The road to precision psychiatry: translating genetics into disease mechanisms

Abstract

Hundreds of genetic loci increasing risk for neuropsychiatric disorders have recently been identified. This success, perhaps paradoxically, has posed challenges for therapeutic development, which are amplified by the highly polygenic and pleiotropic nature of these genetic contributions. Success requires understanding the biological impact of single genetic variants and predicting their effects within an individual. Comprehensive functional genomic annotation of risk loci provides a framework for interpretation of neurobiological impact, requiring experimental validation with in vivo or in vitro model systems. Systems-level, integrative pathway analyses are beginning to elucidate the additive, polygenic contributions of risk variants on specific cellular, molecular, developmental, or circuit-level processes. Although most neuropsychiatric disease modeling has focused on genes disrupted by rare, large-effect-size mutations, common smaller-effect-size variants may also provide solid therapeutic targets to inform precision medicine approaches. Here we enumerate the promise and challenges of a genomics-driven approach to uncovering neuropsychiatric disease mechanisms and facilitating therapeutic development.

Figure 1

Genetic and environmental contribution to liability for neuropsychiatric disease. (a) ACE model liability estimates (see Box 2) are compiled for various neuropsychiatric disorders derived from large-scale twin and/or population-based studies. (b) Genetic contributions can be further partitioned by variant classes, including common, rare inherited, and rare de novo mutations. The contribution of de novo variants to disease liability is lower than their overall frequency in cases due to incomplete penetrance. Data are compiled from refs. ,,–,,,,,–.

Figure 2

Neurobiological framework for interpretation of individual disease-associated variants. (a) When considering a neurobiological framework for interpretation of disease-associated genetic variation, it is most important to begin with variants that meet genome-wide significance thresholds. (b) Independent replication is also critical, which can be supported by prior reported associations in a clinical genetic database (for example, ClinVar) and by an appropriate observed frequency in large population reference databases (for example, ExAC). (c) Functional annotation differs for coding and noncoding variants, although some general principles apply to both (for example, inheritance, evolutionary conservation). For coding variants, the target gene is known and annotation is initially based on impact to the amino acid sequence. Synonymous mutations, often interpreted as neutral, can contribute to human disease risk by changing transcription factor or microRNA binding or by altering mRNA stability or secondary structure. Nonsense, frameshift, and canonical splice site mutations are generally placed in the most deleterious, likely gene disrupting category, although their disease association must still be statistically supported. Interpretation of missense mutations is more difficult, relying typically on evolutionary constraint or by inferred disruption of protein structure or biochemical function. Functional annotation of noncoding variants is a rapidly evolving area, but can be broadly conceptualized as (top) predicting a regulatory effect and (bottom) identifying target gene(s). Computational methods can predict the likelihood that noncoding regions act as enhancers, repressors, or insulators within a given tissue or cell line on the basis of epigenetic annotations. Gene targets can be inferred through statistical frameworks such as eQTL or by mapping intrachromosomal physical binding interactions through chromosome conformation capture methods. (d) Predictions of the potential impact of a variant on the target gene should be experimentally validated. Gene-level disruption can be confirmed in a cell-based experimental system, as long as genomic and epigenetic context are considered. Model organisms with construct validity may also be useful. (e) Once the proximal biological effect of a disease-associated variant is determined, disease mechanisms can begin to be inferred through follow up investigation in preclinical or clinical settings. Performing comprehensive clinical and medical phenotyping of individuals harboring specific, known disease-associated variants will be especially important for mechanistic insight as well as future ‘genotype-first’ precision medicine approaches. NHGRI, National Human Genome Research Institute; EBI, European Bioinformatics Institute; ATAC-seq, assay for transposase-accessible chromatin with sequencing; DHS, DNase I hypersensitivity sites; ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; TSS, transcription start site; SIFT, sorting intolerant from tolerant; MAPP, multivariate analysis of protein polymorphism.