Publications by authors named "Karol Estrada"

84 Publications

Identifying therapeutic drug targets using bidirectional effect genes.

Nat Commun 2021 04 13;12(1):2224. Epub 2021 Apr 13.

BioMarin Pharmaceutical Inc., Novato, CA, USA.

Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-21843-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044152PMC
April 2021

Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes.

Nat Genet 2021 03 15;53(3):392-402. Epub 2021 Feb 15.

Open Targets, Wellcome Genome Campus, Cambridge, UK.

Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer's disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-020-00776-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610386PMC
March 2021

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.

PLoS Genet 2021 01 8;17(1):e1009224. Epub 2021 Jan 8.

MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom.

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1009224DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7819609PMC
January 2021

Differences in the Presentation and Progression of Parkinson's Disease by Sex.

Mov Disord 2021 01 1;36(1):106-117. Epub 2020 Oct 1.

Department of Neurology, Nottingham University NHS Trust, Nottingham, UK.

Background: Previous studies reported various symptoms of Parkinson's disease (PD) associated with sex. Some were conflicting or confirmed in only one study.

Objectives: We examined sex associations to PD phenotypes cross-sectionally and longitudinally in large-scale data.

Methods: We tested 40 clinical phenotypes, using longitudinal, clinic-based patient cohorts, consisting of 5946 patients, with a median follow-up of 3.1 years. For continuous outcomes, we used linear regressions at baseline to test sex-associated differences in presentation, and linear mixed-effects models to test sex-associated differences in progression. For binomial outcomes, we used logistic regression models at baseline and Cox regression models for survival analyses. We adjusted for age, disease duration, and medication use. In the secondary analyses, data from 17 719 PD patients and 7588 non-PD participants from an online-only, self-assessment PD cohort were cross-sectionally evaluated to determine whether the sex-associated differences identified in the primary analyses were consistent and unique to PD.

Results: Female PD patients had a higher risk of developing dyskinesia early during the follow-up period, with a slower progression in activities of daily living difficulties, and a lower risk of developing cognitive impairments compared with male patients. The findings in the longitudinal, clinic-based cohorts were mostly consistent with the results of the online-only cohort.

Conclusions: We observed sex-associated contributions to PD heterogeneity. These results highlight the necessity of future research to determine the underlying mechanisms and importance of personalized clinical management. © 2020 International Parkinson and Movement Disorder Society.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mds.28312DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883324PMC
January 2021

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

Nat Genet 2020 10 7;52(10):1122-1131. Epub 2020 Sep 7.

MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-020-0682-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610464PMC
October 2020

Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

Cell Rep 2020 07;32(2):107908

Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA.

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2020.107908DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428328PMC
July 2020

Harnessing peripheral DNA methylation differences in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease.

Clin Epigenetics 2020 06 15;12(1):84. Epub 2020 Jun 15.

Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.

Background: Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer's disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants.

Results: In this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1) validated using the genotype tissue expression project portal (GTex).

Conclusions: Our work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13148-020-00864-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294637PMC
June 2020

Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data.

BMC Bioinformatics 2019 Dec 16;20(1):709. Epub 2019 Dec 16.

Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, 64710, Mexico.

Background: Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.

Results: We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.

Conclusions: Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-019-3158-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915925PMC
December 2019

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

Mov Disord 2019 12 10;34(12):1839-1850. Epub 2019 Sep 10.

Translational Genome Sciences, Biogen, Cambridge, Massachusetts, USA.

Background: Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied.

Objectives: To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale.

Methods: We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes.

Results: Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients.

Conclusions: We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mds.27845DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017876PMC
December 2019

Genetic risk of Parkinson disease and progression:: An analysis of 13 longitudinal cohorts.

Neurol Genet 2019 Aug 9;5(4):e348. Epub 2019 Jul 9.

Laboratory of Neurogenetics (H.I., C.B., H.L.L., F.F., D.G.H., A.B.S., M.A.N.), National Institute on Aging, National Institutes of Health, Bethesda; Data Tecnica International (H.I., M.A.N.), Glen Echo, MD; Precision Neurology Program (G.L., C.R.S.), Harvard Medical School, Brigham and Women's Hospital; Neurogenomics Laboratory (G.L., C.R.S.), Harvard Medical School, Brigham and Women's Hospital; Ann Romney Center for Neurologic Diseases (G.L., C.R.S.), Brigham and Women's Hospital, Boston, MA; The Norwegian Centre for Movement Disorders (J.M.-G., G.A.), Stavanger University Hospital; Department of Chemistry (J.M.-G., G.A.), Bioscience and Environmental Engineering, University of Stavanger, Norway; Assistance-Publique Hôpitaux de Paris (J.-C.C.), ICM, INSERM UMRS 1127, CNRS 7225, ICM, Department of Neurology and CIC Neurosciences, Pitié-Salpêtrière Hospital, Paris, France; Department of Neurology (L.P., M.T.), Oslo University Hospital, Norway; Department of Neurology (M.N., B.R.B., B.P.W.), Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Michael J Fox Foundation (S.J.H.), New York; Translational Genome Sciences (K.-D.H.N, K.E.), Biogen, Cambridge, MA; Department of Neurology University of Pennsylvania (J.R.), Philadelphia, PA; Department of Biostatistics and Computational Biology (S.E.), University of Rochester, NY; Department of Computer Science (F.F.), University of Illinois Urbana-Champaign; Department of Neurology (P.A.), Center for Health + Technology, University of Rochester, NY; Department of Clinical Neurosciences (K.M.S., R.W.), University of Cambridge, John van Geest Centre for Brain Repair, UK; Department of Pathology and Laboratory Medicine (V.M.V.D.), Center for Neurodegenerative Disease Research, Parelman School of Medicine at the University of Pennsylvania, Philadelphia; Genetics and Pharmacogenomics (A.G.D.-W.), Merck Research Laboratory, Boston, MA; Statistical Genetics (A.G.D.-W.), Biogen, Cambridge, MA; Institut du cerveau et de la moelle épinière ICM (A.B., F.D.); Sorbonne Université SU (A.B.); INSERM UMR (A.B.), Paris, France; Department of Neurology (G.A.), Stavanger University Hospital, Norway; Preventive Neurology Unit (A.J.N.), Wolfson Institute of Preventive Medicine, Queen Mary University of London; Department of Molecular Neuroscience (A.J.N.), UCL Institute of Neurology, London, UK; Department of Neurology (O.-B.T.), Haukeland University Hospital; University of Bergen (O.-B.T.), Bergen, Norway; Department of Neurology (J.R.E.), Nottingham University NHS Trust, UK; Centre for Clinical Brain Sciences (D.P.B.), University of Edinburgh; Anne Rowling Regenerative Neurology Clinic (D.P.B.), University of Edinburgh; Usher Institute of Population Health Sciences and Informatics (D.P.B.), University of Edinburgh, Scotland; Department of Medical and Molecular Genetics (C.E.W.), Indiana University, Indianapolis; Department of Neurology (D.K.S.), Beth Israel Deaconess Medical Center; Harvard Medical School (D.K.S.), Boston; Voyager Therapeutics (B.R.), Cambridge, MA; Department of Neurology (B.R.), University of Rochester School of Medicine, NY; Institute of Clinical Medicine (M.T.), University of Oslo, Norway; German Center for Neurodegenerative Diseases-Tubingen (P.H.); HIH Tuebingen (P.H.), Germany; Department of Psychiatry (D.W.), University of Pennsylvania School of Medicine; Department of Veterans Affairs (D.W.), Philadelphia, PA; and Department of Clinical Neurosciences (R.A.B., C.H.W.-G.), University of Cambridge, UK; Department of Neurology (J.J.V.H.), Leiden University Medical Center, The Netherlands.

Objective: To determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression.

Methods: We evaluated the association between 31 risk variants and variables measuring disease progression. A total of 23,423 visits by 4,307 patients of European ancestry from 13 longitudinal cohorts in Europe, North America, and Australia were analyzed.

Results: We confirmed the importance of on phenotypes. variants were associated with the development of daytime sleepiness (p.N370S: hazard ratio [HR] 3.28 [1.69-6.34]) and possible REM sleep behavior (p.T408M: odds ratio 6.48 [2.04-20.60]). We also replicated previously reported associations of variants with motor/cognitive declines. The other genotype-phenotype associations include an intergenic variant near and the faster development of motor symptom (Hoehn and Yahr scale 3.0 HR 1.33 [1.16-1.52] for the C allele of rs76904798) and an intronic variant in and the development of wearing-off effects (HR 1.66 [1.19-2.31] for the C allele of rs114138760). Age at onset was associated with variant p.M393T (-0.72 [-1.21 to -0.23] in years), the C allele of rs199347 (intronic region of , 0.70 [0.27-1.14]), and G allele of rs1106180 (intronic region of , 0.62 [0.21-1.03]).

Conclusions: This study provides evidence that alleles associated with Parkinson disease risk, in particular variants, also contribute to the heterogeneity of multiple motor and nonmotor aspects. Accounting for genetic variability will be a useful factor in understanding disease course and in minimizing heterogeneity in clinical trials.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1212/NXG.0000000000000348DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659137PMC
August 2019

Detecting Clustered Independent Rare Variant Associations Using Genetic Algorithms.

IEEE/ACM Trans Comput Biol Bioinform 2021 May-Jun;18(3):932-939. Epub 2021 Jun 3.

The availability of an increasing collection of sequencing data provides the opportunity to study genetic variation with an unprecedented level of detail. There is much interest in uncovering the role of rare variants and their contribution to disease. However, detecting associations of rare variants with small minor allele frequencies (MAF) and modest effects remains a challenge for rare variant association methods. Due to this low signal-to-noise ratio, most methods are underpowered to detect associations even when conducting rare variant association tests at the gene level. We present a new method for detecting rare variant associations. The algorithm consists of two steps. In the first step, a genetic algorithm searches for a promising genomic region containing a collection of genes with causal rare variants. In the second step, a genetic algorithm aims at removing false positives from the located genomic region. We tested the proposed method with a collection of datasets obtained from real exome data. The proposed method possesses sufficient power for detecting associations of rare variants with complex phenotypes. This method can be used for studying the contribution of rare variants with complex disease, particularly in cases where single-variant or gene-based tests are underpowered.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCBB.2019.2930505DOI Listing
June 2021

BIN1 favors the spreading of Tau via extracellular vesicles.

Sci Rep 2019 07 1;9(1):9477. Epub 2019 Jul 1.

Third Rock Ventures, 29 Newbury Street, Suite 30, Boston, MA, 02116, USA.

Despite Bridging INtegrator 1 (BIN1) being the second most statistically-significant locus associated to Late Onset Alzheimer's Disease, its role in disease pathogenesis remains to be clarified. As reports suggest a link between BIN1, Tau and extracellular vesicles, we investigated whether BIN1 could affect Tau spreading via exosomes secretion. We observed that BIN1-associated Tau-containing extracellular vesicles purified from cerebrospinal fluid of AD-affected individuals are seeding-competent. We showed that BIN1 over-expression promotes the release of Tau via extracellular vesicles in vitro as well as exacerbation of Tau pathology in vivo in PS19 mice. Genetic deletion of Bin1 from microglia resulted in reduction of Tau secretion via extracellular vesicles in vitro, and in decrease of Tau spreading in vivo in male, but not female, mice, in the context of PS19 background. Interestingly, ablation of Bin1 in microglia of male mice resulted in significant reduction in the expression of heat-shock proteins, previously implicated in Tau proteostasis. These observations suggest that BIN1 could contribute to the progression of AD-related Tau pathology by altering Tau clearance and promoting release of Tau-enriched extracellular vesicles by microglia.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-45676-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603165PMC
July 2019

Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry.

J Bone Miner Res 2019 07 19;34(7):1284-1296. Epub 2019 Mar 19.

Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA.

Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility. © 2019 American Society for Bone and Mineral Research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jbmr.3698DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650334PMC
July 2019

Disentangling the genetics of lean mass.

Am J Clin Nutr 2019 02;109(2):276-287

Icelandic Heart Association Holtasmari, Kopavogur, Iceland.

Background: Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass.

Objectives: To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci.

Methods: We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms).

Results: Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as "sumo wrestler" loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed "body builder" loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in "body builder" loci were associated with metabolic protection.

Conclusions: In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ajcn/nqy272DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500901PMC
February 2019

Phenome-wide association studies across large population cohorts support drug target validation.

Nat Commun 2018 10 16;9(1):4285. Epub 2018 Oct 16.

Genomics plc, Oxford, OX1 1JD, UK.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-06540-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6191429PMC
October 2018

Translating Human Genetics into Novel Drug Targets.

Authors:
Karol Estrada

Methods Mol Biol 2018 ;1793:277-290

Translational Genome Sciences, Biogen, Cambridge, MA, USA.

The most important promise of the human genome sequencing project is the identification of the genetic cause of devastating human diseases and the subsequent deliver of novel drug therapies to treat these diseases with high unmet medical need. In the last 10 years we have successfully identified hundreds of genetic loci associated with many traits and diseases. The translation of these findings into novel therapies is not straightforward and poses challenges that are usually overlooked in traditional gene mapping. This chapter describes some of the most common challenges and opportunities to use human genetics to identify and validate novel drug targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-7868-7_16DOI Listing
February 2019

A whole-genome sequence study identifies genetic risk factors for neuromyelitis optica.

Nat Commun 2018 05 16;9(1):1929. Epub 2018 May 16.

Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.

Neuromyelitis optica (NMO) is a rare autoimmune disease that affects the optic nerve and spinal cord. Most NMO patients ( > 70%) are seropositive for circulating autoantibodies against aquaporin 4 (NMO-IgG+). Here, we meta-analyze whole-genome sequences from 86 NMO cases and 460 controls with genome-wide SNP array from 129 NMO cases and 784 controls to test for association with SNPs and copy number variation (total N = 215 NMO cases, 1244 controls). We identify two independent signals in the major histocompatibility complex (MHC) region associated with NMO-IgG+, one of which may be explained by structural variation in the complement component 4 genes. Mendelian Randomization analysis reveals a significant causal effect of known systemic lupus erythematosus (SLE), but not multiple sclerosis (MS), risk variants in NMO-IgG+. Our results suggest that genetic variants in the MHC region contribute to the etiology of NMO-IgG+ and that NMO-IgG+ is genetically more similar to SLE than MS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-04332-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955905PMC
May 2018

Identification of a novel locus on chromosome 2q13, which predisposes to clinical vertebral fractures independently of bone density.

Ann Rheum Dis 2018 03 23;77(3):378-385. Epub 2017 Nov 23.

Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Queensland, Australia.

Objectives: To identify genetic determinants of susceptibility to clinical vertebral fractures, which is an important complication of osteoporosis.

Methods: Here we conduct a genome-wide association study in 1553 postmenopausal women with clinical vertebral fractures and 4340 controls, with a two-stage replication involving 1028 cases and 3762 controls. Potentially causal variants were identified using expression quantitative trait loci (eQTL) data from transiliac bone biopsies and bioinformatic studies.

Results: A locus tagged by rs10190845 was identified on chromosome 2q13, which was significantly associated with clinical vertebral fracture (P=1.04×10) with a large effect size (OR 1.74, 95% CI 1.06 to 2.6). Bioinformatic analysis of this locus identified several potentially functional SNPs that are associated with expression of the positional candidate genes (tubulin tyrosine ligase) and (solute carrier family 20 member 1). Three other suggestive loci were identified on chromosomes 1p31, 11q12 and 15q11. All these loci were novel and had not previously been associated with bone mineral density or clinical fractures.

Conclusion: We have identified a novel genetic variant that is associated with clinical vertebral fractures by mechanisms that are independent of BMD. Further studies are now in progress to validate this association and evaluate the underlying mechanism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/annrheumdis-2017-212469DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5912156PMC
March 2018

A Pilot Genome-Wide Association Study in Postmenopausal Mexican-Mestizo Women Implicates the RMND1/CCDC170 Locus Is Associated with Bone Mineral Density.

Int J Genomics 2017 3;2017:5831020. Epub 2017 Aug 3.

Laboratorio de Genómica del Metabolismo Óseo, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.

To identify genetic variants influencing bone mineral density (BMD) in the Mexican-Mestizo population, we performed a GWAS for femoral neck (FN) and lumbar spine (LS) in Mexican-Mestizo postmenopausal women. In the discovery sample, 300,000 SNPs were genotyped in a cohort of 411 postmenopausal women and seven SNPs were analyzed in the replication cohort ( = 420). The combined results of a meta-analysis from the discovery and replication samples identified two loci, (rs6904364, = 2.77 × 10) and (rs17081341, = 1.62 × 10), associated with FN BMD. We also compared our results with those of the Genetic Factors for Osteoporosis (GEFOS) Consortium meta-analysis. The comparison revealed two loci previously reported in the GEFOS meta-analysis: (rs7128738) and (rs11887431) associated with FN and LS BMD, respectively, in our study population. Interestingly, rs17081341 rare in Caucasians (minor allele frequency < 0.03) was found in high frequency in our population, which suggests that this association could be specific to non-Caucasian populations. In conclusion, the first pilot Mexican GWA study of BMD confirmed previously identified loci and also demonstrated the importance of studying variability in diverse populations and/or specific populations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1155/2017/5831020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559934PMC
August 2017

A Loss-of-Function Splice Acceptor Variant in Is Protective for Type 2 Diabetes.

Diabetes 2017 11 24;66(11):2903-2914. Epub 2017 Aug 24.

Department of Genetics, Harvard Medical School, Boston, MA.

Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage-dependent reduction in the expression of isoform 2. In individuals who do not carry the protective allele, expression of isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2337/db17-0187DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652606PMC
November 2017

Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Nat Commun 2017 07 19;8(1):80. Epub 2017 Jul 19.

Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA.

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10) or suggestively genome wide (p < 2.3 × 10). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-017-00031-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5517526PMC
July 2017

Targeted sequencing of genome wide significant loci associated with bone mineral density (BMD) reveals significant novel and rare variants: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) targeted sequencing study.

Hum Mol Genet 2016 12;25(23):5234-5243

The Institute for Aging Research Hebrew SeniorLife and the Department of Medicine Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Background: Bone mineral density (BMD) is a heritable phenotype that predicts fracture risk. We performed fine-mapping by targeted sequencing at WLS, MEF2C, ARHGAP1/F2 and JAG1 loci prioritized by eQTL and bioinformatic approaches among 56 BMD loci from our previous GWAS meta-analysis. Targeted sequencing was conducted in 1,291 Caucasians from the Framingham Heart Study ( n =  925) and Cardiovascular Health Study ( n =  366), including 206 women and men with extreme low femoral neck (FN) BMD. A total of 4,964 sequence variants (SNVs) were observed and 80% were rare with MAF <1%. The associations between previously identified SNPs in these loci and BMD, while nominally significant in sequenced participants, were no longer significant after multiple testing corrections. Conditional analyses did not find protein-coding variants that may be responsible for GWAS signals. On the other hand, in the sequenced subjects, we identified novel associations in WLS , ARHGAP1 , and 5' of MEF2C ( P- values < 8x10  -   5 ; false discovery rate (FDR) q-values < 0.01) that were much more strongly associated with BMD compared to the GWAS SNPs. These associated SNVs are less-common; independent from previous GWAS signals in the same loci; and located in gene regulatory elements. Our findings suggest that protein-coding variants in selected GWAS loci did not contribute to GWAS signals. By performing targeted sequencing in GWAS loci, we identified less-common and rare non-coding SNVs associated with BMD independently from GWAS common SNPs, suggesting both common and less-common variants may associate with disease risks and phenotypes in the same loci.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/hmg/ddw289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837042PMC
December 2016

Analysis of protein-coding genetic variation in 60,706 humans.

Nature 2016 08;536(7616):285-91

Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/nature19057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018207PMC
August 2016

Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis.

Nat Genet 2016 09 25;48(9):1043-8. Epub 2016 Jul 25.

Department of Molecular Genetics, Institute of Pathology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.

To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1-10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/ng.3622DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556360PMC
September 2016

Association of a Genetic Risk Score With Body Mass Index Across Different Birth Cohorts.

JAMA 2016 Jul;316(1):63-9

Department of Epidemiology and Biostatistics, University of California, San Francisco.

Importance: Many genetic variants are associated with body mass index (BMI). Associations may have changed with the 20th century obesity epidemic and may differ for black vs white individuals.

Objective: Using birth cohort as an indicator for exposure to obesogenic environment, to evaluate whether genetic predisposition to higher BMI has a larger magnitude of association among adults from more recent birth cohorts, who were exposed to the obesity epidemic at younger ages.

Design, Setting, And Participants: Observational study of 8788 adults in the US national Health and Retirement Study who were aged 50 years and older, born between 1900 and 1958, with as many as 12 BMI assessments from 1992 to 2014.

Exposures: A multilocus genetic risk score for BMI (GRS-BMI), calculated as the weighted sum of alleles of 29 single nucleotide polymorphisms associated with BMI, with weights equal to the published per-allele effects. The GRS-BMI represents how much each person's BMI is expected to differ, based on genetic background (with respect to these 29 loci), from the BMI of a sample member with median genetic risk. The median-centered GRS-BMI ranged from -1.68 to 2.01.

Main Outcomes And Measures: BMI based on self-reported height and weight.

Results: GRS-BMI was significantly associated with BMI among white participants (n = 7482; mean age at first assessment, 59 years; 3373 [45%] were men; P <.001) and among black participants (n = 1306; mean age at first assessment, 57 years; 505 [39%] were men; P <.001) but accounted for 0.99% of variation in BMI among white participants and 1.37% among black participants. In multilevel models accounting for age, the magnitude of associations of GRS-BMI with BMI were larger for more recent birth cohorts. For example, among white participants, each unit higher GRS-BMI was associated with a difference in BMI of 1.37 (95% CI, 0.93 to 1.80) if born after 1943, and 0.17 (95% CI, -0.55 to 0.89) if born before 1924 (P = .006). For black participants, each unit higher GRS-BMI was associated with a difference in BMI of 3.70 (95% CI, 2.42 to 4.97) if born after 1943, and 1.44 (95% CI, -1.40 to 4.29) if born before 1924.

Conclusions And Relevance: For participants born between 1900 and 1958, the magnitude of association between BMI and a genetic risk score for BMI was larger among persons born in later cohorts. This suggests that associations of known genetic variants with BMI may be modified by obesogenic environments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1001/jama.2016.8729DOI Listing
July 2016

Quantifying prion disease penetrance using large population control cohorts.

Sci Transl Med 2016 Jan;8(322):322ra9

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid 28031, Spain.

More than 100,000 genetic variants are reported to cause Mendelian disease in humans, but the penetrance-the probability that a carrier of the purported disease-causing genotype will indeed develop the disease-is generally unknown. We assess the impact of variants in the prion protein gene (PRNP) on the risk of prion disease by analyzing 16,025 prion disease cases, 60,706 population control exomes, and 531,575 individuals genotyped by 23andMe Inc. We show that missense variants in PRNP previously reported to be pathogenic are at least 30 times more common in the population than expected on the basis of genetic prion disease prevalence. Although some of this excess can be attributed to benign variants falsely assigned as pathogenic, other variants have genuine effects on disease susceptibility but confer lifetime risks ranging from <0.1 to ~100%. We also show that truncating variants in PRNP have position-dependent effects, with true loss-of-function alleles found in healthy older individuals, a finding that supports the safety of therapeutic suppression of prion protein expression.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/scitranslmed.aad5169DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774245PMC
January 2016

Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture.

Nature 2015 Oct 14;526(7571):112-7. Epub 2015 Sep 14.

Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA.

The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/nature14878DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755714PMC
October 2015

Genome-wide association study in an admixed case series reveals IL12A as a new candidate in Behçet disease.

PLoS One 2015 23;10(3):e0119085. Epub 2015 Mar 23.

Internal medicine, Section immunology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Immunology, Erasmus Medical Center, Rotterdam, The Netherlands.

Introduction: The etiology of Behçet's disease (BD) is unknown, but widely considered an excessive T-cell mediated inflammatory response in a genetically susceptible host. Recent genome-wide association studies (GWAS) have shown limited number of novel loci-associations. The rarity and unequal distribution of the disease prevalence amongst different ethnic backgrounds have hampered the use of GWAS in cohorts of mixed ethnicity and sufficient sample size. However, novel statistical approaches have now enabled GWAS in admixed cohorts.

Methods: We ran a GWAS on 336 BD cases and 5,843 controls. The cases consisted of Western Europeans, Middle Eastern and Turkish individuals. Participants from the Generation R study, a multiethnic birth cohort in Rotterdam, The Netherlands were used as controls. All samples were genotyped and data was combined. Linear regression models were corrected for population stratification using Genomic Principal Components and Linear Mixed Modelling. Meta-analysis was performed on selected results previously published.

Results: We identified SNPs associated at genome-wide significant level mapping to the 6p21.33 (HLA) region. In addition to this known signal two potential novel associations on chromosomes 6 and 18 were identified, yet with low minor allele frequencies. Extended meta-analysis reveal a GWS association with the IL12A variant rs17810546 on chromosome 3.

Discussion: We demonstrate that new statistical techniques enable GWAS analyses in a limited sized cohort of mixed ethnicity. After implementation, we confirmed the central role of the HLA region in the disease and identified new regions of interest. Moreover, we validated the association of a variant in the IL2A gene by meta-analysis with previous work. These findings enhance our knowledge of genetic associations and BD, and provide further justification for pursuing collective initiatives in genetic studies given the low prevalence of this and other rare diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0119085PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4370488PMC
February 2016
-->