Genomewide association studies: history, rationale, and prospects for psychiatric disorders.
- Authors
- Psychiatric GWAS Consortium Coordinating Committee; Cichon, Sven; Craddock, Nick; Daly, Mark; Faraone, Stephen V; Gejman, Pablo V; Kelsoe, John; Lehner, Thomas; Levinson, Douglas F; Moran, Audra; Sklar, Pamela; Sullivan, Patrick F
- Year
- 2009
- Journal
- The American journal of psychiatry
- PMID
- 19339359
- DOI
- 10.1176/appi.ajp.2008.08091354
- PMCID
- PMC3894622
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.
Relationship among power, GRR (multiplicative inheritance) and sample sizeThe graphs show expected power (91) for a disease with 1% population prevalence (p - 5 Γ 10β8), 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: GRR. Power increases with GRR.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.Mode of transmission. Power is greater for dominant and multiplicative (log additive) genetic effects, and less for recessive effects (particularly for rare alleles).Selection of controls. For diseases with higher prevalence (e.g., >> 5%), power increases if controls with the disorder/trait of interest are excluded.(40)Technical artifacts of all kinds can reduce power.
| Name | Type |
|---|---|
| 1000 Genomes Project | cohort |
| 15q13.3 deletion | variant |
| 16p11.2 deletion | variant |
| 1q21.1 deletion | variant |
| 22q11.2 deletion | variant |
| 8q24.21 local | variant |
| Adaptive SNPs local | variant |
| ADHD | phenotype |
| African | cohort |
| alcohol | phenotype |
| Alcohol Use Disorder | phenotype |
| Alzheimerβs disease | phenotype |
| ANK3 | gene |
| Asian | cohort |
| attention deficit hyperactivity disorder | phenotype |
| autism | phenotype |
| bipolar disorder | phenotype |
| blood donors local | cohort |
| BMI | phenotype |
| breast cancer | phenotype |
| cancer | phenotype |
| case group | cohort |
| case subjects | cohort |
| CNV | variant |
| CNVs | variant |
| colorectal cancer | phenotype |
| combined schizophrenia-bipolar dataset local | cohort |
| common-SNP local | variant |
| common variants | cohort |
| complex diseases | phenotype |
| complex disorders | phenotype |
| continuous trait local | phenotype |
| control group | cohort |
| control groups | cohort |
| control subjects | cohort |
| copy number variants | variant |
| developmental disabilities | phenotype |
| Dgkh | gene |
| disease | phenotype |
| disease genes | gene |
| disease-related sequence variation local | phenotype |
| disease risk | phenotype |
| disorder | phenotype |
| DNA methylation | drug |
| DNA sequence variations (markers) local | variant |
| European ancestry | cohort |
| European population | cohort |
| families | cohort |
| FTO | gene |
| FTO SNPs local | variant |
| GAIN | cohort |
| gene expression | phenotype |
| genetic risk | cohort |
| GWAS | cohort |
| HapMap | cohort |
| HapMap3 | cohort |
| Heritable phenotype local | phenotype |
| hypertension | phenotype |
| ill cases local | cohort |
| illicit drugs | phenotype |
| illicit drug use | phenotype |
| inflammatory bowel diseases local | phenotype |
| initial GWAS samples local | cohort |
| International consortium of psychiatric GWAS investigators local | cohort |
| intronic_SNP local | variant |
| inversions | variant |
| large collaborative samples local | cohort |
| Large deletions on chromosome 22q11 local | variant |
| large samples local | cohort |
| lipid metabolism | phenotype |
| major depressive disorder | phenotype |
| major psychiatric disorders | phenotype |
| mental retardation | phenotype |
| microRNA | drug |
| nicotine | drug |
| nicotine use disorder | phenotype |
| Non-psychiatric disorders local | phenotype |
| non-synonymous SNP | variant |
| nsSNP | variant |
| obesity | phenotype |
| PGC | cohort |
| PGC meta-analysis | cohort |
| population | cohort |
| population-based birth cohort local | cohort |
| Population substructure local | phenotype |
| prostate cancer | phenotype |
| psychiatric disorders | phenotype |
| Psychiatric Genomics Consortium | cohort |
| psychosis | phenotype |
| psychotropic medication | drug |
| rare CNVs | variant |
| rare long CNV local | variant |
| rare pathogenic variant local | variant |
| rare_pathogenic_variant local | variant |
| rare variant | cohort |
| replication sample | cohort |
| risk allele | cohort |
| schizophrenia | phenotype |
| single nucleotide polymorphisms | variant |
| SNP | cohort |
| SNP array local | drug |
| SNP in ZNF804A local | variant |
| structural variant | cohort |
| substance | phenotype |
| substance use | phenotype |
| synonymous_exonic_SNP local | variant |
| synonymous exonic SNPs local | variant |
| TCF7L2 | gene |
| translocation | variant |
| Twin cohort | cohort |
| type 2 diabetes | phenotype |
| type 2 diabetes (T2D) local | phenotype |
| UK Wellcome Trust Case Control Consortium local | cohort |
| Velocardiofacial/DiGeorge syndrome local | phenotype |
| WTCCC | cohort |
| WTCCC bipolar disorder cases local | cohort |
| WTCCC controls local | cohort |
| Zeggini et al. study local | cohort |
| ZNF804A | gene |
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