Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53 949)

Davies, Gail; Armstrong, Nicola J.; Bis, Joshua C.; Bressler, Jan; Chouraki, Vincent; Giddaluru, Sudheer; Hofer, Edith; Ibrahim-Verbaas, Carla A.; Kirin, Mirna; Lahti, Jari; Lee, S. J. Van Der; Hellard, Stéphanie Le; Liu, T.; Marioni, Riccardo E.; Oldmeadow, Christopher; Postmus, Iris; Smith, Albert V.; Smith, Jennifer A.; Thalamuthu, Anbupalam; Thomson, Russell; Vitart, Véronique; Wang, J.; Yu, Lei; Zgaga, Lina; Zhao, Wei; Boxall, Ruth; Harris, Sarah E.; Hill, W. David; Liewald, David C.; Luciano, Michelle; Adams, Hieab H.H.; Ames, David; Amin, Najaf; Amouyel, Philippe; Assareh, Amelia A.; Au, Rhoda; Becker, James T.; Beiser, Alexa; Berr, Claudine; Bertram, Lars; Boerwinkle, Eric; Buckley, B. M.; Campbell, Harry; Corley, Janie; Jager, Philip L. De; Dufouil, Carole; Eriksson, Johan G.; Espeseth, Thomas; Faul, Jessica D.; Ford, Ian; Scotland, Generation; Gottesman, R. F.; Griswold, Michael; Gudnason, Vilmundur; Harris, Tamara B.; Heiss, Gerardo; Hofman, Albert; Holliday, Elizabeth G.; Huffman, Jennifer E.; Kardia, Sharon L.R.; Kochan, Nicole A.; Knopman, David S.; Kwok, John B.; Lambert, Jean‐Charles; Lee, T.; Li, G.; Li, S. C.; Loitfelder, Marisa; López, Oscar L.; Lundervold, Astri J.; Lundqvist, Annamari; Mather, Karen A.; Mirza, Saira Saeed; Nyberg, Lars; Oostra, Ben A.; Palotie, Aarno; Papenberg, Göran; Pattie, Alison; Petrovic, Katja; Polašek, Ozren; Psaty, Bruce M.; Redmond, Paul; Reppermund, Simone; Rotter, Jerome I.; Schmidt, Helena; Schuur, Maaike; Schofield, Peter R.; Scott, Rodney J.; Steen, Vidar M.; Stott, David J.; Swieten, John van; Taylor, Kent D.; Trollor, Julian N.; Trompet, Stella; Uitterlinden, André G.; Weinstein, Galit; Widén, Elisabeth; Windham, B. Gwen; Jukema, J. Wouter; Wright, Alan F.; Wright, Margaret J.; Yang, Qiong; Amieva, Hélène; Attia, John; Bennett, David A.; Brodaty, Henry; Craen, Anton J. M. de; Hayward, Caroline; Ikram, M. Arfan; Lindenberger, Ulman; Nilsson, Lars Göran; Porteous, David J.; Räikkönen, Katri; Reinvang, Ivar; Rudan, Igor; Sachdev, Perminder S.; Schmidt, Reinhold; Srikanth, Velandai; Starr, John M.; Turner, Stephen T.; Weir, David R.; Wilson, James F.; Duijn, Cornelia M. van; Launer, Lenore J.; Fitzpatrick, A.; Seshadri, Sudha; Mosley, TH; Deary, Ian J.

doi:10.1038/mp.2014.188

Cited by 350 publications

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“…Performance on cognitive tasks varies between individuals and is highly heritable 1 and polygenic 2,3 . However, to date, progress in identifying molecular genetic contributions to healthy human cognitive abilities has been limited 4,5 .A distinction can be made between cognitive domains such as the ability to apply acquired knowledge and learned skills (so called crystallized abilities) and fluid cognitive abilities such as the capacity to establish new memories, reason in novel situations or perform cognitive tasks accurately and quickly 6 . Notably, within individuals, performance on different measures of cognitive ability tend to be positively correlated such that people who do well in one domain, such as memory, tend to do well in other domains 7 .…”

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Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

et al. 2015

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2Genetic determinants of cognition are poorly characterized and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here, we used a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities including memory. Using exome sequence data from 6,871 trios, we find that M3 genes are also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease genes in the developed human brain, and provide empirical support for a convergent generegulatory network influencing cognition and neurodevelopmental disease.Cognition refers to human mental abilities such as memory, attention, processing speed, reasoning and executive function. Performance on cognitive tasks varies between individuals and is highly heritable 1 and polygenic 2,3 . However, to date, progress in identifying molecular genetic contributions to healthy human cognitive abilities has been limited 4,5 .A distinction can be made between cognitive domains such as the ability to apply acquired knowledge and learned skills (so called crystallized abilities) and fluid cognitive abilities such as the capacity to establish new memories, reason in novel situations or perform cognitive tasks accurately and quickly 6 . Notably, within individuals, performance on different measures of cognitive ability tend to be positively correlated such that people who do well in one domain, such as memory, tend to do well in other domains 7 . Seemingly disparate domains of cognitive ability also show high levels of genetic correlation in twin studies, typically in excess of 0.6 8 , and analyses using genome-wide similarity between unrelated individuals 3 (genome-wide complex trait analysis, GCTA) has also demonstrated substantial genetic correlation between diverse cognitive and learning abilities 9,10 . These studies suggest genes that influence human cognition may exert pleiotropic effects across diverse cognitive domains, such that genes regulating one cognitive ability might influence other cognitive abilities.Since impairment of cognitive function is a core clinical feature of many neurodevelopmental diseases including schizophrenia 11 , autism 12 , epilepsy 13 and intellectual disability (by definition), we sought to investigate gene-regulatory networks for human cognition and to determine their relationship to neurodevelopmental disease. An overview of our experimental design is provided in Supplementary Fig. 1. RESULTS Gene co-expression network a...

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Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

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“…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.…”

Section: Snp-based Heritability and Genetic Correlationsmentioning

confidence: 99%

“…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.…”

Section: Snp-based Heritability and Genetic Correlationsmentioning

confidence: 99%

“…[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. 34 In addition to a relatively poor understanding of the molecular genetic underpinnings of TMT, its cognitive and psychometric architecture merits further research.…”

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Genetic contributions to Trail Making Test performance in UK Biobank

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The Trail Making Test (TMT) is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the TMT and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The single-nucleotide polymorphism-based heritability estimates for trail-making measures were 7.9% (part A), 22.4% (part B) and 17.6% (part B − part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (r g 40.6), general cognitive function (r g 40.6), processing speed (r g 40.7) and memory (r g 40.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed and memory (standardized β between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed.Molecular Psychiatry advance online publication, 19 September 2017; doi:10.1038/mp.2017.189 INTRODUCTIONThe Trail Making Test (TMT) is widely used in both research and clinical settings as a test of some aspects of executive function. [1][2][3] The TMT is usually given as two parts, from which three measures are derived. In TMT Part A (TMT A), participants are required to connect an array of numbers in ascending order, by drawing a continuous line (trail) between them as quickly and accurately as possible. TMT Part B (TMT B) requires participants to connect an array of both numbers and letters in alternating ascending order (1, A, 2, B, 3, C and so on) with the same emphasis on speed and accuracy (Supplementary Figure 1). Subtracting TMT A completion time from that of TMT B (TMT B − A) is thought to allow the relative contributions of visual search and psychomotor speed to be parsed from the more complex executive functions (such as cognitive flexibility) required to alternate between numbers and letters. [4][5][6][7][8] TMT performance has been ascribed to a number of cognitive processes, 'including attention, visual search and scanning, sequencing and shifting, psychomotor speed, abstraction, flexibility, ability to execute and modify a plan of action, and ability to maintain two trains of thought simultaneously '. 9 It is considered a useful tool in research and clinical practice due to the sensitivity of the task (particularly TMT B and B − A) to frontal lobe damage (in some, but not other studies 10 ) and dementia. [11][12][13] There are declines in both TMT A and B performance in ageing. 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...

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Copy Number Variations and Cognitive Phenotypes in Unselected Populations

Männik

Mägi

Macé

et al. 2015

JAMA

148

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The association of copy number variations (CNVs), differing numbers of copies of genetic sequence at locations in the genome, with phenotypes such as intellectual disability has been almost exclusively evaluated using clinically ascertained cohorts. The contribution of these genetic variants to cognitive phenotypes in the general population remains unclear.OBJECTIVE To investigate the clinical features conferred by CNVs associated with known syndromes in adult carriers without clinical preselection and to assess the genome-wide consequences of rare CNVs (frequency Յ0.05%; size Ն250 kilobase pairs [kb]) on carriers' educational attainment and intellectual disability prevalence in the general population. DESIGN, SETTING, AND PARTICIPANTSThe population biobank of Estonia contains 52 000 participants enrolled from 2002 through 2010. General practitioners examined participants and filled out a questionnaire of health-and lifestyle-related questions, as well as reported diagnoses. Copy number variant analysis was conducted on a random sample of 7877 individuals and genotype-phenotype associations with education and disease traits were evaluated. Our results were replicated on a high-functioning group of 993 Estonians and 3 geographically distinct populations in the United Kingdom, the United States, and Italy. MAIN OUTCOMES AND MEASURESPhenotypes of genomic disorders in the general population, prevalence of autosomal CNVs, and association of these variants with educational attainment (from less than primary school through scientific degree) and prevalence of intellectual disability. RESULTSOf the 7877 in the Estonian cohort, we identified 56 carriers of CNVs associated with known syndromes. Their phenotypes, including cognitive and psychiatric problems, epilepsy, neuropathies, obesity, and congenital malformations are similar to those described for carriers of identical rearrangements ascertained in clinical cohorts. A genome-wide evaluation of rare autosomal CNVs (frequency, Յ0.05%; Ն250 kb) identified 831 carriers (10.5%) of the screened general population. Eleven of 216 (5.1%) carriers of a deletion of at least 250 kb (odds ratio [OR], 3.16; 95% CI, 1.51-5.98; P = 1.5e-03) and 6 of 102 (5.9%) carriers of a duplication of at least 1 Mb (OR, 3.67; 95% CI, 1.29-8.54; P = .008) had an intellectual disability compared with 114 of 6819 (1.7%) in the Estonian cohort. The mean education attainment was 3.81 (P = 1.06e-04) among 248 (Ն250 kb) deletion carriers and 3.69 (P = 5.024e-05) among 115 duplication carriers (Ն1 Mb). Of the deletion carriers, 33.5% did not graduate from high school (OR, 1.48; 95% CI, 1.12-1.95; P = .005) and 39.1% of duplication carriers did not graduate high school (OR, 1.89; 95% CI, 1.27-2.8; P = 1.6e-03). Evidence for an association between rare CNVs and lower educational attainment was supported by analyses of cohorts of adults from Italy and the United States and adolescents from the United Kingdom.CONCLUSIONS AND RELEVANCE Known pathogenic CNVs in unselected, but assumed to be healthy, adult popu...

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Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53 949)

Cited by 350 publications

References 81 publications

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Genetic contributions to Trail Making Test performance in UK Biobank

Copy Number Variations and Cognitive Phenotypes in Unselected Populations

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