GWAS for executive function and processing speed suggests involvement of the CADM2 gene

Abstract

To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429–32 070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value = 3.12 × 10−8) and in the joint discovery and replication meta-analysis (P-value = 3.28 × 10−9 after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value = 4 × 10−4). The protein encoded by CADM2 is involved in glutamate signaling (P-value = 7.22 × 10−15), gamma-aminobutyric acid (GABA) transport (P-value = 1.36 × 10−11) and neuron cell-cell adhesion (P-value = 1.48 × 10−13). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.

Evidence for Recent Polygenic Selection on Educational Attainment Inferred from GWAS Hits

“…Thus, a goal of this study is to test the results obtained by Piffer (2013Piffer ( , 2015 against the genetic variants found by the latest GWAS of educational attainment. The important question is whether the new GWAS findings support the results obtained by Piffer (2015) using the first GWAS of this kind (Rietveld et al, 2013) and subsequent studies on general cognitive ability (Davies et al, 2015;Ibrahim-Verbaas et al,, 2015). In other words, the question is: are the population frequencies of the latest GWAS hits correlated to the frequencies of the original GWAS hits employed by Piffer (2013Piffer ( , 2015?…”

Genetic contributions to Trail Making Test performance in UK Biobank

“…32 LD score regression was also used to estimate genetic correlations between trail making measures in UK Biobank and trail making part A, trail making part B, general cognitive function, processing speed and memory from the CHARGE consortium (participants aged 45 years or older) meta-analyses of these cognitive phenotypes. 31,33,51 Gene-based association analysis MAGMA 52 was used to derive gene-based associations using the summary results of the three GWAS for trail making. Genes (18 354) were analysed after the SNPs were assigned a gene based on their position using the Genetic contributions to TMT performance SP Hagenaars et al NCBI 37.3 build, without additional boundaries placed around the genes.…”

“…The gene-based associations for trail making were compared with gene-based associations for VNR and with the gene-based associations for trail making, general cognitive function, processing speed and memory from the CHARGE consortium, based on the GWAS summary results. 31,33,51 The CHARGE summary results were converted from HapMap2 to 1000G format, to ensure the maximum overlap between the two samples. This was achieved using the LiftOver programme, which converts coordinate ranges between genome assemblies.…”

“…10,[14][15][16][17][18][19][20] There is also evidence for performance deficits on TMT B in mood disorders 21 and in patients with schizophrenia and their relatives 18,[22][23][24][25][26][27] Family-based and twin-based studies have provided evidence for a genetic contribution to individual differences in trail making, estimating the heritability for trail making part A between 0.23 and 0.38, and between 0.39 and 0.65 for trail making part B. [28][29][30] A recent genome-wide association study (GWAS) of trail making part A and part B in a sample of around 6000 individuals did not find any genome-wide significant hits; 31 however, GWAS of other cognitive phenotypes have demonstrated that much larger sample sizes are required to reliably identify significant genetic loci. 32,33 Trail making is thought to have genetic influences that are shared with other cognitive abilities, with a twin-based genetic correlation of 0.48 reported between trail making, measured as the ratio between trail making part A and trail making part B, and general cognitive function, and 0.52 with working memory.…”

“…The largest TMT GWAS (N~6000) to date used a variety of assessments across multiple cohorts across multiple countries, potentially leading to heterogeneity in both sample composition and in the cognitive measures. 31 The current study design, using 423 000 UK Biobank participants, mitigates such confounds: the almost fourfold increase in sample size yields greater statistical power, and the single sample reduces heterogeneity in genetic testing and in phenotype, because all participants had white British ancestry and took the same TMT test with the same instructions. In addition, we add to this significantly larger GWAS than has previously been conducted with (1) estimates of the single-nucleotide polymorphism (SNP)-based heritability of TMT performance and (2) an examination of genetic overlap among the different TMT test measures and with other cognitive functions, using polygenic profile analysis and genetic correlations in independent samples.…”