Juan R. González

Global variation in copy number in the human genome

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

SNPassoc: an R package to perform whole genome association studies

UNLABELLED The popularization of large-scale genotyping projects has led to the widespread adoption of genetic association studies as the tool of choice in the search for single nucleotide polymorphisms (SNPs) underlying susceptibility to complex diseases. Although the analysis of individual SNPs is a relatively trivial task, when the number is large and multiple genetic models need to be explored it becomes necessary a tool to automate the analyses. In order to address this issue, we developed SNPassoc, an R package to carry out most common analyses in whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). AVAILABILITY Package SNPassoc is available at CRAN from http://cran.r-project.org. SUPPLEMENTARY INFORMATION A tutorial is available on Bioinformatics online and in http://davinci.crg.es/estivill_lab/snpassoc.

Brain-Derived Neurotrophic Factor Val66Met and Psychiatric Disorders: Meta-Analysis of Case-Control Studies Confirm Association to Substance-Related Disorders, Eating Disorders, and Schizophrenia

BACKGROUND There is an increasing recognition that the pathophysiology of mental disorders could be the result of deregulation of synaptic plasticity with alterations of neurotrophins. The valine (Val)66-to-methionine (Met) variant, located in the pro brain-derived neurotrophic factor (BDNF) sequence, has been extensively studied through linkage and association approaches in several psychiatric disorders. METHODS We performed a meta-analysis restricted to individual case-control studies in different categories of mental disorders and BDNF Val66Met polymorphism. We included data from 39 case-control studies encompassing psychiatric phenotypes: eating disorders, substance-related disorders, mood disorders, and schizophrenia, among others. RESULTS The association of Val66Met was confined to three diagnoses: substance-related disorders, eating disorders, and schizophrenia. The Val/Met and the Met/Met genotypes increase the risk for eating disorders up to 33%, while these same genotypes confer a 21% protective effect in substance-related disorders. The homozygous carriers Met/Met showed a 19% increased risk of schizophrenia with respect to the heterozygous state. CONCLUSIONS The study confirms the association of Val66Met to substance-related disorders, eating disorders, and schizophrenia. It remains to be determined if other variants in tight linkage disequilibrium with Val66Met could configure an extended functional haplotype that would explain observed discrepancies in risk estimations across studies.

Principal Component Analysis of the Effects of Environmental Enrichment and (-)-epigallocatechin-3-gallate on Age-Associated Learning Deficits in a Mouse Model of Down Syndrome.

Down syndrome (DS) individuals present increased risk for Alzheimers disease (AD) neuropathology and AD-type dementia. Here, we investigated the use of green tea extracts containing (-)-epigallocatechin-3-gallate (EGCG), as co-adjuvant to enhance the effects of environmental enrichment (EE) in Ts65Dn mice, a segmental trisomy model of DS that partially mimics DS/AD pathology, at the age of initiation of cognitive decline. Classical repeated measures ANOVA showed that combined EE-EGCG treatment was more efficient than EE or EGCG alone to improve specific spatial learning related variables. Using principal component analysis (PCA) we found that several spatial learning parameters contributed similarly to a first PC and explained a large proportion of the variance among groups, thus representing a composite learning measure. This PC1 revealed that EGCG or EE alone had no significant effect. However, combined EE-EGCG significantly ameliorated learning alterations of middle age Ts65Dn mice. Interestingly, PCA revealed an increased variability along learning sessions with good and poor learners in Ts65Dn, and this stratification did not disappear upon treatments. Our results suggest that combining EE and EGCG represents a viable therapeutic approach for amelioration of age-related cognitive decline in DS, although its efficacy may vary across individuals.

Genetic factors conferring an increased susceptibility to develop Crohn's disease also influence disease phenotype: results from the IBDchip European Project

Objective Through genome-wide association scans and meta-analyses thereof, over 70 genetic loci (Crohns disease (CD) single nucleotide polymorphisms (SNPs)) are significantly associated with CD. We aimed to investigate the influence of CD-SNPs and basic patient characteristics on CD clinical course, and develop statistical models to predict CD clinical course. Design This retrospective study included 1528 patients with CD with more than 10 years of follow-up from eight European referral hospitals. CD outcomes of interest were ileal (L1), colonic (L2) and ileocolonic disease location (L3); stenosing (B2) or penetrating behaviour (B3); perianal disease; extraintestinal manifestations; and bowel resection. A complicated disease course was defined as stenosing or penetrating behaviour, perianal disease and/or bowel resection. Association between CD-SNPs or patient characteristics and specified outcomes was studied. Results Several CD-SNPs and clinical characteristics were statistically associated with outcomes of interest. The NOD2 gene was the most important genetic factor, being an independent predictive factor for ileal location (p=2.02×10-06, OR=1.90), stenosing (p=3.16×10-06, OR=1.82) and penetrating (p=1.26×10-02, OR=1.25) CD behaviours, and need for surgery (p=2.28×e-05, OR=1.73), and as such was also the strongest factor associated with a complicated disease course (p=6.86×10-06, OR=2.96). Immunomodulator (azathioprine/6-mercaptopurine and methotrexate) use within 3 years after diagnosis led to a reduction in bowel stenoses (p=1.48×10-06, OR=0.35) and surgical rate (p=1.71×10-07, OR=0.34). Association between each outcome and genetic scores, created using significant SNPs in the univariate analysis, revealed large differences in the probability of developing fistulising disease (IL23R, LOC441108, PRDM1, NOD2; p=9.64e-4, HR=1.43), need for surgery (IRGM, TNFSF15, C13ORF31, NOD2; p=7.12×10-03, HR=1.35), and stenosing disease (NOD2, JAK2, ATG16L1; p=3.01×10-02, HR=1.29) among patients with low and high score. Conclusions This large multicentre cohort study has found several genetic and clinical factors influencing the clinical course of CD. NOD2 and early immunomodulator use are the clinically most meaningful predictors for its clinical course.

The Human Early-Life Exposome (HELIX): Project Rationale and Design

Background: Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure–health effect relationships. The “exposome” concept encompasses the totality of exposures from conception onward, complementing the genome. Objectives: The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the “early-life exposome.” Here we describe the general design of the project. Methods: In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother–child pairs, and biomarkers will be measured in a subset of 1,200 mother–child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure–response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures. Conclusions: HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome. Citation: Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagaña X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Gražulevičienė R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RR, Meltzer HM, Sabidó E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ. 2014. The Human Early-Life Exposome (HELIX): project rationale and design. Environ Health Perspect 122:535–544; http://dx.doi.org/10.1289/ehp.1307204

Maximizing association statistics over genetic models

The assessment of the association between a candidate locus and a disease may require the assumption of an inheritance model. Most researchers select the additive model and test the association with the Cochran‐Armitage trend test. This test assumes a dose‐response effect with regard to the number of copies of the variant allele. However, if there is reason to expect dominance or recessiveness in the effect of the variant allele, the heterozygous genotype may be grouped with one of the two homozygous, depending on the inheritance model, and a simple test on the 2 × 2 table can be used to assess independence. When the underlying genetic model is unknown, association may be assessed using the max‐statistic, which selects the largest test statistic from the dominant, recessive and additive models. The statistical significance of the max‐statistic has been previously addressed using permutation or Monte Carlo simulation approaches. We aimed to provide simpler alternatives to the max‐test to make it feasible in large‐scale association studies. Our simulations show that this procedure has an effective number of tests of 2.2, which can be used to correct the significance level or P‐values. We also derive the asymptotic distribution of max‐statistic, which leads to a simple way to calculate the significance level and allows the derivation of a formula for power calculations in the design of studies that plan to use the max‐statistic. A simulation study shows that the use of the max‐statistic is a powerful approach that provides safeguard against model uncertainty. Genet. Epidemiol. 2008 Wiley‐Liss, Inc.

Genetic Variants of the FADS Gene Cluster and ELOVL Gene Family, Colostrums LC-PUFA Levels, Breastfeeding, and Child Cognition

Introduction Breastfeeding effects on cognition are attributed to long-chain polyunsaturated fatty acids (LC-PUFAs), but controversy persists. Genetic variation in fatty acid desaturase (FADS) and elongase (ELOVL) enzymes has been overlooked when studying the effects of LC-PUFAs supply on cognition. We aimed to: 1) to determine whether maternal genetic variants in the FADS cluster and ELOVL genes contribute to differences in LC-PUFA levels in colostrum; 2) to analyze whether these maternal variants are related to child cognition; and 3) to assess whether childrens variants modify breastfeeding effects on cognition. Methods Data come from two population-based birth cohorts (n = 400 mother-child pairs from INMA-Sabadell; and n = 340 children from INMA-Menorca). LC-PUFAs were measured in 270 colostrum samples from INMA-Sabadell. Tag SNPs were genotyped both in mothers and children (13 in the FADS cluster, 6 in ELOVL2, and 7 in ELOVL5). Child cognition was assessed at 14 mo and 4 y using the Bayley Scales of Infant Development and the McCarthy Scales of Childrens Abilities, respectively. Results Children of mothers carrying genetic variants associated with lower FADS1 activity (regulating AA and EPA synthesis), higher FADS2 activity (regulating DHA synthesis), and with higher EPA/AA and DHA/AA ratios in colostrum showed a significant advantage in cognition at 14 mo (3.5 to 5.3 points). Not being breastfed conferred an 8- to 9-point disadvantage in cognition among children GG homozygote for rs174468 (low FADS1 activity) but not among those with the A allele. Moreover, not being breastfed resulted in a disadvantage in cognition (5 to 8 points) among children CC homozygote for rs2397142 (low ELOVL5 activity), but not among those carrying the G allele. Conclusion Genetically determined maternal supplies of LC-PUFAs during pregnancy and lactation appear to be crucial for child cognition. Breastfeeding effects on cognition are modified by child genetic variation in fatty acid desaturase and elongase enzymes.

Identification of new putative susceptibility genes for several psychiatric disorders by association analysis of regulatory and non-synonymous SNPs of 306 genes involved in neurotransmission and neurodevelopment†‡

A fundamental difficulty in human genetics research is the identification of the spectrum of genetic variants that contribute to the susceptibility to common/complex disorders. We tested here the hypothesis that functional genetic variants may confer susceptibility to several related common disorders. We analyzed five main psychiatric diagnostic categories (substance‐abuse, anxiety, eating, psychotic, and mood disorders) and two different control groups, representing a total of 3,214 samples, for 748 promoter and non‐synonymous single nucleotide polymorphisms (SNPs) at 306 genes involved in neurotransmission and/or neurodevelopment. We identified strong associations to individual disorders, such as growth hormone releasing hormone (GHRH) with anxiety disorders, prolactin regulatory element (PREB) with eating disorders, ionotropic kainate glutamate receptor 5 (GRIK5) with bipolar disorder and several SNPs associated to several disorders, that may represent individual and related disease susceptibility factors. Remarkably, a functional SNP, rs945032, located in the promoter region of the bradykinin receptor B2 gene (BDKRB2) was associated to three disorders (panic disorder, substance abuse, and bipolar disorder), and two additional BDKRB2 SNPs to obsessive‐compulsive disorder and major depression, providing evidence for common variants of susceptibility to several related psychiatric disorders. The association of BDKRB2 (odd ratios between 1.65 and 3.06) to several psychiatric disorders supports the view that a common genetic variant could confer susceptibility to clinically related phenotypes, and defines a new functional hint in the pathophysiology of psychiatric diseases.

A brain-derived neurotrophic factor (BDNF) haplotype is associated with antidepressant treatment outcome in mood disorders.

Brain-derived neurotrophic factor (BDNF) has been studied extensively in relation to the susceptibility to mood disorders (MD), although it remains to be clarified whether BDNF is a susceptibility locus for MD phenotypes, including therapeutic response to antidepressants. We have performed a single-marker and haplotype association study of eight TagSNPs polymorphisms in the genomic region containing the BDNF gene in 342 control subjects and 374 patients with MD, and have tested the association with antidepressant treatment outcome. None of the eight single nucleotide polymorphisms (TagSNPs) was significantly associated with MD phenotype after Bonferroni correction. In the single-marker analysis, a SNP was found to be associated with the patients state of ‘remitter’ after adequate trial with a single antidepressant phenotype (odds ratio (OR)=2.95; P=0.0025). We also identified a haplotype associated with this phenotype. This study supports the implication of BDNF in antidepressant treatment outcome in MD, with specific association with 5′ upstream region of BDNF gene.