Genomewide association studies: history, rationale, and prospects for psychiatric disorders

Abstract

Objective: The authors conducted a review of the history and empirical basis of genomewide association studies (GWAS), the rationale for GWAS of psychiatric disorders, results to date, limitations, and plans for GWAS meta-analyses.

Method: A literature review was carried out, power and other issues discussed, and planned studies assessed.

Results: Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.

Conclusions: GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.

Figure 1. Relationship among power, GRR (multiplicative inheritance) and sample size

The graphs show expected power (91) for a disease with 1% population prevalence (p - 5 × 10⁻⁸), depending on minor (less frequent) allele frequency of the tested SNP, sample size (assuming the N of cases shown in the graph legend, and the same N of controls (power is similar for the same N of case-parent trios), and genotypic relative risk (GRR), which is the ratio of the risk of disease to carriers of a particular genotype vs. non-carriers (thus, if GRR is 1.2, risk is increased by 20%). The calculations assume indirect association between a tested SNP allele and a risk allele at a correlation (r²) of 0.8, so that the effective sample sizes are approximately 80% of those shown. A sample of 8,000 cases and 8,000 controls will miss most associated alleles that confer much less than a 20% increase in risk (GRR << 1.2), whereas 20,000/20,000 would detect most associated alleles with GRR = 1.12 and frequency > 15-20%. Factors that affect power include:

1. GRR. Power increases with GRR.

2. Allele frequency and LD. Power increases with the minor allele frequency of the associated SNP and with stronger LD between than SNP and an untested risk allele.

3. Mode of transmission. Power is greater for dominant and multiplicative (log additive) genetic effects, and less for recessive effects (particularly for rare alleles).

4. Selection of controls. For diseases with higher prevalence (e.g., >> 5%), power increases if controls with the disorder/trait of interest are excluded.(40)

5. Technical artifacts of all kinds can reduce power.