Psychiatric Genomics: An Update and an Agenda

Abstract

The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. This global effort is dedicated to rapid progress and open science, and in the past decade it has delivered an increasing flow of new knowledge about the fundamental basis of common psychiatric disorders. The PGC has recently commenced a program of research designed to deliver "actionable" findings-genomic results that 1) reveal fundamental biology, 2) inform clinical practice, and 3) deliver new therapeutic targets. The central idea of the PGC is to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering a phase of accelerated genetic discovery for multiple psychiatric disorders. These findings are likely to elucidate the genetic portions of these truly complex traits, and this knowledge can then be mined for its relevance for improved therapeutics and its impact on psychiatric practice within a precision medicine framework. [AJP at 175: Remembering Our Past As We Envision Our Future November 1946: The Genetic Theory of Schizophrenia Franz Kallmann's influential twin study of schizophrenia in 691 twin pairs was the largest in the field for nearly four decades. (Am J Psychiatry 1946; 103:309-322 )].

Keywords: Biological Markers; Genetics.

Figure 1. Theoretical power of GWAS and observed genetic effect sizes

a. Statistical power of GWAS (in theory). Upper curve ( ) shows minimum detectable genotypic relative risks for common variants for 1,000 cases and 1,000 controls (90% power, additive model, lifetime risk 0.01, α=5e-8). Lower curve ( ) shows 90% power for the PGC 2014 schizophrenia paper (37,000 cases and 113,000 controls, additive model, lifetime morbid risk 0.01, α=5×10^{− 8}). Black dots show the top 10 loci in the PGC schizophrenia report. These loci are highly significant with P-values ranging from 1.7×10⁻¹³ to 3.8×10⁻³². b. Odds ratios (OR, log₁₀ scale) and allele frequencies from published GWAS. From EBI-NHGRI GWAS catalog (accessed 1/27/2017), contains 2,308 GWAS papers published 3/2005–7/2016. There are 9,485 SNP-trait associations (P ≤ 1×1⁻⁸) including 7,487 SNPS and 870 traits. Dots show frequency and OR (transformed to be >1 and allele frequencies to 0–0.50). Contours show densest areas of the plot. Horizontal lines show 50^th (OR=1.22) and 90^th (OR=1.95) percentiles for ORs: most associations are subtle. Of 62 associations with OR>5, most are for infectious disease (N=31; e.g., influenza susceptibility), pharmacogenomic (N=13; e.g., rare adverse drug reactions like flucloxacillin-induced liver injury), eye disease (N=4; e.g., glaucoma), or pigmentation (N=2; e.g., blue vs. brown eyes). Only a few diseases have atypically large ORs (e.g., celiac disease, melanoma, membranous nephropathy, myasthenia gravis, ovarian cancer, Parkinson's disease, progressive supranuclear palsy, thyrotoxic hypokalemic periodic paralysis, and type 1 diabetes). The only psychiatric finding was alcohol consumption and ALDH2 in individuals of East Asian ancestry.

Figure 2. The types of genetic variants empirically associate with severe psychiatric disorders

a. Genetic causes of severe intellectual disability (ID) (58), autism spectrum disorder (ASD) (59, 60), and schizophrenia (SCZ) (61), including copy number variation (CNV), inherited known recessives, and single nucleotide variants (SNV). For severe ID, most SNV and CNV are de novo. The unknown grouping includes common variation, undiscovered rare genetic causes, phenocopies, and causation due to non-genetic effects. b. Significant genetic associations for schizophrenia. Y-axis is log₁₀ of odds ratio. X-axis is log₁₀ of allele frequency in controls. Odds ratios transformed to be >1 and frequencies to be ≤ 0.5. The dots on the lower right ( ) shows common-variant associations for schizophrenia (P<1e-8) (19). Open diamonds ( ) show copy number variation associated with schizophrenia (34). Filled square ( ) shows the lone variant identified using whole exome sequencing (35).

Figure 3. GWAS sample sizes and rates of discovery

a. Numbers of cases for PGC GWAS analyses. For the original five PGC disorders (ADHD..SCZ), there are three bars for the numbers of cases in the initial “PGC1” reports, the next round of papers (“PGC2”), and the projected numbers by 2019. For the four disorders added in 2013 (ED..SUD), the PGC2 and projected PGC3 numbers are shown. Abbreviations: ADHD=attention-deficit hyperactivity disorder, AUT=autism, BIP=bipolar disorder, MDD=major depressive disorder, SCZ=schizophrenia, ED=eating disorders, OCD/TS=obsessive-compulsive disorder/Tourette syndrome, PTSD=posttraumatic stress disorder, and SUD=substance use disorders. b. Relation between numbers of cases and genome-wide significant SNPs in GWAS. Lines show discovery paths for inflammatory bowel disease (IBD), schizophrenia (SCZ), height, and bipolar disorder (BIP). IBD has an exceptional genetic architecture and excellent clinical diagnostic specificity that enabled considerable discovery with relatively smaller numbers of cases. SCZ, height, and BIP follow more typical and approximately similar discover paths c. Cartoon of hypothetical relation between number of cases and genome-wide significant associations for a human complex disease or trait. There is an initial dead zone whose length depends how many cases are accrued and the largest effect size. This is followed by an inflection point where the significant associations begin to accumulate and then a linear phase. Complexities arising from the true nature of the initially unknown genetic architecture could change the form of this curve importantly.

Figure 4. Examples of genomic regions significantly associated with schizophrenia

Examples of genome-wide significant regions for schizophrenia with tracks showing the location (hg19), genes in the region, GWAS results from the literature, and the schizophrenia results (one green vertical bar per SNP, height corresponds to −log₁₀(P-value) with 7.3 equivalent to 5×10⁻⁸. (a) intronic association in CACNA1C, (b) association mostly upstream of DRD2, (c) multigenic association, and (d) intergenic association.