Identification of novel loci associated with infant cognitive ability.

It is believed that genetic factors play a large role in the development of many cognitive and neurological processes; however, epidemiological evidence for the genetic basis of childhood neurodevelopment is very limited. Identification of the genetic polymorphisms associated with early-stage neurodevelopment will help elucidate biological mechanisms involved in neuro-behavior and provide a better understanding of the developing brain. To search for such variants, we performed a genome-wide association study (GWAS) for infant mental and motor ability at two years of age with mothers and children recruited from cohorts in Bangladesh and Mexico. Infant ability was assessed using mental and motor composite scores calculated with country-specific versions of the Bayley Scales of Infant Development. A missense variant (rs1055153) located in the gene WWTR1 reached genome-wide significance in association with mental composite score (meta-analysis effect size of minor allele ßmeta = -6.04; 95% CI: -8.13 to -3.94; P = 1.56×10-8). Infants carrying the minor allele reported substantially lower cognitive scores in both cohorts, and this variant is predicted to be in the top 0.3% of most deleterious substitutions in the human genome. Fine mapping and region-based association testing provided additional suggestive evidence that both WWTR1 and a second gene, LRP1B, were associated with infant cognitive ability. Comparisons with recently conducted GWAS in intelligence and educational attainment indicate that our phenotypes do not possess a high genetic correlation with either adolescent or adult cognitive traits, suggesting that infant neurological assessments should be treated as an independent outcome of interest. Additional functional studies and replication efforts in other cohorts may help uncover new biological pathways and genetic architectures that are crucial to the developing brain.

Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment.

BACKGROUND: Neurodevelopment is a complex process involving both genetic and environmental factors. Prenatal exposure to lead (Pb) has been associated with lower performance on neurodevelopmental tests. Adverse neurodevelopmental outcomes are more frequent and/or more severe when toxic exposures interact with genetic susceptibility. METHODS: To explore possible loci associated with increased susceptibility to prenatal Pb exposure, we performed a genome-wide gene-environment interaction study (GWIS) in young children from Mexico (n = 390) and Bangladesh (n = 497). Prenatal Pb exposure was estimated by cord blood Pb concentration. Neurodevelopment was assessed using the Bayley Scales of Infant Development. RESULTS: We identified a locus on chromosome 8, containing UNC5D, and demonstrated evidence of its genome-wide significance with mental composite scores (rs9642758, p meta = 4.35 × 10-6). Within this locus, the joint effects of two independent single nucleotide polymorphisms (SNPs, rs9642758 and rs10503970) had a p-value of 4.38 × 10-9 for mental composite scores. Correlating GWIS results with in vitro transcriptomic profiles identified one common gene, SLC1A5, which is involved in synaptic function, neuronal development, and excitotoxicity. Further analysis revealed interconnected interactions that formed a large network of 52 genes enriched with oxidative stress genes and neurodevelopmental genes. CONCLUSIONS: Our findings suggest that certain genetic polymorphisms within/near genes relevant to neurodevelopment might modify the toxic effects of Pb exposure via oxidative stress.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs.