Comment on “Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets” by Lam et al.

Abstract

Lam et al. (2017) reported a large-scale genome-wide association study (GWAS) of cognitive ability. They used the new analytical method of Multi-Trait Analysis of GWAS (MTAG) (Turley et al., 2017) to combine GWAS data sets on the correlated phenotypes of cognitive ability and education, deriving 70 loci that they described as “trait specific” to cognitive ability. The purpose of this short commentary is to examine whether the use of MTAG, in this case (Lam et al., 2017), has resulted in a phenotype more similar to education than cognitive ability.

The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls

“…The genetic correlation with IQ was 20 significant (rg = -0.13, p = 5x10 -7 ), because the excluded 23andMe depression 21 cohort has a positive genetic correlation with IQ (rg = 0.06, p = 0.01). The greater 22 genetic correlation of MOOD with combined MDD (0.98) compared to PGC BD 23 (0.55) persisted when comparing down-sampled MOOD to combined MDD (0.85) 24 and PGC BD (0.75; Supplementary Table 6). Full genetic correlation results are provided in Supplementary Table 5.…”

“…10 Sensitivity analysis using down-sampled PGC MDD 11 Results from MOOD showed greater similarity to PGC MDD than to PGC BD. 12 Cross-trait meta-analyses may be biased if the power of the composite analyses 13 differs substantially (23,24). The mean chi-square of combined MDD [1.7] exceeded 14 that of PGC BD [1.39], suggesting this bias may affect our results ( Supplementary 15 Table 2).…”

“…21 Summary statistics were limited to common variants (MAF > 0.05; 22 Supplementary Methods) either genotyped or imputed with high confidence (INFO 23 score > 0.6) in all studies. Controls were shared between PGC MDD and PGC BD, 24 and (due to the inclusion of summary data in PGC MDD) the extent of this overlap 1 was unknown. Meta-analyses were therefore performed in METACARPA, which 2 controls for sample overlap of unknown extent between studies using the variance- 3 covariance matrix of the observed effect sizes at each variant, weighted by the 4 sample sizes (21,22).…”

“…Full results 19 of the down-sampled analyses are described in the Supplementary Materials. 20 Estimation of SNP-based heritability captured by common variants and genetic 21 correlations with published GWAS 22 The SNP-based heritability captured by common variants was assessed using 23 linkage disequilibrium score regression (LDSC) for each meta-analysed set of data 24 9 (25). SNP-based heritability estimates were transformed to the liability scale, 1 assuming population prevalences of 15% for combined MDD, 1% for PGC BD, and 2 16% for MOOD, and lower and upper bounds of these prevalences for comparison 3 (Supplementary Methods).…”

“…20 Hierarchical clustering was performed using just the subtypes, and including results 21 from six external GWAS relevant to mood disorders (Supplementary Methods). To 22 validate our conclusion of a genetic mood disorder spectrum, we performed principal 23 component analysis of the genetic correlation matrix including the six external 24 GWAS (Supplementary Methods and Results). 25 Association of PGC BD polygenic risk scores with major depressive disorder 1 subtypes 2 Polygenic risk score analyses were performed using PRSice2 to assess 3 whether rMDD was genetically more similar to PGC BD than were sMDD or subMDD 4 (Supplementary Methods) (36).…”