A genome-wide association study for extremely high intelligence.

We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.

Multi-polygenic score approach to trait prediction.

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Phenome-wide analysis of genome-wide polygenic scores.

Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents' behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development.

Heritability and genetic correlations of insulin resistance and component phenotypes in Asian Indian families using a multivariate analysis.

AIMS/HYPOTHESIS: Insulin resistance and related metabolic disturbances are more common among Asian Indians than European whites. Little is known about the heritability of insulin resistance traits in Asian Indians. Our objective was to estimate heritabilities and genetic correlations in Asian Indian families. METHODS: Phenotypic data were assembled for 181 UK Asian Indian probands with premature CHD, and their 1,454 first-, second- and third-degree relatives. We calculated (narrow-sense) heritabilities and genetic correlations for insulin resistance traits, and common environmental effects using all study participants and a multivariate model. The analysis was repeated in a subsample consisting of individuals not on drug therapy. RESULTS: Heritability estimates (SE) for individuals not on drug therapy were: BMI 0.31 (0.04), WHR 0.27 (0.04), systolic BP 0.29 (0.03), triacylglycerol 0.40 (0.04), HDL-cholesterol 0.53 (0.04), glucose 0.37 (0.03), HOMA of insulin resistance (HOMA-IR) 0.22 (0.04), and HbA(1c) 0.60 (0.04). We observed many significant genetic correlations between the traits, in particular between HOMA-IR and BMI. Heritability estimates were lower for all phenotypes when analysed among all participants. CONCLUSIONS/INTERPRETATION: Genetic factors contribute to a significant proportion of the total variance in insulin resistance and related metabolic disturbances in Asian Indian CHD families.

A genome-wide screen in 1119 relative pairs with autoimmune thyroid disease.

CONTEXT: Autoimmune thyroid diseases (AITD), comprising Graves' disease and autoimmune hypothyroidism, are characterized by loss of immunological self-tolerance to thyroid antigens. These are complex diseases arising from a combination of genetic and environmental factors. An understanding of the genetic susceptibility factors for AITD could help to target treatments more effectively and identify people at risk for these conditions. OBJECTIVE: The objective of this study was to identify regions of genetic linkage to AITD that could potentially harbor genetic susceptibility factors for these conditions. DESIGN: The study design was a genome-wide screen performed on affected relative pairs with AITD. SETTING: Patients were recruited through hospital endocrinology clinics. PARTICIPANTS: Some 1119 Caucasian relative pairs affected with AITD (Graves' disease or autoimmune hypothyroidism) were recruited into the study. INTERVENTION: Blood samples were obtained from each participant for DNA analysis, and clinical questionnaires were completed. MAIN OUTCOME MEASURE: The study aimed to identify regions of genetic linkage to AITD. RESULTS: Three regions of suggestive linkage were obtained on chromosomes 18p11 (maximum LOD score, 2.5), 2q36 (maximum LOD score, 2.2), and 11p15 (maximum LOD score, 2.0). No linkage to human leukocyte antigen was found. CONCLUSIONS: The absence of significant evidence of linkage at any one locus in such a large dataset argues that genetic susceptibility to AITD reflects a number of loci, each with a modest effect. Linkage analysis may be limited in defining such loci, and large-scale association studies may prove to be more useful in identifying genetic susceptibility factors for AITD.