuals with autism (
      • Voineagu I.
      • Wang X.
      • Johnston P.
      • Lowe J.K.
      • Tian Y.
      • Horvath S.
      • et al.
      Transcriptomic analysis of autistic brain reveals convergent molecular pathology.
      ). Compared with individuals without ASD, brains of individuals with ASD demonstrated downregulation of 209 genes, enriched for gene categories related to synaptic function, whereas 235 genes implicated in immune and inflammatory response were upregulated. The former group was significantly enriched for association signals in GWAS, whereas the latter group was not. These studies suggest that relevant immunologic changes are likely caused by environmental factors; however, the roles of rare variants resulting in observed findings cannot be ruled out.
      Two studies report associations between ASD and folate exposure. Each study reported protective effects of intake at different times—first trimester versus preconception—suggesting that there may be multiple points during fetal development when folate is protective (
      • Schmidt R.
      • Tancredi D.J.
      • Ozonoff S.
      • Hansen R.L.
      • Hartiala J.
      • Allayee H.
      • et al.
      Maternal periconceptional folic acid intake and risk of autism specturm disorders and developmental delay in the CHARGE (CHilhood Autism Risk from Genetics and Environment) case-control study.
      ,
      • Suren P.
      • Roth C.
      • Bresnahan M.
      • Haugen M.
      • Hornig M.
      • Hirtz D.
      • et al.
      Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children.
      ).
      The roles of advanced maternal and paternal age at pregnancy on ASD risk in offspring have been consistently reported (
      • Sandin S.
      • Hultman C.M.
      • Kolevzon A.
      • Gross R.
      • MacCabe J.H.
      • Reichenberg A.
      Advancing maternal age is associated with increasing risk for autism: A review and meta-analysis.
      ,
      • Idring S.
      • Magnusson C.
      • Lundberg M.
      • Ek M.
      • Rai D.
      • Svensson A.C.
      • et al.
      Parental age and the risk of autism spectrum disorders: Findings from a Swedish population-based cohort.
      ). Suggested mechanisms include increased rates of de novo mutation, epigenetic dysfunction, and cumulative exposure to environmental toxins (
      • Kong A.
      • Frigge M.L.
      • Masson G.
      • Besenbacher S.
      • Sulem P.
      • Magnusson G.
      • et al.
      Rate of de novo mutations and the importance of father’s age to disease risk.
      ,
      • Sandin S.
      • Hultman C.M.
      • Kolevzon A.
      • Gross R.
      • MacCabe J.H.
      • Reichenberg A.
      Advancing maternal age is associated with increasing risk for autism: A review and meta-analysis.
      ,
      • D’Onofrio B.M.
      • Rickert M.E.
      • Frans E.
      • Kuja-Halkola R.
      • Almqvist C.
      • Sjolander A.
      • et al.
      Paternal age at childbearing and offspring psychiatric and academic morbidity.
      ).
      Studies examining relationships between maternal smoking or drinking during pregnancy and ASD risk are inconsistent and inconclusive. No studies examined the impact of paternal exposure on ASD risks (
      • Lyall K.
      • Schmidt R.J.
      • Hertz-Picciotto I.
      Maternal lifestyle and environmental risk factors for autism spectrum disorders.
      ). Exposures to antiepileptic medications and antidepressants, before and during pregnancy, suggest increased risk for ASD; however, exposure timing differed in each study, making sound conclusions challenging (
      • Lyall K.
      • Schmidt R.J.
      • Hertz-Picciotto I.
      Maternal lifestyle and environmental risk factors for autism spectrum disorders.
      ). Studies of pesticide exposure are still too few to draw conclusions.
      Inconsistencies in findings and methodologic differences in ASD environmental studies make it challenging to reach solid conclusions; inconsistent results are not surprising given significant methodologic differences in work to date. Phenotype measurement differences occur because 1) quality of ASD diagnoses varies greatly; 2) comorbidity, especially intellectual disabilities, is not considered, making it difficult to disaggregate risk factors for intellectual disabilities or other comorbidity from those for ASD; and 3 case ascertainment from administrative datasets increases likelihood of ascertainment bias because of factors associated with accessibility or eligibility for inclusion. Administrative datasets have great diversity because inclusion criteria are administratively but not clinically salient and may change over time. Measurement differences in these studies include 1) varying methods to measure perinatal risks; 2) timing and dose of exposure not consistently measured, making it difficult to align exposure with specific developmental processes and to compare exposures across studies; 3) validity of exposure data jeopardized by retrospective recall; and 4) ecologic fallacy present when group level exposures are obtained.

      G×E

      Genes and environment rarely act alone to create ASD or other NDDs (
      • Sanders S.J.
      • Ercan-Sencicek A.G.
      • Hus V.
      • Luo R.
      • Murtha M.T.
      • Moreno-De-Luca D.
      • et al.
      Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.
      ). Despite many studies exploring roles of genes or environmental factors in ASD, few examine G×E. Because development is a dynamic process reflecting a constant interplay between genes and environment, these interactions occur constantly. Specific perturbations in this process likely play a role in ASD etiology; ignoring these interactions may obscure independent genetic or environmental effects, leading to false-negative and inconsistent findings (
      • Tsuang M.T.
      • Bar J.L.
      • Stone W.S.
      • Faraone S.V.
      Gene-environment interactions in mental disorders.
      ). Both human and animal studies suggest that G×E plays a role in ASD pathogenesis (
      • Chaste P.
      • Leboyer M.
      Autism risk factors: Genes, environment, and gene-environment interactions.
      ).
      When considering G×E, it is important to distinguish 1) statistical versus biological interaction, 2) additive versus multiplicative interaction, and 3) gene-environmental correlation (rGE) versus G×E. Statistical interaction of multiple factors is the coefficient of the product term of the risk factors. Although convenient for identifying interactions, it ignores biological plausibility of interaction mechanisms and depends on statistical models (
      • Yang Q.
      • Khoury M.J.
      Evolving methods in genetic epidemiology. III. Gene-environment interaction in epidemiologic research.
      ). Statistical interactions can be artifactual by data anomalies, including data distribution problems, making a careful examination of data characteristics essential when statistical interactions are detected (
      • Kendler K.S.
      • Gardner C.O.
      Interpretation of interactions: Guide for the perplexed.
      ). Biological interaction refers to multiple biological factors acting together to increase or decrease disease risk or the pathophysiologic substrate of disorders (
      • Ahlbom A.
      • Alfredsson L.
      Interaction: A word with two meanings creates confusion.
      ). An example is phenylketonuria, caused by genetic mutation. Having the mutation is not sufficient; environmental phenylalanine in addition to the genetic anomaly is required to create the disease phenotype. Disruption of that interaction (phenylalanine-restricted diet) is sufficient intervention to treat the condition.
      Interactions can be detected as multiplicative or additive. Multiplicative interactions exist when the relative risk of having multiple factors does not equal the product of the relative risks associated with each factor separately (
      • van der Mei I.A.
      • Otahal P.
      • Simpson Jr, S.
      • Taylor B.
      • Winzenberg T.
      Meta-analyses to investigate gene-environment interactions in neuroepidemiology.
      ). Logistic regression implicitly uses multiplicative scales (
      • Ahlbom A.
      • Alfredsson L.
      Interaction: A word with two meanings creates confusion.
      ). Additive interactions exist when the excess risk attributable to multiple factors does not equal the sum of excess risk caused by each factor separately (
      • van der Mei I.A.
      • Otahal P.
      • Simpson Jr, S.
      • Taylor B.
      • Winzenberg T.
      Meta-analyses to investigate gene-environment interactions in neuroepidemiology.
      ). Despite the analytic convenience of multiplicative interactions, use of additive interactions is advocated when examining biological interactions because of its public health implications (
      • Rothman K.J.
      • Greenland S.
      Modern Epidemiology.
      ). Because detection of interactions is depends on the model used, it is possible to have one significant additive or multiplicative interaction but not the other (
      • Kendler K.S.
      • Gardner C.O.
      Interpretation of interactions: Guide for the perplexed.
      ). For example, bladder cancer risk (OR) in individuals with the chr1p13.3 deletion is 1.70 (95% confidence interval, 1.38–2.09), in individuals with smoker status is 3.30 (95% confidence interval, 2.71–4.03), and in individuals with the joint effect is 4.69 (95% confidence interval, 3.86–5.69); the null risks under additive and multiplicative interaction models are 4.00 and 5.61, respectively, making G×E significant only in the additive model (
      • Garcia-Closas M.
      • Rothman N.
      • Figueroa J.D.
      • Prokunina-Olsson L.
      • Han S.S.
      • Baris D.
      • et al.
      Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer.
      ).
      Genetic difference in vulnerability to particular environmental factors that contribute to a phenotype defines G×E. The genetic difference that causes differential exposure to particular environment factors defines rGE; rGE may be heritable, even if consequences of exposure are not (
      • Kendler K.S.
      • Eaves L.J.
      Models for the joint effect of genotype and environment on liability to psychiatric illness.
      ,
      • Jaffee S.R.
      • Price T.S.
      Gene-environment correlations: A review of the evidence and implications for prevention of mental illness.
      ). For example, individuals with antisocial behavior are more likely to engage in risk-taking behaviors such as drinking and driving (
      • Fairchild G.
      • van Goozen S.H.
      • Calder A.J.
      • Goodyer I.M.
      Research review: Evaluating and reformulating the developmental taxonomic theory of antisocial behaviour.
      ); however, increased risk for automobile accidents associated with drinking and driving is an rGE and not genetic. To avoid mistaking rGE as genetic effects or G×E, investigators must be aware that even small rGE may cause type I errors in G×E studies with case-only or case-control designs (
      • Jaffee S.R.
      • Price T.S.
      Gene-environment correlations: A review of the evidence and implications for prevention of mental illness.
      ).
      Several research designs are useful for G×E studies. When shared or nonshared environment data are available, twin studies can provide inferences about specific G×E (
      • Hunter D.J.
      Gene-environment interactions in human diseases.
      ). Discordant MZ twin, adoption, and half-sibling studies can identify environmental factors and G×E (
      • Merikangas K.
      • Karayiorgou M.
      Genetics of psychiatric disorders: Advances in genetic epidemiology and molecular genetics.
      ). Nested case-control studies offer advantages by minimizing selection and recall biases; however, they are not optimal for rare diseases, and selection bias can occur from low participation rates (
      • Hunter D.J.
      Gene-environment interactions in human diseases.
      ). Although case-only designs are efficient for examining G×E, they are based on no rGE and rare disease assumptions; even minor violations of these assumptions cause significant bias (
      • Gatto N.M.
      • Campbell U.B.
      • Rundle A.G.
      • Ahsan H.
      Further development of the case-only design for assessing gene-environment interaction: Evaluation of and adjustment for bias.
      ,
      • Albert P.S.
      • Ratnasinghe D.
      • Tangrea J.
      • Wacholder S.
      Limitations of the case-only design for identifying gene-environment interactions.
      ). In discordant sibling studies, unaffected siblings had fewer perinatal complications than ASD probands but more than controls, suggesting that individuals with ASD react differently to the same environmental stimuli and have less tolerance to perinatal environment compared with siblings (
      • Chaste P.
      • Leboyer M.
      Autism risk factors: Genes, environment, and gene-environment interactions.
      ,
      • Glasson E.J.
      • Bower C.
      • Petterson B.
      • de Klerk N.
      • Chaney G.
      • Hallmayer J.F.
      Perinatal factors and the development of autism: A population study.
      ).
      Inconsistent findings regarding parental smoking and ASD risks may be explained by G×E. One study comparing stillbirths of smoking and nonsmoking mothers found EN2 strongly expressed in arcuate nucleus neurons in fetuses of nonsmokers but not in 11 of 12 fetuses of smoking mothers; however, small sample size and multiple comparisons hamper the interpretations of the results (
      • Lavezzi A.M.
      • Ottaviani G.
      • Matturri L.
      Adverse effects of prenatal tobacco smoke exposure on biological parameters of the developing brainstem.
      ). Intrauterine smoke exposure also damages serotonin projections in cortex and striatum, producing sex-selective changes in 5-hydroxytryptamine 1A and 5-hydroxytryptamine 2 receptor expression and inducing adenyl cyclase causing sensitization of heterologous inputs in this signaling pathway (
      • Xu Z.
      • Seidler F.J.
      • Ali S.F.
      • Slikker Jr, W.
      • Slotkin T.A.
      Fetal and adolescent nicotine administration: Effects on CNS serotonergic systems.
      ,
      • Slotkin T.A.
      • Tate C.A.
      • Seidler F.J.
      Prenatal nicotine exposure alters the responses to subsequent nicotine administration and withdrawal in adolescence: Serotonin receptors and cell signaling.
      ). EN2 and SLC6A4 were associated with ASD in candidate gene studies but not in GWAS. The GWAS replication failures might stem partially from failure to consider interactions between environmental events (in utero nicotine exposure) and genetic vulnerabilities (EN2 or SLC6A4 variants) in increased ASD risk. These G×E hypotheses require further examination.
      Animal studies support G×E in ASD pathogenesis. Pletnikov et al. (
      • Pletnikov M.V.
      • Rubin S.A.
      • Vogel M.W.
      • Moran T.H.
      • Carbone K.M.
      Effects of genetic background on neonatal Borna disease virus infection-induced neurodevelopmental damage. II. Neurochemical alterations and responses to pharmacological treatments.
      ) reported differences in brain pathology, behavior, neurochemistry, and drug response in rats exposed to Borna disease virus, a teratogen causing neurodevelopmental damage and behavioral deficits analogous to ASD. Other animal models, ranging from Caenorhabditis elegans to mice, also demonstrate that genetic defects in synaptic function alter sensitivity to environmental factors, suggesting plausible G×E mechanisms (
      • Chaste P.
      • Leboyer M.
      Autism risk factors: Genes, environment, and gene-environment interactions.
      ).
      Using a case-control design, one group reported three G×E from the same study population. In the study of 429 children with ASD and 278 typical children, maternal MTHFR 677TT (rs1801133), CBS rs234715 GT+TT, and child COMT 472AA (rs4680) genotypes conferred greater ASD risk when the mother did not take vitamins periconceptionally (
      • Schmidt R.J.
      • Hansen R.L.
      • Hartiala J.
      • Allayee H.
      • Schmidt L.C.
      • Tancredi D.J.
      • et al.
      Prenatal vitamins, one-carbon metabolism gene variants, and risk for autism.
      ). However, observed G×E lost significance after multiple comparison corrections. Also, population stratification was not controlled for when investigators reported differences in racial composition between cases and controls. In the second study of 429 children with ASD, 130 children with developmental disorders, and 278 typically developing children, the strongest protective effects of maternal folate intake during the first month of pregnancy were reported in mothers and children with the MTHFR 677C>T variant (
      • Schmidt R.
      • Tancredi D.J.
      • Ozonoff S.
      • Hansen R.L.
      • Hartiala J.
      • Allayee H.
      • et al.
      Maternal periconceptional folic acid intake and risk of autism specturm disorders and developmental delay in the CHARGE (CHilhood Autism Risk from Genetics and Environment) case-control study.
      ). Multiple comparisons were not addressed, and residual population stratification, resulting from crude racial categories and retrospective exposure data collection, are sources of type I error. The third study reported interactions between high pollution and nitrogen dioxide and the MET CC genotype (rs1858830) in 251 cases and 156 control subjects. Air pollution was determined using public air quality data for the area where individuals reported residence at birth (
      • Volk H.
      • Kerin T.
      • Lurmann F.
      • Hertz-Picciotto I.
      • McConnell R.
      • Campbell D.
      Autism spectrum disorder interaction of air pollution with the MET receptor tyrosine kinase gene.
      ). In addition to uncorrected multiple comparisons and population stratification, pollution exposure was measured at a group level leading to concern about ecological fallacy and type I error. All three studies used a candidate gene approach prone to false-positive findings (
      • Duncan L.E.
      • Keller M.C.
      A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry.
      ). None of these G×E findings have been independently replicated.

      Challenges in G×e research and future suggestions

      Studies involving G×E are power-intensive. Even testing for a single G×E specified a priori, the exponential growth in the number of comparisons requires large samples (
      • Boffetta P.
      • Winn D.
      • Ioannidis J.P.
      • Thomas D.
      • Little J.
      • Smith G.
      • et al.
      Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans.
      ). For example, in an unmatched case-control study with a log-additive inheritance model, sample sizes required to examine a G×E effect size of OR 1.5 and 2.0 with 80% power and two-tail p value .05 (without multiple comparison corrections) are 31,084 and 9550, respectively, when prevalence of a disease, environmental risk, and a risk allele are 1%, 5%, and 5% and main effects of environmental and genetic risks are OR 1.2 and 1.2 (
      • Gauderman W.J.
      • Morrison J.M.
      ). Researchers attempt to overcome this challenge in two ways: increasing sample size by establishing large consortia that share data and meta-analyses. Several large-scale, population-based genetic epidemiologic ASD studies are underway, including Norwegian Autism Birth Cohort Study and Mother and Child Cohort Study, Danish Birth Cohort Study, Finnish Birth Cohort Study, and Swedish Registry–based studies as well as U.S.-based Childhood Autism Risks from Genetics and Environment Study and Early Autism Risk Longitudinal Investigation Study (
      • Zerbo O.
      • Iosif A.M.
      • Walker C.
      • Ozonoff S.
      • Hansen R.L.
      • Hertz-Picciotto I.
      Is maternal influenza or fever during pregnancy associated with autism or developmental delays? Results from the CHARGE (CHildhood Autism Risks from Genetics and Environment) study.
      ,
      • Suren P.
      • Roth C.
      • Bresnahan M.
      • Haugen M.
      • Hornig M.
      • Hirtz D.
      • et al.
      Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children.
      ,
      • Stoltenberg C.
      • Schjoberg S.
      • Bresnahan M.
      • Hornig M.
      • Hirtz D.
      • Dahl C.
      • et al.
      The autism birth cohort: A paradigm for gene-environment-timing research.
      ,
      • Madsen K.M.
      • Hviid A.
      • Vestergaard M.
      • Schendel D.
      • Wohlfahrt J.
      • Thorsen P.
      • et al.
      MMR vaccination and autism—a population-based follow-up study [in Danish].
      ,
      • Tran P.L.
      • Lehti V.
      • Lampi K.M.
      • Helenius H.
      • Suominen A.
      • Gissler M.
      • et al.
      Smoking during pregnancy and risk of autism spectrum disorder in a Finnish National Birth Cohort.
      ,
      • Power R.A.
      • Kyaga S.
      • Uher R.
      • MacCabe J.
      • Langstrom N.
      • Landen M.
      • et al.
      Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa or substance abuse vs their unaffected siblings.
      ,
      • Newschaffer C.J.
      • Croen L.A.
      • Fallin M.D.
      • Hertz-Picciotto I.
      • Nguyen D.
      • Lee N.L.
      • et al.
      Infant siblings and the investigation of autism risk factors.
      ). All these studies except Childhood Autism Risks from Genetics and Environment use prospective cohort designs; European studies use population-based representative samples. Biological specimens are collected during pregnancy with repeated phenotype and exposure measurements during follow-up. When combined with attempts to harmonize the exposure and phenotype measurements across studies, power to detect G×E is significantly increased.
      There are not enough methodologically sound G×E ASD studies to conduct meta-analyses. Investigators wishing to conduct valid meta-analyses need ASD G×E studies with ample size, appropriately ascertained samples, detailed phenotypes, sound environmental measurement, state-of-the-art sequencing data, and strong statistical analyses. Investigators and editors must publish both positive and negative findings to minimize publication bias (
      • Boffetta P.
      • Winn D.
      • Ioannidis J.P.
      • Thomas D.
      • Little J.
      • Smith G.
      • et al.
      Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans.
      ).
      Research identifying how environmental factors affect ASD pathogenesis has often been characterized by relatively low levels of accuracy and reliability in the measurement of environmental exposures, leading to both type I and II errors in G×E studies. Environmental measures in ASD G×E studies were based on questionnaire or ecological measures rather than necessary measurement at the individual level. In the cited air pollution G×E study, linking group-level exposures of air pollution to individual-level exposure is difficult because it depends on specific information on exposure duration and individual biological factors, including inhalation or absorption of pollutants, activities, ages, and preexisting health conditions (
      Committee on Human and Environmental Exposure Science in the 21st Century, Board on Environmental Studies and Toxicology, Division on Earth and Life Studies, National Research Council
      Exposure Science in the 21st Century: A Vision and a Strategy.
      ). There is no information about how maternal air pollutant exposure is correlated to fetal exposure in humans. Solving these problems is critical for identifying biomarkers of individual exposures (parental and child) and understanding specific environmental risk and its influence on ASD pathogenesis. Forthcoming technologies, such as geographic information systems and biological monitoring and sensing, enable more precise measurement of environmental exposure at individual levels (
      Committee on Human and Environmental Exposure Science in the 21st Century, Board on Environmental Studies and Toxicology, Division on Earth and Life Studies, National Research Council
      Exposure Science in the 21st Century: A Vision and a Strategy.
      ). Meanwhile, researchers must use established methods to reduce measurement error, including validity and reliability studies for questionnaires and ecological data (
      • White E.
      • Hunt J.R.
      • Casso D.
      Exposure measurement in cohort studies: The challenges of prospective data collection.
      ).
      Systematically ascertained population-based samples representing the entire spectrum of the ASD phenotype are optimal for G×E studies. For cost and convenience, clinical or administrative samples are used more commonly even though they are more vulnerable to selection bias in terms of case severity, comorbidity, and factors associated with access to services (
      • Di Sclafani V.
      • Finn P.
      • Fein G.
      Treatment-naive active alcoholics have greater psychiatric comorbidity than normal controls but less than treated abstinent alcoholics.
      ,
      • Berkson J.
      Limitations of the application of the fourfold table analysis to hospital data.
      ). For example, two studies revealed that sex prevalence discrepancy decreased from 4–5:1 to 2–2.5:1 in epidemiologically ascertained children with ASD (
      • Sandin S.
      • Lichtenstein P.
      • Kuja-Halkola R.
      • Larsson H.
      • Hultman C.M.
      • Reichenberg A.
      The familial risk of autism.
      ,
      • Kim Y.S.
      • Leventhal B.L.
      • Koh Y.J.
      • Fombonne E.
      • Laska E.
      • Lim E.C.
      • et al.
      Prevalence of autism spectrum disorders in a total population sample.
      ). Similarly, the proportion of children with ASD and intellectual disabilities decreases significantly as community ascertainment is more complete (
      • Kim Y.S.
      • Leventhal B.L.
      • Koh Y.J.
      • Fombonne E.
      • Laska E.
      • Lim E.C.
      • et al.
      Prevalence of autism spectrum disorders in a total population sample.
      ,
      Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC)
      Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2010.
      ).
      Heterogeneity of ASD phenotype poses challenges in etiological research, including G×E studies. Dimensional phenotyping using tools already available (e.g., Social Responsiveness Scale) is less vulnerable to phenotype heterogeneity and can complement traditional diagnostic approaches (
      • Abrahams B.S.
      • Geschwind D.H.
      Advances in autism genetics: On the threshold of a new neurobiology.
      ,
      • Constantino J.N.
      • Gruber C.P.
      Social Responsiveness Scale (SRS).
      ).
      Methodologic advances can address preexisting challenges in G×E studies. Experimental models allow G×E testing in animal and cellular systems allowing subsequent population-based G×E studies with more specificity (
      • Stamou M.
      • Streifel K.M.
      • Goines P.E.
      • Lein P.J.
      Neuronal connectivity as a convergent target of gene x environmental interactions that confer risk for Autsim Specturm Disorders.
      ). Advances in technology will allow examination of genetic and environmental factors for ASD risks without a priori hypotheses about candidate genetic and environmental factors (
      • Balshaw D.M.
      • Kwok R.K.
      Innovative methods for improving measures of the personal environment.
      ). There is already successful gene discovery using agnostic approaches through GWAS (
      • Kato N.
      Insights into the genetic basis of type 2 diabetes.
      ). Similarly, proof-of-principle environment-wide association studies are being examined (
      • Balshaw D.M.
      • Kwok R.K.
      Innovative methods for improving measures of the personal environment.
      ). When methods are well established, environment-wide association studies combined with GWAS will provide opportunities for identifying novel G×E mechanisms (
      • Patel C.J.
      • Chen R.
      • Kodama K.
      • Ioannidis J.P.
      • Butte A.J.
      Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus.
      ).

      Conclusions

      New genetic and bioinformatics strategies have yielded important clues to ASD genetic architecture. Recently, a large, epidemiologically ascertained ASD sample estimated liabilities of 2.6% from rare de novo mutation, 3% from rare inherited variants, and 49% from common inherited variants (
      • Gaugler T.
      • Klei L.
      • Sanders S.J.
      • Bodea C.A.
      • Goldberg A.P.
      • Lee A.B.
      • et al.
      Most genetic risk for autism resides with common variation.
      ). Understanding ASD pathogenesis will require more sophistication. Significant effort must focus on how genes and environment interact with one another in typical development and its perturbations, including perturbations that yield ASD. Larger sample sizes are necessary, but they are not sufficient to address complex questions. Study designs should include the following: 1) sample ascertainment that is epidemiologic and population-based to capture the entire ASD spectrum; 2) phenotypic characterization that is both categorical and dimensional; 3) environmental measurement with accuracy, validity, and biomarkers; 4) statistical methods that address challenges such as population stratification, multiple comparisons, and G×E with rare genetic variants (e.g., rare variant burden analyses stratified by environmental exposure status in cases); 5) animal models to test hypotheses before and after conducting studies in human subjects; and 6) new methods including GWAS and environment-wide association studies to broaden the capacity to search for G×E, precise partitioning of heritability with the use of dense genetic markers from GWAS to refine the search for G×E, and consideration of G×E not only as the cause of disease but also as a disease course and outcome modifier (
      • Vinkhuyzen A.A.
      • Wray N.R.
      • Yang J.
      • Goddard M.E.
      • Visscher P.M.
      Estimation and partition of heritability in human populations using whole-genome analysis methods.
      ).
      We have the tools to study and understand G×E with recently identified genes; application of methodologically rigorous studies has major potential for discovering new ASD etiological substrates. Although examination of G×E appears to be a daunting task, tremendous recent progress in gene discovery opens new horizons for advancing our understanding of the role of G×E in the pathogenesis of ASD and ultimately identifying the causes, treatments, and even preventive measures for ASD and other NDDs.

      Acknowledgments and Disclosures

      This work was supported by the National Institutes of Health Grant No. R01 ES021462.
      BLL has consulted for Janssen and has received a research grant from Roche. YSK reports no biomedical financial interests or potential conflicts of interest.

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