Chiara Sabatti

Large recurrent microdeletions associated with schizophrenia.

Reduced fecundity, associated with severe mental disorders, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism, schizophrenia and mental retardation. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation and autism. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.

Variance component model to account for sample structure in genome-wide association studies

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts

Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797–22,562 persons, aged 18–104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 × 10−8), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 × 10−11; LDL, P = 2.6 × 10−10), TMEM57 (TC, P = 5.4 × 10−10), CTCF-PRMT8 region (HDL, P = 8.3 × 10−16), DNAH11 (LDL, P = 6.1 × 10−9), FADS3-FADS2 (TC, P = 1.5 × 10−10; LDL, P = 4.4 × 10−13) and MADD-FOLH1 region (HDL, P = 6 × 10−11). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.

Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene–environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.

Network component analysis: Reconstruction of regulatory signals in biological systems

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

Disruption of the neurexin 1 gene is associated with schizophrenia

Deletions within the neurexin 1 gene (NRXN1; 2p16.3) are associated with autism and have also been reported in two families with schizophrenia. We examined NRXN1, and the closely related NRXN2 and NRXN3 genes, for copy number variants (CNVs) in 2977 schizophrenia patients and 33 746 controls from seven European populations (Iceland, Finland, Norway, Germany, The Netherlands, Italy and UK) using microarray data. We found 66 deletions and 5 duplications in NRXN1, including a de novo deletion: 12 deletions and 2 duplications occurred in schizophrenia cases (0.47%) compared to 49 and 3 (0.15%) in controls. There was no common breakpoint and the CNVs varied from 18 to 420 kb. No CNVs were found in NRXN2 or NRXN3. We performed a Cochran-Mantel-Haenszel exact test to estimate association between all CNVs and schizophrenia (P = 0.13; OR = 1.73; 95% CI 0.81-3.50). Because the penetrance of NRXN1 CNVs may vary according to the level of functional impact on the gene, we next restricted the association analysis to CNVs that disrupt exons (0.24% of cases and 0.015% of controls). These were significantly associated with a high odds ratio (P = 0.0027; OR 8.97, 95% CI 1.8-51.9). We conclude that NRXN1 deletions affecting exons confer risk of schizophrenia.

The Human Phenome Project

A principal goal of genetic research is to identify specific genotypes that are associated with human phenotypes. It will soon be possible to conduct genome-wide genotyping on a massive scale. Our current approaches for defining and assaying phenotypes may be inadequate for making optimal use of such genotypic data. We propose an international effort to create phenomic databases, that is, comprehensive assemblages of systematically collected phenotypic information, and to develop new approaches for analyzing such phenotypic data. We term this effort the Human Phenome Project and suggest a scientific and organizational scope for the project.

The DYT1 phenotype and guidelines for diagnostic testing

Objective: To develop diagnostic testing guidelines for the DYT1 GAG deletion in the Ashkenazi Jewish (AJ) and non-Jewish (NJ) primary torsion dystonia (PTD) populations and to determine the range of dystonic features in affected DYT1 deletion carriers. Methods: The authors screened 267 individuals with PTD; 170 were clinically ascertained for diagnosis and treatment, 87 were affected family members ascertained for genetic studies, and 10 were clinically and genetically ascertained and included in both groups. We used published primers and PCR amplification across the critical DYT1 region to determine GAG deletion status. Features of dystonia in clinically ascertained (affected) DYT1 GAG deletion carriers and noncarriers were compared to determine a classification scheme that optimized prediction of carriers. The authors assessed the range of clinical features in the genetically ascertained (affected) DYT1 deletion carriers and tested for differences between AJ and NJ patients. Results: The optimal algorithm for classification of clinically ascertained carriers was disease onset before age 24 years in a limb (misclassification, 16.5%; sensitivity, 95%; specificity, 80%). Although application of this classification scheme provided good separation in the AJ group (sensitivity, 96%; specificity, 88%), as well as in the group overall, it was less specific in discriminating NJ carriers from noncarriers (sensitivity, 94%; specificity, 69%). Using age 26 years as the cut-off and any site at onset gave a sensitivity of 100%, but specificity decreased to 54% (63% in AJ and 43% in NJ). Among genetically ascertained carriers, onset up to age 44 years occurred, although the great majority displayed early limb onset. There were no significant differences between AJ and NJ genetically ascertained carriers, except that a higher proportion of NJ carriers had onset in a leg, rather than an arm, and widespread disease. Conclusions: Diagnostic DYT1 testing in conjunction with genetic counseling is recommended for patients with PTD with onset before age 26 years, as this single criterion detected 100% of clinically ascertained carriers, with specificities of 43% to 63%. Testing patients with onset after age 26 years also may be warranted in those having an affected relative with early onset, as the only carriers we observed with onset at age 26 or later were genetically ascertained relatives of individuals whose symptoms started before age 26 years.

Recurrent CNVs disrupt three candidate genes in schizophrenia patients

Schizophrenia is a severe psychiatric disease with complex etiology, affecting approximately 1% of the general population. Most genetics studies so far have focused on disease association with common genetic variation, such as single-nucleotide polymorphisms (SNPs), but it has recently become apparent that large-scale genomic copy-number variants (CNVs) are involved in disease development as well. To assess the role of rare CNVs in schizophrenia, we screened 54 patients with deficit schizophrenia using Affymetrixs GeneChip 250K SNP arrays. We identified 90 CNVs in total, 77 of which have been reported previously in unaffected control cohorts. Among the genes disrupted by the remaining rare CNVs are MYT1L, CTNND2, NRXN1, and ASTN2, genes that play an important role in neuronal functioning but--except for NRXN1--have not been associated with schizophrenia before. We studied the occurrence of CNVs at these four loci in an additional cohort of 752 patients and 706 normal controls from The Netherlands. We identified eight additional CNVs, of which the four that affect coding sequences were found only in the patient cohort. Our study supports a role for rare CNVs in schizophrenia susceptibility and identifies at least three candidate genes for this complex disorder.

The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology

Efforts to identify gene variants associated with susceptibility to common diseases use three approaches: pedigree and affected sib-pair linkage studies and association studies of population samples. The different aims of these study designs reflect their derivation from biological versus epidemiological traditions. Similar principles regarding determination of the evidence levels required to consider the results statistically significant apply to both linkage and association studies, however. Such determination requires explicit attention to the prior probability of particular findings, as well as appropriate correction for multiple comparisons. For most common diseases, increasing the sample size in a study is a crucial step in achieving statistically significant genetic mapping results. Recent studies suggest that the technology and statistical methodology will soon be available to make well-powered studies feasible using any of these approaches.