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Obsessive-compulsive disorder (OCD) is a chronic, disabling condition with inadequate treatment options that leave most patients with substantial residual symptoms. Structural, neurochemical, and behavioral findings point to a significant role for basal ganglia circuits and for the glutamate system in OCD. Genetic linkage and association studies in OCD point to SLC1A1 , which encodes the neuronal glutamate/aspartate/cysteine transporter excitatory amino acid transporter 3 (EAAT3)/excitatory amino acid transporter 1 (EAAC1). However, no previous studies have investigated EAAT3 in basal ganglia circuits or in relation to OCD-related behavior. Here, we report a model of Slc1a1 loss based on an excisable STOP cassette that yields successful ablation of EAAT3 expression and function. Using amphetamine as a probe, we found that EAAT3 loss prevents expected increases in ( i ) locomotor activity, ( ii ) stereotypy, and ( iii ) immediate early gene induction in the dorsal striatum following amphetamine administration. Further, Slc1a1 -STOP mice showed diminished grooming in an SKF-38393 challenge experiment, a pharmacologic model of OCD-like grooming behavior. This reduced grooming is accompanied by reduced dopamine D 1 receptor binding in the dorsal striatum of Slc1a1 -STOP mice. Slc1a1 -STOP mice also exhibit reduced extracellular dopamine concentrations in the dorsal striatum both at baseline and following amphetamine challenge. Viral-mediated restoration of Slc1a1 /EAAT3 expression in the midbrain but not in the striatum results in partial rescue of amphetamine-induced locomotion and stereotypy in Slc1a1 -STOP mice, consistent with an impact of EAAT3 loss on presynaptic dopaminergic function. Collectively, these findings indicate that the most consistently associated OCD candidate gene impacts basal ganglia-dependent repetitive behaviors., Competing Interests: Conflict of interest statement: C.K. has received research funding from Forest. C.K.J. has received research funding from AstraZeneca, Johnson & Johnson, Bristol-Myers Squibb, and Seaside Therapeutics. J.V. has consulted or served on advisory boards for Roche, Novartis, and SynapDx and has received research funding from Roche, Novartis, SynapDx, Seaside Therapeutics, and Forest.

Genetic variants affecting hematopoiesis can influence commonly measured blood cell traits. To identify factors that affect hematopoiesis, we performed association studies for blood cell traits in the population-based Estonian Biobank using high-coverage whole-genome sequencing (WGS) in 2,284 samples and SNP genotyping in an additional 14,904 samples. Using up to 7,134 samples with available phenotype data, our analyses identified 17 associations across 14 blood cell traits. Integration of WGS-based fine-mapping and complementary epigenomic datasets provided evidence for causal mechanisms at several loci, including at a previously undiscovered basophil count-associated locus near the master hematopoietic transcription factor CEBPA The fine-mapped variant at this basophil count association near CEBPA overlapped an enhancer active in common myeloid progenitors and influenced its activity. In situ perturbation of this enhancer by CRISPR/Cas9 mutagenesis in hematopoietic stem and progenitor cells demonstrated that it is necessary for and specifically regulates CEBPA expression during basophil differentiation. We additionally identified basophil count-associated variation at another more pleiotropic myeloid enhancer near GATA2, highlighting regulatory mechanisms for ordered expression of master hematopoietic regulators during lineage specification. Our study illustrates how population-based genetic studies can provide key insights into poorly understood cell differentiation processes of considerable physiologic relevance., Competing Interests: The authors declare no conflict of interest.

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer's disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques., Competing Interests: B.F. has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. His interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

A substantial body of research has explored the relative roles of genetic and environmental factors on phenotype expression in humans. Recent research has also sought to identify gene-environment (or g-by-e) interactions, with mixed success. One potential reason for these mixed results may relate to the fact that genetic effects might be modified by changes in the environment over time. For example, the noted rise of obesity in the United States in the latter part of the 20th century might reflect an interaction between genetic variation and changing environmental conditions that together affect the penetrance of genetic influences. To evaluate this hypothesis, we use longitudinal data from the Framingham Heart Study collected over 30 y from a geographically relatively localized sample to test whether the well-documented association between the rs993609 variant of the FTO (fat mass and obesity associated) gene and body mass index (BMI) varies across birth cohorts, time period, and the lifecycle. Such cohort and period effects integrate many potential environmental factors, and this gene-by-environment analysis examines interactions with both time-varying contemporaneous and historical environmental influences. Using constrained linear age-period-cohort models that include family controls, we find that there is a robust relationship between birth cohort and the genotype-phenotype correlation between the FTO risk allele and BMI, with an observed inflection point for those born after 1942. These results suggest genetic influences on complex traits like obesity can vary over time, presumably because of global environmental changes that modify allelic penetrance.

Inactivating mutations in chromodomain helicase DNA binding protein 7 (CHD7) cause CHARGE syndrome, a severe multiorgan system disorder of which Isolated gonadotropin-releasing hormone (GnRH) deficiency (IGD) is a minor feature. Recent reports have described predominantly missense CHD7 alleles in IGD patients, but it is unclear if these alleles are relevant to causality or overall genetic burden of Kallmann syndrome (KS) and normosmic form of IGD. To address this question, we sequenced CHD7 in 783 well-phenotyped IGD patients lacking full CHARGE features; we identified nonsynonymous rare sequence variants in 5.2% of the IGD cohort (73% missense and 27% splice variants). Functional analyses in zebrafish using a surrogate otolith assay of a representative set of these CHD7 alleles showed that rare sequence variants observed in controls showed no altered function. In contrast, 75% of the IGD-associated alleles were deleterious and resulted in both KS and normosmic IGD. In two families, pathogenic mutations in CHD7 coexisted with mutations in other known IGD genes. Taken together, our data suggest that rare deleterious CHD7 alleles contribute to the mutational burden of patients with both KS and normosmic forms of IGD in the absence of full CHARGE syndrome. These findings (i) implicate a unique role or preferential sensitivity for CHD7 in the ontogeny of GnRH neurons, (ii) reiterate the emerging genetic complexity of this family of IGD disorders, and (iii) demonstrate how the coordinated use of well-phenotyped cohorts, families, and functional studies can inform genetic architecture and provide insights into the developmental biology of cellular systems.

Truncating mutations of chromodomain helicase DNA-binding protein 8 (CHD8), and of many other genes with diverse functions, are strong-effect risk factors for autism spectrum disorder (ASD), suggesting multiple mechanisms of pathogenesis. We explored the transcriptional networks that CHD8 regulates in neural progenitor cells (NPCs) by reducing its expression and then integrating transcriptome sequencing (RNA sequencing) with genome-wide CHD8 binding (ChIP sequencing). Suppressing CHD8 to levels comparable with the loss of a single allele caused altered expression of 1,756 genes, 64.9% of which were up-regulated. CHD8 showed widespread binding to chromatin, with 7,324 replicated sites that marked 5,658 genes. Integration of these data suggests that a limited array of direct regulatory effects of CHD8 produced a much larger network of secondary expression changes. Genes indirectly down-regulated (i.e., without CHD8-binding sites) reflect pathways involved in brain development, including synapse formation, neuron differentiation, cell adhesion, and axon guidance, whereas CHD8-bound genes are strongly associated with chromatin modification and transcriptional regulation. Genes associated with ASD were strongly enriched among indirectly down-regulated loci (P < 10(-8)) and CHD8-bound genes (P = 0.0043), which align with previously identified coexpression modules during fetal development. We also find an intriguing enrichment of cancer-related gene sets among CHD8-bound genes (P < 10(-10)). In vivo suppression of chd8 in zebrafish produced macrocephaly comparable to that of humans with inactivating mutations. These data indicate that heterozygous disruption of CHD8 precipitates a network of gene-expression changes involved in neurodevelopmental pathways in which many ASD-associated genes may converge on shared mechanisms of pathogenesis.

Advanced age-related macular degeneration (AMD) is the leading cause of late onset blindness. We present results of a genome-wide association study of 979 advanced AMD cases and 1,709 controls using the Affymetrix 6.0 platform with replication in seven additional cohorts (totaling 5,789 unrelated cases and 4,234 unrelated controls). We also present a comprehensive analysis of copy-number variations and polymorphisms for AMD. Our discovery data implicated the association between AMD and a variant in the hepatic lipase gene (LIPC) in the high-density lipoprotein cholesterol (HDL) pathway (discovery P = 4.53e-05 for rs493258). Our LIPC association was strongest for a functional promoter variant, rs10468017, (P = 1.34e-08), that influences LIPC expression and serum HDL levels with a protective effect of the minor T allele (HDL increasing) for advanced wet and dry AMD. The association we found with LIPC was corroborated by the Michigan/Penn/Mayo genome-wide association study; the locus near the tissue inhibitor of metalloproteinase 3 was corroborated by our replication cohort for rs9621532 with P = 3.71e-09. We observed weaker associations with other HDL loci (ABCA1, P = 9.73e-04; cholesterylester transfer protein, P = 1.41e-03; FADS1-3, P = 2.69e-02). Based on a lack of consistent association between HDL increasing alleles and AMD risk, the LIPC association may not be the result of an effect on HDL levels, but it could represent a pleiotropic effect of the same functional component. Results implicate different biologic pathways than previously reported and provide new avenues for prevention and treatment of AMD.

Mutations in either the mitochondrial or nuclear genomes can give rise to respiratory chain disease (RCD), a large class of devastating metabolic disorders. Their clinical management is challenging, in part because we lack facile and accurate biomarkers to aid in diagnosis and in the monitoring of disease progression. Here we introduce a sequential strategy that combines biochemical analysis of spent media from cell culture with analysis of patient plasma to identify disease biomarkers. First, we applied global metabolic profiling to spotlight 32 metabolites whose uptake or secretion kinetics were altered by chemical inhibition of the respiratory chain in cultured muscle . These metabolites span a wide range of pathways and include lactate and alanine, which are used clinically as biomarkers of RCD. We next measured the cell culture-defined metabolites in human plasma to discover that creatine is reproducibly elevated in two independent cohorts of RCD patients, exceeding lactate and alanine in magnitude of elevation and statistical significance. In cell culture extracellular creatine was inversely related to the intracellular phosphocreatine:creatine ratio suggesting that the elevation of plasma creatine in RCD patients signals a low energetic state of tissues using the phosphocreatine shuttle. Our study identifies plasma creatine as a potential biomarker of human mitochondrial dysfunction that could be clinically useful. More generally, we illustrate how spent media from cellular models of disease may provide a window into the biochemical derangements in human plasma, an approach that could, in principle, be extended to a range of complex diseases.