The standard approach in genome-wide association studies (GWAS), a topic that is often shared in these GEITP pages, is to analyze one single trait at a time. However, such an approach does not really achieve all the information contained in summary statistics from GWAS of related traits. In the [attached] article, authors have developed a method –– multi-trait analysis of GWAS (MTAG) –– that enables the joint analysis of multiple traits, thereby boosting statistical power to detect genetic associations for each trait analyzed. In comparison to the many existing multi-trait methods, authors believe that MTAG has the unique combination of four features that make it potentially useful in many settings:
First, it can be applied to GWAS summary statistics from an arbitrary number of traits (without access to individual-level data).
Second, summary statistics need not be derived from independent discovery samples [MTAG uses bivariate linkage disequilibrium (LD) score regression to account for (possibly unknown) sample overlap between GWAS results for different traits].
[Remember: LD is defined as “the nonrandom association of alleles at two or more loci in a general population; when alleles are “in LD,” haplotypes (group of genes on the same chromosome that was inherited together from a single parent) occur at much higher-than-expected frequencies. LD between two alleles is thus related to the time of the mutation events, genetic distance along the chromosome, and population history; LD can of course be used to improve the power of GWAS.]
Third, MTAG generates trait-specific effect-size estimates for each single nucleotide variant (SNV).
Fourth, even when applied to many traits, MTAG is computationally quick, because every step has a closed-form solution.
In the attached article, authors applied MTAG to summary statistics for three phenotypes (traits): symptoms of major depressive disorder (MDD) in an effective population size (Neff) of 354,862; neurotic behavior (N = 168,105), and subjective well-being (N = 388,538). When compared to the 32, 9, and 13 genome-wide significant loci identified, respectively, in single-trait GWAS (most of which are themselves novel) –– MTAG was shown to increase the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yielded more informative bioinformatics analyses and increased the variance explained (In statistics, ‘variance explained’ measures the proportion to which a mathematical model accounts for the variation of a given data set) by polygenic scores –– by approximately 25%.
Nature Genet Feb 2o18; 50: 229–237