Biology subjects

Amin, N.

Publications and source records attributed to Amin, N..

6 recordsLinked to original sources

bioRxiv2018

Novel DNA methylation sites of glucose and insulin homeostasis: an integrative cross-omics analysis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of the functional relevance of the phenomenon remains limited. Because obesity is the main risk factor for T2D and a driver of methylation from previous study, we aimed to explore the effect of DNA methylation in the early phases of T2D pathology while accounting for body mass index (BMI). We performed a blood-based epigenome-wide association study (EWAS) of fasting glucose and insulin among 4,808 non-diabetic European individuals and replicated the findings in an independent sample consisting of 11,750 non-diabetic subjects. We integrated blood-based in silico cross-omics databases comprising genomics, epigenomics and transcriptomics collected by BIOS project of the Biobanking and BioMolecular resources Research Infrastructure of the Netherlands (BBMRI-NL), the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, and the tissue-specific Genotype-Tissue Expression (GTEx) project. We identified and replicated nine novel differentially methylated sites in whole blood (P-value < 1.27 x 10-7): sites in LETM1, RBM20, IRS2, MAN2A2 genes and 1q25.3 region were associated with fasting insulin; sites in FCRL6, SLAMF1, APOBEC3H genes and 15q26.1 region were associated with fasting glucose. The association between SLAMF1, APOBEC3H and 15q26.1 methylation sites and glucose emerged only when accounted for BMI. Follow-up in silico cross-omics analyses indicate that the cis-acting meQTLs near SLAMF1 and SLAMF1 expression are involved in glucose level regulation. Moreover, our data suggest that differential methylation in FCRL6 may affect glucose level and the risk of T2D by regulating FCLR6 expression in the liver. In conclusion, the present study provided nine new DNA methylation sites associated with glycemia homeostasis and also provided new insights of glycemia related loci into the genetics, epigenetics and transcriptomics pathways based on the integration of cross-omics data in silico.

genomics↗

bioRxiv2018

Genetic Determinants of Cortical Structure (Thickness, Surface Area and Volumes) among Disease Free Adults in the CHARGE Consortium

Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,822 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 161 genome-wide significant associations pointing to wnt/{beta}-catenin, TGF-{beta} and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

genetics↗

bioRxiv2018

Quality Control and Integration of Genotypes from Two Calling Pipelines for Whole Genome Sequence Data in the Alzheimer’s Disease Sequencing Project

The Alzheimers Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed \"consensus calling,\" to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.\n\nAbbreviationsAD, Alzheimers disease; QC, Quality Control; LSSAC, Large-Scale Sequencing and Analysis Center; Broad, Broad Institute Genomics Service; Baylor, Baylor College of Medicine Human Genome Sequencing Center; WashU, Washington University-St. Louis McDonnell Genome Institute; WGS, whole genome sequencing; WES, whole exome sequencing; indel, insertion-deletion variants; VCF, variant control format; MI, Mendelian inconsistency; MC, Mendelian consistency; GWAS, genome-wide association study; VR, referent allele read depth; DP, overall read depth; MS, mapping score; GQ, genotype quality score; Ti/Tv, Transition/Transversion; CS, concordance code

genetics↗

bioRxiv2018

Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

Sleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits, 5 of which are not accessible through self-report alone. We identified 47 genetic associations across the sleep traits (P<5x10-8) and replicated our findings in 5,819 individuals from 3 independent studies. These included 26 novel associations for sleep quality and 10 for nocturnal sleep duration. The majority of newly identified variants were associated with a single sleep trait, except for variants previously associated with restless legs syndrome that were associated with multiple sleep traits. Of the new associated and replicated sleep duration loci, we were able to fine-map a missense variant (p.Tyr727Cys) in PDE11A, a dual-specificity 3,5-cyclic nucleotide phosphodiesterase expressed in the hippocampus, as the likely causal variant. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.

genetics↗

bioRxiv2017

Genome-wide Association Study Links APOEϵ4 and BACE1 Variants with Plasma Amyloid β Levels

INTRODUCTIONThere is increasing interest in plasma A{beta} as an endophenotype and biomarker of Alzheimers disease (AD). Identifying the genetic determinants of plasma A{beta} levels may elucidate important processes that determine plasma A{beta} measures. METHODSWe included 12,369 non-demented participants derived from eight population-based studies. Imputed genetic data and plasma A{beta}1-40, A{beta}1-42 levels and A{beta}1-42/A{beta}1-40 ratio were used to perform genome-wide association studies, gene-based and pathway analyses. Significant variants and genes were followed-up for the association with PET A{beta} deposition and AD risk. RESULTSSingle-variant analysis identified associations across APOE for A{beta}1-42 and A{beta}1-42/A{beta}1-40 ratio, and BACE1 for A{beta}1-40. Gene-based analysis of A{beta}1-40 additionally identified associations for APP, PSEN2, CCK and ZNF397. There was suggestive interaction between a BACE1 variant and APOE{varepsilon}4 on brain A{beta} deposition. DISCUSSIONIdentification of variants near/in known major A{beta}-processing genes strengthens the relevance of plasma-A{beta} levels both as an endophenotype and a biomarker of AD.

genetics↗

bioRxiv2017

Spillover effects of a combined water, sanitation, and handwashing intervention in rural Bangladesh: a randomized controlled trial

BackgroundWater, sanitation, and handwashing (WSH) interventions may confer indirect benefits (\"spillovers\") on neighbors of recipients by interrupting pathogen transmission. We measured geographically local spillovers in WASH Benefits, a cluster-randomized trial in rural Bangladesh, by comparing outcomes among neighbors of intervention vs. control participants.\n\nMethodsWASH Benefits had randomly allocated geographically-defined clusters to a compound-level intervention (chlorinated drinking water, upgraded sanitation, and handwashing promotion) or control and followed children for two years. We enrolled neighboring children age-matched to trial participants that would have been eligible for WASH Benefits had they been conceived slightly earlier or later. After 28 months of intervention, we quantified fecal indicator bacteria in toy rinse and drinking water samples, measured soil-transmitted helminth infections, and recorded caregiver-reported diarrhea and respiratory illness. Neither fieldworkers nor participants were masked. Analysis was intention-to-treat.\n\nResultsWe enrolled neighbors of WASH Benefits participants in 90 control (N=900) and 90 intervention clusters (N=899). Neighbors characteristics were balanced across arms. The prevalence of any detectable E. coli in tubewell samples was lower for neighbors of intervention vs. control (prevalence ratio=0.83; 0.73, 0.95). There was no difference in E. coli and coliform prevalence between arms for other environmental samples. Disease prevalence was similar in neighbors of intervention vs. control participants: Ascaris (prevalence difference [PD]=0.00; -0.07, 0.08), hookworm (PD=0.01; -0.01, 0.04), Trichuris (PD=0.02; -0.02, 0.05), diarrhea (PD=0.00; -0.02,0.03), respiratory illness (PD=-0.01; -0.04, 0.03).\n\nConclusionsWe found spillover effects of a compound-level combined WSH intervention for tubewell water contamination but not for child health outcomes.\n\nKey MessagesO_LIWater, sanitation, and handwashing (WSH) interventions may confer indirect benefits (\"spillovers\") on neighbors of recipients by interrupting pathogen transmission, reducing environmental contamination, or spurring the adoption of health behaviors.\nC_LIO_LIWe conducted a randomized trial in rural Bangladesh to measure whether neighbors of a compound-level WSH intervention improved hygiene behaviors and had lower prevalence environmental contamination, soil-transmitted helminth infection, diarrhea, and respiratory illness among children under 5 years after two years of intervention.\nC_LIO_LIWe did not find evidence of intervention adoption or improved hygiene behavior among neighbors of a WSH intervention delivered for 2 years.\nC_LIO_LIThe WSH intervention reduced fecal contamination of neighbors tubewell water but did not lead to spillovers for other proximal measures of contamination in the domestic environment or for child health outcomes. For proximal spillover effects to translate to distal spillover effects, improvements in neighbors health behaviors may have been necessary.\nC_LI

epidemiology↗