Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).

12The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P ≤ 5 x 10 -8 ); 199 survived multiple testing correction (P ≤ 8.3 x 10 -10 ; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression and ADHD.One Sentence Summary: Common genetic variation is associated with inter-individual variation in the structure of the human cortex, both globally and within specific regions, and is shared with genetic risk factors for some neuropsychiatric disorders.The human cerebral cortex is the outer grey matter layer of the brain, which is implicated in multiple aspects of higher cognitive function. Its distinct folding pattern is characterised by convex (gyral) and concave (sulcal) regions. Computational brain mapping approaches use the consistent folding patterns across individual cortices to label brain regions(1). During fetal development excitatory neurons, the predominant neuronal cell-type in the cortex, are generated from neural progenitor cells in the developing germinal zone(2). The radial unit hypothesis(3) posits that the expansion of cortical surface area (SA) is driven by the proliferation of these neural progenitor cells, whereas thickness (TH) is determined by the number of neurogenic divisions. Variation in global and regional measures of cortical SA and TH are associated with neuropsychiatric disorders and psychological traits(4) ( Table S1). Twin and family-based brain imaging studies show that SA and TH measurements are highly heritable and are largely influenced by independent genetic factors(5). Despite extensive studies of genes impacting cortical structure in model organisms (6), our current understanding of genetic variation impacting human cortical size and patterning is limited to rare, highly penetrant variants (7,8). These variants often disrupt cortical development, leading to altered post-natal structure. However, little is known about how common genetic variants impact human cortical SA and TH.To address this, we conducted genome-wide association meta-analyses of cortical SA and TH measures in 51,662 individuals from 60 cohorts from around the world (Tables S2-S4). Cortical measures were extracted from structural brain MRI scan...

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Oxamniquine resistance evolved in the human blood fluke (Schistosoma mansoni) in Brazil in the 1970s. We crossed parental parasites differing ~500-fold in drug response, determined drug sensitivity and marker segregation in clonally-derived F2s, and identified a single QTL (LOD=31) on chromosome 6. A sulfotransferase was identified as the causative gene using RNAi knockdown and biochemical complementation assays and we subsequently demonstrated independent origins of loss-of-function mutations in field-derived and laboratory-selected resistant parasites. These results demonstrate the utility of linkage mapping in a human helminth parasite, while crystallographic analyses of protein-drug interactions illuminate the mode of drug action and provide a framework for rational design of oxamniquine derivatives that kill both S. mansoni and S. haematobium, the two species responsible for >99% of schistosomiasis cases worldwide.

Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals -138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans.

Genetic Analysis Workshop 18 (GAW18) focused on identification of genes and functional variants that influence complex phenotypes in human sequence data. Data for the workshop were donated by the T2D-GENES Consortium and included whole genome sequences for odd-numbered autosomes in 464 key individuals selected from 20 Mexican American families, a dense set of single-nucleotide polymorphisms in 959 individuals in these families, and longitudinal data on systolic and diastolic blood pressure measured at 1-4 examinations over a period of 20 years. Simulated phenotypes were generated based on the real sequence data and pedigree structures. In the design of the simulation model, gene expression measures from the San Antonio Family Heart Study (not distributed as part of the GAW18 data) were used to identify genes whose mRNA levels were correlated with blood pressure. Observed variants within these genes were designated as functional in the GAW18 simulation if they were nonsynonymous and predicted to have deleterious effects on protein function or if they were noncoding and associated with mRNA levels. Two simulated longitudinal phenotypes were modeled to have the same trait distributions as the real systolic and diastolic blood pressure data, with effects of age, sex, and medication use, including a genotype-medication interaction. For each phenotype, more than 1000 sequence variants in more than 200 genes present on the odd-numbered autosomes individually explained less than 0.01-2.78% of phenotypic variance. Cumulatively, variants in the most influential gene explained 7.79% of trait variance. An additional simulated phenotype, Q1, was designed to be correlated among family members but to not be associated with any sequence variants. Two hundred replicates of the phenotypes were simulated, with each including data for 849 individuals.

Statistical genetic analysis of quantitative traits in large pedigrees is a formidable computational task due to the necessity of taking the non-independence among relatives into account. With the growing awareness that rare sequence variants may be important in human quantitative variation, heritability and association study designs involving large pedigrees will increase in frequency due to the greater chance of observing multiple copies of rare variants amongst related individuals. Therefore, it is important to have statistical genetic test procedures that utilize all available information for extracting evidence regarding genetic association. Optimal testing for marker/phenotype association involves the exact calculation of the likelihood ratio statistic which requires the repeated inversion of potentially large matrices. In a whole genome sequence association context, such computation may be prohibitive. Toward this end, we have developed a rapid and efficient eigensimplification of the likelihood that makes analysis of family data commensurate with the analysis of a comparable sample of unrelated individuals. Our theoretical results which are based on a spectral representation of the likelihood yield simple exact expressions for the expected likelihood ratio test statistic (ELRT) for pedigrees of arbitrary size and complexity. For heritability, the ELRT is: −∑lntrue[1+ĥ2false(λgi−1false)true], where ĥ2 and λgi are respectively the heritability and eigenvalues of the pedigree-derived genetic relationship kernel (GRK). For association analysis of sequence variants, the ELRT is given by ELRTtrue[hq2>0:unrelatedstrue]−true(ELRTtrue[ht2>0:pedigreestrue]−ELRTtrue[hr2>0:pedigreestrue]true), where ht2,hq2, and hr2 are the total, quantitative trait nucleotide, and residual heritabilities, respectively. Using these results, fast and accurate analytical power analyses are possible, eliminating the need for computer simulation. Additional benefits of eigensimplification include a simple method for calculation of the exact distribution of the ELRT under the null hypothesis which turns out to differ from that expected under the usual asymptotic theory. Further, when combined with the use of empirical GRKs—estimated over a large number of genetic markers— our theory reveals potential problems associated with non positive semi-definite kernels. These procedures are being added to our general statistical genetic computer package, SOLAR.

Progranulin (GRN) loss-of-function mutations leading to progranulin protein (PGRN) haploinsufficiency are prevalent genetic causes of frontotemporal dementia. Reports also indicated PGRN-mediated neuroprotection in models of Alzheimer's and Parkinson's disease; thus, increasing PGRN levels is a promising therapeutic for multiple disorders. To uncover novel PGRN regulators, we linked whole-genome sequence data from 920 individuals with plasma PGRN levels and identified the prosaposin (PSAP) locus as a new locus significantly associated with plasma PGRN levels. Here we show that both PSAP reduction and overexpression lead to significantly elevated extracellular PGRN levels. Intriguingly, PSAP knockdown increases PGRN monomers, whereas PSAP overexpression increases PGRN oligomers, partly through a protein–protein interaction. PSAP-induced changes in PGRN levels and oligomerization replicate in human-derived fibroblasts obtained from a GRN mutation carrier, further supporting PSAP as a potential PGRN-related therapeutic target. Future studies should focus on addressing the relevance and cellular mechanism by which PGRN oligomeric species provide neuroprotection.