Molecular genetic contributions to socioeconomic status and intelligence

Education, socioeconomic status, and intelligence are commonly used as predictors of health outcomes, social environment, and mortality. Education and socioeconomic status are typically viewed as environmental variables although both correlate with intelligence, which has a substantial genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). DNA-derived heritability estimates and genetic correlations were calculated using the ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status

Data integration in eHealth: a domain/disease specific roadmap

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain

Evolutionary conserved longevity genes and human cognitive abilities in elderly cohorts

Genetic influences have an important role in the ageing process. The genetic factors that influence success in bodily ageing may also contribute to the successful ageing of cognitive abilities. A comparative genomics approach found longevity genes conserved between yeast Saccharomyces cerevisiae and nematode Caenorhabditis elegans. We hypothesised that these longevity genes influence variance in cognitive ability and age-related cognitive decline in humans. Here, we investigated six of these genes that have human orthologs and show expression in the brain. We tested AFG3L2 (MIM: 604581, AFG3 ATPase family gene 3-like 2 (yeast)), FRAP1 (MIM: 601231, a FK506 binding protein 12-rapamycin associated protein), MAT1A, MAT2A (MIM: 610550 and 601468, methionine adenosyltransferases I alpha and II alpha, respectively), SYNJ1 and SYNJ2 (MIM: 604297 and 609410, synaptojanin-1 and synaptojanin-2, respectively) in approximately 1000 healthy older Scots: the Lothian Birth Cohort 1936 (LBC1936). They were tested on general cognitive ability at age 11 years. At a mean age of 70 years, they re-sat the same general cognitive ability test and underwent an additional battery of diverse cognitive tests. In all, 70 tag and functional SNPs in the six longevity genes were genotyped and tested for association with cognition and cognitive ageing in LBC1936. Suggestive associations were detected between SNPs in SYNJ2, MAT1A, AFG3L2 and SYNJ1 and a general memory factor and general cognitive ability at age 11 and 70 years. Replication studies for cognitive ability associations were performed in 2506 samples from the Cognitive Ageing Genetics in England and Scotland consortium. A meta-analysis replicated the SYNJ2 association with cognitive abilities (lowest P=0.00077). SYNJ2 is a novel gene in which variation is potentially associated with cognitive abilities

Brain cortical characteristics of lifetime cognitive ageing

Regional cortical brain volume is the product of surface area and thickness. These measures exhibit partially distinct trajectories of change across the brain’s cortex in older age, but it is unclear which cortical characteristics at which loci are sensitive to cognitive ageing differences. We examine associations between change in intelligence from age 11 to 73 years and regional cortical volume, surface area, and thickness measured at age 73 years in 568 community-dwelling older adults, all born in 1936. A relative positive change in intelligence from 11 to 73 was associated with larger volume and surface area in selective frontal, temporal, parietal, and occipital regions (r < 0.180, FDR-corrected q < 0.05). There were no significant associations between cognitive ageing and a thinner cortex for any region. Interestingly, thickness and surface area were phenotypically independent across bilateral lateral temporal loci, whose surface area was significantly related to change in intelligence. These findings suggest that associations between regional cortical volume and cognitive ageing differences are predominantly driven by surface area rather than thickness among healthy older adults. Regional brain surface area has been relatively underexplored, and is a potentially informative biomarker for identifying determinants of cognitive ageing differences

Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations

We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction

ARTICLE Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations

We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n~350,000) and height (n~250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n ¼ 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n ¼ 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n ¼ 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction