Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

/pdf/an-atlas-of-genetic-correlations-across-human-diseases-and-11fyqwnh4c.pdf

An atlas of genetic correlations across human diseases and traits.

Fluid intelligence (Gf) refers to the ability to reason and to solve new problems independently of previously acquired knowledge. Gf is critical for a wide variety of cognitive tasks, and it is considered one of the most important factors in learning. Moreover, Gf is closely related to professional and educational success, especially in complex and demanding environments. Although performance on tests of Gf can be improved through direct practice on the tests themselves, there is no evidence that training on any other regimen yields increased Gf in adults. Furthermore, there is a long history of research into cognitive training showing that, although performance on trained tasks can increase dramatically, transfer of this learning to other tasks remains poor. Here, we present evidence for transfer from training on a demanding working memory task to measures of Gf. This transfer results even though the trained task is entirely different from the intelligence test itself. Furthermore, we demonstrate that the extent of gain in intelligence critically depends on the amount of training: the more training, the more improvement in Gf. That is, the training effect is dosage-dependent. Thus, in contrast to many previous studies, we conclude that it is possible to improve Gf without practicing the testing tasks themselves, opening a wide range of applications.

/pdf/improving-fluid-intelligence-with-training-on-working-memory-1lofpgaegw.pdf

Improving fluid intelligence with training on working memory

Genome-wide association study identifies 74 loci associated with educational attainment

Neuroscience is contributing to an understanding of the biological bases of human intelligence differences. This work is principally being conducted along two empirical fronts: genetics — quantitative and molecular — and brain imaging. Quantitative genetic studies have established that there are additive genetic contributions to different aspects of cognitive ability — especially general intelligence — and how they change through the lifespan. Molecular genetic studies have yet to identify reliably reproducible contributions from individual genes. Structural and functional brain-imaging studies have identified differences in brain pathways, especially parieto-frontal pathways, that contribute to intelligence differences. There is also evidence that brain efficiency correlates positively with intelligence.

/pdf/the-neuroscience-of-human-intelligence-differences-2mdlx5it3p.pdf

The Neuroscience of Human Intelligence Differences

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

/pdf/genome-wide-association-meta-analysis-in-269-867-individuals-4f6xo5800s.pdf

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

https://emilkirkegaard.dk/en/wp-content/uploads/Intelligence-and-educational-achievement.pdf

Intelligence and educational achievement

Sex, intelligence and educational achievement in a national cohort of over 175,000 11-year-old schoolchildren in England.

The genetic influence on the association between contemporaneously measured intelligence and academic achievement in childhood was examined in nationally representative cohorts from England and The Netherlands using a whole population indirect twin design, including singleton data. We identified 1,056 same-sex (SS) and 495 opposite-sex (OS) twin pairs among 174,098 British 11 year-olds with test scores from 2004, and, 785 SS and 327 OS twin pairs among 120,995 Dutch schoolchildren, aged 8, 10 or 12 years, with assessments from 1994 to 2002. The estimate of intelligence heritability was large in both cohorts, consistent with previous studies (h (2) = 0.70 ± 0.14, England; h (2) = 0.43 ± 0.28-0.67 ± 0.31, The Netherlands), as was the heritability of academic achievement variables (h (2) = 0.51 ± 0.16-0.81 ± 0.16, England; h (2) = 0.36 ± 0.27-0.74 ± 0.27, The Netherlands). Additive genetic covariance explained the large majority of the phenotypic correlations between intelligence and academic achievement scores in England, when standardised to a bivariate heritability (Biv h (2) = 0.76 ± 0.15-0.88 ± 0.16), and less consistent but often large proportions of the phenotypic correlations in The Netherlands (Biv h (2) = 0.33 ± 0.52-1.00 ± 0.43). In the British cohort both nonverbal and verbal reasoning showed very high additive genetic covariance with achievement scores (Biv h (2) = 0.94-0.98; Biv h (2) = 0.77-1.00 respectively). In The Netherlands, covariance estimates were consistent across age groups. The heritability of intelligence-academic achievement associations in two population cohorts of elementary schoolchildren, using a twin pair extraction method, is at the high end of estimates reported by studies of largely preselected twin samples.

/pdf/multivariate-genetic-analyses-of-cognition-and-academic-10nxsnjjj5.pdf

Cres Fernandes

Papers

Intelligence and educational achievement

Sex, intelligence and educational achievement in a national cohort of over 175,000 11-year-old schoolchildren in England.

Multivariate Genetic Analyses of Cognition and Academic Achievement from Two Population Samples of 174,000 and 166,000 School Children

Is there still a cognitive cost of being a twin in the UK