Genome-wide association studies (GWAS) have been a common theme on these GEITP pages. Researchers select a phenotype (trait) and then screen the entire genome (looking at differences in single-nucleotide variants; SNVs) of hundreds or thousands of individuals (cohort) — looking for markers in, or close, to a gene that might be highly statistically significantly associated with the trait. GWAS can help find “small-effect” genes — especially those contributing to quantitative traits (e.g. height, body mass index), complex diseases (e.g. type-2 diabetes, schizophrenia), and responses to drugs (toxicity, efficacy) or environmental toxicants (toxicity). Expression quantitative trait loci (eQTLs) are responsible for a substantial proportion of gene-expression variance; eQTLs appear to be a link between associated loci and disease susceptibility and have yielded results for many complex traits. Consequently, numerous methods to identify and interpret co-localization of eQTLs and GWAS loci have been developed.
However, these methods: [a] require simplifying assumptions about genetic architecture (i.e. one causal variant per GWAS locus) and/or linkage disequilibrium (LD; the non-random association of alleles at different loci in a given population); [b] may be underpowered or overly conservative, especially with heterogeneous alleles; [c] and have not yet yielded substantial insights into disease biology. Biologically relevant transcriptomic information (mRNAs that have been transcribed from all active genes’ DNA) can be extracted through detailed RNA-sequencing (RNA-seq) — as recently described by the CommonMind Consortium (CMC) in a large cohort of genotyped individuals with schizophrenia and bipolar disorder. However, these analyses are statistically underpowered to detect significant differential expression of small-effect genes mapping at schizophrenia risk loci (due to the small effects predicted by GWAS, combined with difficulty of obtaining adequate sample sizes of neurological tissues, such as brain..!!) and do not necessarily identify all risk variation in GWAS loci.
Transcriptomic imputation [see attached article] is an alternative approach that leverages large eQTL reference panels to bridge the gap between large-scale genotyping studies and biologically useful transcriptome studies. [Imputation is a magical ‘smoke-and-mirrors’ statistical inference of unobserved genotypes, achieved by using known haplotypes in a population (e.g. from the human HapMap or 1000 Genomes Projects) — thereby allowing to test for associations between a trait of interest (e.g. a disease) and experimentally untyped genetic variants, but whose genotypes have been statistically inferred (“imputed”). In other words, “other people have this SNV pattern, so maybe this person also has this pattern“.] Transcriptomic imputation approaches codify relationships between genotype and gene expression in matched panels of individuals, then impute the genetic component of the transcriptome into large-scale genotype-only datasets (e.g. case-control GWAS cohorts), enabling investigation of disease-associated gene-expression changes. This allows geneticists to study genes having “modest effect-sizes“, which likely represents a large proportion of genomic risk genes in psychiatric disorders.
Authors [see attached article] used the largest eQTL reference panel available for the dorso-lateral prefrontal cortex of brain (DLPFC) to create a set of gene expression predictors and demonstrate their utility. They applied DLPFC and 12 Genotype-Tissue-Expression-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. Authors identified 413 gene x gene associations across 13 brain regions. Stepwise conditioning identified 67 non-immune-related genes, of which 14 did not fall within previous GWAS loci. They identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. Lastly, authors investigated developmental expression patterns among the 67 non-immune-related genes and identified specific groups of pre- and post-natal expression. These are complex approaches to a very complex disease..!! 🙂
Nat Genet Apr 2o19; 51: 659-674