Interpreting the role of de novo protein-coding mutations in neuropsychiatric disease

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power.

Association claims in the sequencing era

Since the completion of the Human Genome Project, the field of human genetics has been in great flux, largely due to technological advances in studying DNA sequence variation. Although community-wide adoption of statistical standards was key to the success of genome-wide association studies, similar standards have not yet been globally applied to the processing and interpretation of sequencing data. It has proven particularly challenging to pinpoint unequivocally disease variants in sequencing studies of polygenic traits. Here, we comment on a number of factors that may contribute to irreproducible claims of association in scientific literature and discuss possible steps that we can take towards cultural change.

The effect of paternal age on offspring intelligence and personality when controlling for paternal trait level

Paternal age at conception has been found to predict the number of new genetic mutations. We examined the effect of father’s age at birth on offspring intelligence, head circumference and personality traits. Using the Minnesota Twin Family Study sample we tested paternal age effects while controlling for parents’ trait levels measured with the same precision as offspring’s. From evolutionary genetic considerations we predicted a negative effect of paternal age on offspring intelligence, but not on other traits. Controlling for parental intelligence (IQ) had the effect of turning an initially positive association non-significantly negative. We found paternal age effects on offspring IQ and Multidimensional Personality Questionnaire Absorption, but they were not robustly significant, nor replicable with additional covariates. No other noteworthy effects were found. Parents’ intelligence and personality correlated with their ages at twin birth, which may have obscured a small negative effect of advanced paternal age (<1% of variance explained) on intelligence. We discuss future avenues for studies of paternal age effects and suggest that stronger research designs are needed to rule out confounding factors involving birth order and the Flynn effect.

Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism

Genetic studies have identified dozens of autism spectrum disorder (ASD) susceptibility genes, raising two critical questions: (1) do these genetic loci converge on specific biological processes, and (2) where does the phenotypic specificity of ASD arise, given its genetic overlap with intellectual disability (ID)? To address this, we mapped ASD and ID risk genes onto coexpression networks representing developmental trajectories and transcriptional profiles representing fetal and adult cortical laminae. ASD genes tightly coalesce in modules that implicate distinct biological functions during human cortical development, including early transcriptional regulation and synaptic development. Bioinformatic analyses suggest that translational regulation by FMRP and transcriptional coregulation by common transcription factors connect these processes. At a circuit level, ASD genes are enriched in superficial cortical layers and glutamatergic projection neurons. Furthermore, we show that the patterns of ASD and ID risk genes are distinct, providing a biological framework for further investigating the pathophysiology of ASD.

A de novo convergence of autism genetics and molecular neuroscience

Autism spectrum disorder (ASD) and intellectual disability (ID) are neurodevelopmental disorders with large genetic components, but identification of pathogenic genes has proceeded slowly because hundreds of loci are involved. New exome sequencing technology has identified novel rare variants and has found that sporadic cases of ASD/ID are enriched for disruptive de novo mutations. Targeted large-scale resequencing studies have confirmed the significance of specific loci, including chromodomain helicase DNA binding protein 8 (CHD8), sodium channel, voltage-gated, type II, alpha subunit (SCN2A), dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A), and catenin (cadherin-associated protein), beta 1, 88 kDa (CTNNB1, beta-catenin). We review recent studies and suggest that they have led to a convergence on three functional pathways: (i) chromatin remodeling; (ii) wnt signaling during development; and (iii) synaptic function. These pathways and genes significantly expand the neurobiological targets for study, and suggest a path for future genetic and functional studies.

The future of genomics for developmentalists

The momentum of genomic science will carry it far into the future and into the heart of research on typical and atypical behavioral development. The purpose of this paper is to focus on a few implications and applications of these advances for understanding behavioral development. Quantitative genetics is genomic and will chart the course for molecular genomic research now that these two worlds of genetics are merging in the search for many genes of small effect. Although current attempts to identify specific genes have had limited success, known as the missing heritability problem, whole-genome sequencing will improve this situation by identifying all DNA sequence variations, including rare variants. Because the heritability of complex traits is caused by many DNA variants of small effect in the population, polygenic scores that are composites of hundreds or thousands of DNA variants will be used by developmentalists to predict children's genetic risk and resilience. The most far-reaching advance will be the widespread availability of whole-genome sequence for children, which means that developmentalists would no longer need to obtain DNA or to genotype children in order to use genomic information in research or in the clinic.

The genomics of schizophrenia: update and implications

Schizophrenia is strongly familial yet rarely (if ever) exhibits classical Mendelian inheritance patterns. The advent of large-scale genotyping and sequencing projects has yielded large data sets with higher statistical power in an effort to uncover new associations with schizophrenia. Here, we review the challenges in dissecting the genetics of schizophrenia and provide an update of the current understanding of the underlying genomics. We discuss the breadth of susceptibility alleles, including those that may occur with low frequency and high disease risk, such as the 22q11.2 hemideletion, as well as alleles that may occur with greater frequency but convey a lower risk of schizophrenia, such as variants in genes encoding subunits of the voltage-gated L-type calcium channel. Finally, we provide an overview of the clinical implications for the diagnosis and treatment of schizophrenia based on progress in understanding the underlying genetic basis.