Publications by authors named "Achilleas N Pitsillides"

10 Publications

  • Page 1 of 1

Sugar-Sweetened Beverage Consumption May Modify Associations Between Genetic Variants in the CHREBP (Carbohydrate Responsive Element Binding Protein) Locus and HDL-C (High-Density Lipoprotein Cholesterol) and Triglyceride Concentrations.

Circ Genom Precis Med 2021 Aug 16;14(4):e003288. Epub 2021 Jul 16.

Department of Clinical Epidemiology (R.L.G., D.O.M.-K., F.R.R., R.dM.), Leiden University Medical Center, the Netherlands.

Background: ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the locus and dyslipidemia.

Methods: Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake.

Results: In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16-3.07] mg/dL per allele; <0.0001), but not significantly among the lowest SSB consumers (=0.81; <0.0001). Similar results were observed for 2 additional variants (rs35709627 and rs71556736). For triglyceride, rs55673514 was positively associated with triglyceride concentrations only among the highest SSB consumers (β, 0.06 [95% CI, 0.02-0.09] ln-mg/dL per allele, =0.001) but not the lowest SSB consumers (=0.84; =0.0005).

Conclusions: Our results identified genetic variants in the locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in triglyceride concentrations. Registration: URL:; Unique identifier: NCT00005133, NCT00005121, NCT00005487, and NCT00000479.
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August 2021

Genome sequencing unveils a regulatory landscape of platelet reactivity.

Nat Commun 2021 06 15;12(1):3626. Epub 2021 Jun 15.

Division of Intramural Research, Population Sciences Branch, National Heart, Lung and Blood Institute, Bethesda, MD, USA.

Platelet aggregation at the site of atherosclerotic vascular injury is the underlying pathophysiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes.
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June 2021

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Nature 2021 02 10;590(7845):290-299. Epub 2021 Feb 10.

The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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February 2021

Network analysis of drug effect on triglyceride-associated DNA methylation.

BMC Proc 2018 17;12(Suppl 9):27. Epub 2018 Sep 17.

1Department of Biostatistics, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118 USA.

Background: DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites.

Methods: We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules.

Results: Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways ( values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results.

Conclusions: The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglyceride-associated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites.
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September 2018

Whole genome sequence analyses of brain imaging measures in the Framingham Study.

Neurology 2018 01 27;90(3):e188-e196. Epub 2017 Dec 27.

From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston.

Objective: We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program.

Methods: We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants.

Results: We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, = 10) and in 16q23 for hippocampal volume (MAF 0.05, = 2.7 × 10). Previously identified associations in 12q24 for hippocampal volume (rs7294919, = 4.4 × 10) and in 17q25 for WMH (rs7214628, = 2.0 × 10) were confirmed. Gene-based tests detected associations ( ≤ 2.3 × 10) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease- () and Parkinson disease-associated genes (). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways.

Conclusions: Whole genome sequence-wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.
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January 2018

Whole exome sequencing in the Framingham Heart Study identifies rare variation in HYAL2 that influences platelet aggregation.

Thromb Haemost 2017 06 16;117(6):1083-1092. Epub 2017 Mar 16.

Andrew D. Johnson, Tenure Track Investigator, Population Sciences Branch, National Heart, Lung, and Blood Institute, The Framingham Heart Study, 73 Mt. Wayte Ave. Suite #2, Framingham, MA 01702, USA, Tel.: +1 508 663 4082, E-mail:

Inhibition of platelet reactivity is a common therapeutic strategy in secondary prevention of cardiovascular disease. Genetic and environmental factors influence inter-individual variation in platelet reactivity. Identifying genes that contribute to platelet reactivity can reveal new biological mechanisms and possible therapeutic targets. Here, we examined rare coding variation to identify genes associated with platelet reactivity in a population-based cohort. To do so, we performed whole exome sequencing in the Framingham Heart Study and conducted single variant and gene-based association tests against platelet reactivity to collagen, adenosine diphosphate (ADP), and epinephrine agonists in up to 1,211 individuals. Single variant tests revealed no significant associations (p<1.44×10), though we observed a suggestive association with previously implicated MRVI1 (rs11042902, p = 1.95×10). Using gene-based association tests of rare and low-frequency variants, we found significant associations of HYAL2 with increased ADP-induced aggregation (p = 1.07×10) and GSTZ1 with increased epinephrine-induced aggregation (p = 1.62×10). HYAL2 also showed suggestive associations with epinephrine-induced aggregation (p = 2.64×10). The rare variants in the HYAL2 gene-based association included a missense variant (N357S) at a known N-glycosylation site and a nonsense variant (Q406*) that removes a glycophosphatidylinositol (GPI) anchor from the resulting protein. These variants suggest that improper membrane trafficking of HYAL2 influences platelet reactivity. We also observed suggestive associations of AR (p = 7.39×10) and MAPRE1 (p = 7.26×10) with ADP-induced reactivity. Our study demonstrates that gene-based tests and other grouping strategies of rare variants are powerful approaches to detect associations in population-based analyses of complex traits not detected by single variant tests and possible new genetic influences on platelet reactivity.
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June 2017

Association of genetic variations and gene expression in a family-based study.

BMC Proc 2016 18;10(Suppl 7):109-112. Epub 2016 Oct 18.

National Heart Lung and Blood Institute's Framingham Heart Study, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA 01702 USA ; Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA 02118 USA.

Background: Expression quantitative trait locus (eQTL) maps are considered a valuable resource in studying complex diseases. The availability of gene expression data from the Genetic Analysis Workshop 19 (GAW19) provides a great opportunity to investigate the association of gene expression with genetic variants in blood.

Methods: A total of 267 samples with gene expression and whole genome sequencing data were employed in this study. We used linear mixed models with genetic random effects along with a permutation procedure to create an eQTL map. The eQTL map was further tested in terms of functional implication, including the enrichment in disease-related variants and in regulatory regions.

Results: We identified 22,869 significant eQTLs from the GAW19 data set. These eQTLs were highly enriched with genetic loci associated with blood pressure and DNase hypersensitive regions. In addition, the majority of genes associated with eQTLs showed moderate to high heritability ( > 0.4).

Conclusions: We successfully created an eQTL map from the GAW19 data set. Our study indicated that the eQTLs were enriched within regulatory regions, and tended to have relatively high heritability.
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October 2016

Genome-wide gene-environment interactions on quantitative traits using family data.

Eur J Hum Genet 2016 07 2;24(7):1022-8. Epub 2015 Dec 2.

Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA.

Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.
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July 2016

Hypoglycemia risk and glucose variability indices derived from routine self-monitoring of blood glucose are related to laboratory measures of insulin sensitivity and epinephrine counterregulation.

Diabetes Technol Ther 2011 Jan;13(1):11-7

Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia 22904, USA.

Background: the widely held assumptions that in type 1 diabetes glucose variability may correlate with insulin sensitivity and impaired epinephrine counterregulation have not been studied directly. Here we investigate possible relationships between outpatient measures of glucose variability and risk for hypoglycemia with physiological characteristics: insulin sensitivity and hypoglycemia counterregulation.

Methods: thirty-four subjects with type 1 diabetes (14 women, 20 men; 37 ± 2.1 years old; glycosylated hemoglobin [HbA1c], 7.6  ±  0.21%) performed self-monitoring of blood glucose (SMBG) for a month, followed by an inpatient hyperinsulinemic euglycemic and hypoglycemic clamp. SMBG field data were used to calculate measures of glucose variability and risk of hypoglycemia, while the clamp procedure was used to evaluate insulin sensitivity and epinephrine response during induced hypoglycemia. Spearman partial correlations adjusted for age, duration of diabetes, body mass index, gender, and HbA1c were used to assess the relationship between the field indices of glucose variability and the physiological characteristics of diabetes.

Results: two glucose variability measures correlated positively (P < 0.01) with insulin sensitivity: the Average Daily Risk Range (ADRR) (ρ = 0.5) and the Glycemic Lability Index (ρ = 0.48). The Low Blood Glucose Index, a measure of the risk for hypoglycemia, and the ADRR correlated negatively with maximum epinephrine response during hypoglycemia: ρ = -0.46, P < 0.01 and ρ = -0.4, P = 0.03, respectively.

Conclusions: higher insulin sensitivity and lower epinephrine response during hypoglycemia are related to increased glucose variability (as quantified by the ADRR), irrespective of HbA1c and other patient characteristics. Lower epinephrine relates to risk for hypoglycemia as well.
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January 2011