Publications by authors named "Yun Ju Sung"

65 Publications

Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders.

Nat Neurosci 2021 09 8;24(9):1302-1312. Epub 2021 Jul 8.

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer's disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer's disease, Parkinson's disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.
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http://dx.doi.org/10.1038/s41593-021-00886-6DOI Listing
September 2021

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure.

Mol Psychiatry 2021 Apr 15. Epub 2021 Apr 15.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 P < 5 × 10), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (P < 5 × 10). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (P = 2 × 10). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (P < 10). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.
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http://dx.doi.org/10.1038/s41380-021-01087-0DOI Listing
April 2021

Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions.

Eur J Hum Genet 2021 May 26;29(5):839-850. Epub 2021 Jan 26.

Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.
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http://dx.doi.org/10.1038/s41431-021-00808-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110957PMC
May 2021

Functional Analysis of Rare Genetic Variants in Complement Factor I () using a Serum-Based Assay in Advanced Age-related Macular Degeneration.

Transl Vis Sci Technol 2020 08 24;9(9):37. Epub 2020 Aug 24.

Department of Ophthalmology and Visual Sciences, University of Massachusetts School of Medicine, Worcester, MA, USA.

Purpose: Factor I (FI) is a serine protease regulator of the complement system. Genetic variants in are associated with advanced age-related macular degeneration (AAMD). However, the clinical and functional impact of these variants is unknown. This study assessed the functional significance of rare variants using a serum-based assay.

Methods: Carriers of rare variants with (n = 78) and without AAMD (n = 28), and noncarriers with (n = 49) and without AMD (n = 44) were evaluated. Function of FI was determined by measuring the proteolytic cleavage of C3b to iC3b, using the cofactor protein, Factor H.

Results: variants were categorized into three groups based on antigenic and functional assessments. Type 1 variants (n = 18) in 35 patients with AAMD demonstrated low serum FI levels and a corresponding decrease in FI function. Type 2 variants (n = 6) in 7 individuals demonstrated normal serum FI antigenic levels but reduced degradation of C3b to iC3b. Type 3 variants (n = 15) in 64 individuals demonstrated normal antigenic levels and degradation of C3b to iC3b. However, iC3b generation was low when measured per unit of FI. Thus most rare variants demonstrate either low antigenic levels (type 1) or normal levels but reduced function (types 2 or 3).

Conclusions: Results provide for the first time a comprehensive functional assessment in serum of rare genetic variants and further establish FI's key role in the pathogenesis of AAMD.

Translational Relevance: Stratifying patients in the clinic with a rare variant will facilitate screening and targeting patients most likely to benefit from complement therapies.
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http://dx.doi.org/10.1167/tvst.9.9.37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453046PMC
August 2020

Efficient gene-environment interaction tests for large biobank-scale sequencing studies.

Genet Epidemiol 2020 11 30;44(8):908-923. Epub 2020 Aug 30.

Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas.

Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.
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http://dx.doi.org/10.1002/gepi.22351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754763PMC
November 2020

sp. nov., isolated from freshwater sediment and reclassification of as comb. nov.

Int J Syst Evol Microbiol 2020 Jun 8;70(6):3878-3887. Epub 2020 Jun 8.

College of Biology and the Environment, Co-Innovation Centre for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, PR China.

A polyphasic taxonomic study was carried out on strains CHu50b-3-2 and CHu40b-3-1 isolated from a 67 cm-long sediment core collected from the Daechung Reservoir at a water depth of 17 m, Daejeon, Republic of Korea. The cells of the strains were Gram-stain-negative, non-spore-forming, non-motile and rod-shaped. Comparative 16S rRNA gene sequence studies showed a clear affiliation of two strains with , which showed the highest pairwise sequence similarities to KTce-2 (96.5 %), Gsoil193 (96.3 %), Gsoil 357 (96.1 %), T20R-70 (96.1 %), KCTC 12130 (95.4 %) and YC5194 (95.3 %). The phylogenetic analysis based on 16S rRNA gene sequences showed that the strains formed a clear phylogenetic lineage with the genus . The major fatty acids were identified as summed feature 9 (iso-C 9 and/or C 10-methyl), iso-C, iso-C and iso-C. The respiratory quinone was identified as ubiquinone Q-8. The major polar lipids were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine and an unidentified phospholipid. The genomic DNA G+C content was determined to be 66.8 mol% (genome) for strain CHu50b-3-2 and 66.4 mol% (HPLC) for strain CHu40b-3-1. Based on the combined genotypic and phenotypic data, we propose that strains CHu50b-3-2 and CHu40b-3-1 represent a novel species of the genus , for which the name sp. nov. is proposed. The type strain is CHu50b-3-2 (=KCTC 72973=CCTCC AB 2019129). Besides Gsoil 068 formed a phylogenetic group together with strain RIB1-20 (EF626688) that is clearly separated from all other known strains. Based on the phylogenetic relationships together with fatty acid compositions, Gsoil 068 should be reclassified as a member of the genus comb. nov. (type strain Gsoil 068=KCTC 12601=DSM 17927).
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http://dx.doi.org/10.1099/ijsem.0.004253DOI Listing
June 2020

Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci.

Mol Psychiatry 2021 Jun 5;26(6):2111-2125. Epub 2020 May 5.

Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA.

Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P < 5 × 10). Two novel loci were identified based on the 1DF test of interaction with educational attainment, while the remaining 16 loci were identified through the 2DF joint test of genetic and interaction effects. Ten novel loci were identified in individuals of African ancestry. Several novel loci show strong biological plausibility since they involve physiologic systems implicated in BP regulation. They include genes involved in the central nervous system-adrenal signaling axis (ZDHHC17, CADPS, PIK3C2G), vascular structure and function (GNB3, CDON), and renal function (HAS2 and HAS2-AS1, SLIT3). Collectively, these findings suggest a role of educational attainment or SES in further dissection of the genetic architecture of BP.
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http://dx.doi.org/10.1038/s41380-020-0719-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641978PMC
June 2021

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Genet Epidemiol 2020 09 29;44(6):629-641. Epub 2020 Mar 29.

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10 ) and 11 suggestive (p < 1 × 10 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.
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http://dx.doi.org/10.1002/gepi.22292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717887PMC
September 2020

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Nat Commun 2019 11 12;10(1):5121. Epub 2019 Nov 12.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
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http://dx.doi.org/10.1038/s41467-019-12958-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851116PMC
November 2019

Lacisediminimonas profundi gen. nov., sp. nov., a member of the family Oxalobacteraceae isolated from freshwater sediment.

Antonie Van Leeuwenhoek 2020 Feb 25;113(2):253-264. Epub 2019 Sep 25.

College of Biology and the Environment, Co-Innovation Centre for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210-037, China.

A novel Gram-stain-negative bacterial strain, CHu64-6-4, was isolated from a 67-cm-long sediment core collected from the Daechung Reservoir at a water depth of 17 m, Daejeon, Republic of Korea. The cells of strain CHu64-6-4 were aerobic nonmotile and formed colorless colonies on R2A agar. The phylogenetic analysis based on 16S rRNA gene sequencing indicated that the strain formed a separate lineage within the family Oxalobacteraceae, exhibiting 97.2% and 97.1% 16S rRNA gene sequence similarities to Glaciimonas singularis and Paraherbaspirillum soli, respectively. Strain CHu64-6-4 showed less than 74.4% average nucleotide identity compared to the type strains of related genera within the family Oxalobacteraceae. In the UPGMA dendrogram based on the ANI values of genomic sequences, strain CHu64-6-4 formed an evolutionary lineage independent of the genera Glaciimonas and some other taxa. The chemotaxonomic results showed Q-8 as the predominant respiratory ubiquinone, phosphatidylglycerol, diphosphatidylglycerol, and phosphatidylethnolamine as the major polar lipids, Summed Feature 3 (Cω7c and/or iso-C 2-OH), C, and Cω7c as the major fatty acids, and a DNA G+C content of 62.1 mol%. The combined genotypic and phenotypic data showed that strain CHu64-6-4 could be distinguished from all genera within the family Oxalobacteraceae and represents a novel genus, Lacisediminimonas profundi gen. nov., with the name Lacisediminimonas profundi sp. nov., in the family Oxalobacteraceae. The type strain is CHu64-6-4 (=KCTC 62287=JCM 32676).
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http://dx.doi.org/10.1007/s10482-019-01334-zDOI Listing
February 2020

A Multimodality Approach to Assessing Factor I Genetic Variants in Atypical Hemolytic Uremic Syndrome.

Kidney Int Rep 2019 Jul 9;4(7):1007-1017. Epub 2019 Apr 9.

Department of Medicine, Division of Rheumatology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.

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http://dx.doi.org/10.1016/j.ekir.2019.04.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609824PMC
July 2019

The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions.

Hum Hered 2018 5;83(6):315-332. Epub 2019 Jun 5.

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.

Background: Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci.

Objectives: This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification.

Method: For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error.

Results: In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power.

Conclusion: SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.
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http://dx.doi.org/10.1159/000499711DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662918PMC
October 2019

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure.

Hum Mol Genet 2019 08;28(15):2615-2633

Icelandic Heart Association, Kopavogur, Iceland.

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.
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http://dx.doi.org/10.1093/hmg/ddz070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644157PMC
August 2019

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Nat Commun 2019 01 22;10(1):376. Epub 2019 Jan 22.

Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.
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http://dx.doi.org/10.1038/s41467-018-08008-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342931PMC
January 2019

Smoking Interacts With CHRNA5, a Nicotinic Acetylcholine Receptor Subunit Gene, to Influence the Risk of IBD-Related Surgery.

Inflamm Bowel Dis 2018 04;24(5):1057-1064

Division of Gastroenterology, Washington University in St. Louis, St. Louis, Missouri.

Background: Inflammatory bowel disease (IBD) is a chronic luminal disease with genetic and environmental factors affecting phenotype. This study evaluated the relationship between CHRNA5, a nicotinic receptor subunit gene, and smoking in predicting IBD-related surgery as well as the relationship between CHRNA5 and nicotine dependence.

Methods: Participants completed a smoking questionnaire and were genotyped for CHRNA5 rs16969968. Demographic and clinical data were obtained from medical records. Wilcoxon, ANOVA, Chi square, and Fisher's exact tests were used for comparisons. Logistic regression was used to evaluate the effect of clinical and genetic predictors on surgery, stratified by disease subtype given paradoxical effects of smoking. Kaplan-Meier curves were used to examine the effect of smoking and genotype on time to surgery. (Significance: P < 0.05 for main effects; P < 0.2 for interaction terms).

Results: 400 (65.8%) patients had Crohn's disease (CD) and 208 (34.2%) had ulcerative colitis (UC). 298 (49%) underwent an IBD-related surgery. There was a trend towards significance between rs16969968 and smoking behavior (smoking status [P = 0.05], nicotine dependence [AA > AG > GG; P = 0.08]). Smoking and genotype were not independently associated with surgery in UC or CD. However, interaction between rs16969968 and smoking in predicting surgery was observed for both UC (OR = 2.72; P = 0.05) and CD (OR = 2.88; P = 0.1). CHRNA5 genotype, but not smoking, predicted time to surgery in patients with UC (P = 0.007) but not in patients with CD. The interaction between smoking and genotype was not significantly associated with time to surgery in UC or CD.

Conclusions: The CHRNA5 rs16969968 A variant interacts with smoking to influence IBD-related surgery. 10.1093/ibd/izx094_video1izx094.video15775248538001.
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http://dx.doi.org/10.1093/ibd/izx094DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994591PMC
April 2018

Genome-Wide Gene-Potassium Interaction Analyses on Blood Pressure: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity).

Circ Cardiovasc Genet 2017 Dec;10(6)

From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.).

Background: Gene-environmental interaction analysis can identify novel genetic factors for blood pressure (BP). We performed genome-wide analyses to identify genomic loci that interact with potassium to influence BP using single-marker (1 and 2 joint tests) and gene-based tests among Chinese participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity).

Methods And Results: Among 1876 GenSalt participants, the average of 3 urine samples was used to estimate potassium excretion. Nine BP measurements were taken using a random-zero sphygmomanometer. A total of 2.2 million single nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (<1.00×10) from GenSalt were evaluated for replication among 775 Chinese participants of the MESA (Multi-ethnic Study of Atherosclerosis). Single nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 tests identified interactions for rs16882447 on systolic BP (=2.83×10) and rs958929 on pulse pressure (=1.58×10). The 2 tests confirmed the rs16882447 signal for systolic BP (=1.15×10). Genome-wide gene-based analysis identified (=2.59×10) at 4p15.32 and (=4.49×10) at 9p22.2 for systolic BP, (=1.18×10), and (=1.36×10) at 6p21 for diastolic BP, (=1.05×10) at 1p32.2, and (=5.34×10) at 6q25.3 for pulse pressure. The (=3.57×10) gene was also significant for mean arterial pressure.

Conclusions: We identified 2 novel BP loci and 6 genes through the examination of single nucleotide polymorphism- and gene-based interactions with potassium.
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http://dx.doi.org/10.1161/CIRCGENETICS.117.001811DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728702PMC
December 2017

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Nat Commun 2017 04 26;8:14977. Epub 2017 Apr 26.

Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.
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http://dx.doi.org/10.1038/ncomms14977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414044PMC
April 2017

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape.

Nat Commun 2016 11 23;7:13357. Epub 2016 Nov 23.

Department of Kinesiology, Laval University, Québec, Québec, Canada G1V 0A6.

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
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http://dx.doi.org/10.1038/ncomms13357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114527PMC
November 2016

Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in Hispanics and African Americans.

PLoS One 2016 13;11(10):e0164132. Epub 2016 Oct 13.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Despite the substantial burden of hypertension in US minority populations, few genetic studies of blood pressure have been conducted in Hispanics and African Americans, and it is unclear whether many of the established loci identified in European-descent populations contribute to blood pressure variation in non-European descent populations. Using the Metabochip array, we sought to characterize the genetic architecture of previously identified blood pressure loci, and identify novel cardiometabolic variants related to systolic and diastolic blood pressure in a multi-ethnic US population including Hispanics (n = 19,706) and African Americans (n = 18,744). Several known blood pressure loci replicated in African Americans and Hispanics. Fourteen variants in three loci (KCNK3, FGF5, ATXN2-SH2B3) were significantly associated with blood pressure in Hispanics. The most significant diastolic blood pressure variant identified in our analysis, rs2586886/KCNK3 (P = 5.2 x 10-9), also replicated in independent Hispanic and European-descent samples. African American and trans-ethnic meta-analysis data identified novel variants in the FGF5, ULK4 and HOXA-EVX1 loci, which have not been previously associated with blood pressure traits. Our identification and independent replication of variants in KCNK3, a gene implicated in primary hyperaldosteronism, as well as a variant in HOTTIP (HOXA-EVX1) suggest that further work to clarify the roles of these genes may be warranted. Overall, our findings suggest that loci identified in European descent populations also contribute to blood pressure variation in diverse populations including Hispanics and African Americans-populations that are understudied for hypertension genetic risk factors.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164132PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5063457PMC
May 2017

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group.

Genet Epidemiol 2016 07 27;40(5):404-15. Epub 2016 May 27.

Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom.

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.
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http://dx.doi.org/10.1002/gepi.21978DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911246PMC
July 2016

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk.

Nat Commun 2016 Feb 1;7:10495. Epub 2016 Feb 1.

Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10(-8)), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.
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http://dx.doi.org/10.1038/ncomms10495DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740398PMC
February 2016

Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels.

Nat Commun 2016 Feb 1;7:10494. Epub 2016 Feb 1.

Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachussetts 02215, USA.

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P<10(-6) in 19,979 additional individuals. We identify five loci robustly associated (P<5 × 10(-8)) with leptin levels in/near LEP, SLC32A1, GCKR, CCNL1 and FTO. Although the association of the FTO obesity locus with leptin levels is abolished by adjustment for BMI, associations of the four other loci are independent of adiposity. The GCKR locus was found associated with multiple metabolic traits in previous GWAS and the CCNL1 locus with birth weight. Knockdown experiments in mouse adipose tissue explants show convincing evidence for adipogenin, a regulator of adipocyte differentiation, as the novel causal gene in the SLC32A1 locus influencing leptin levels. Our findings provide novel insights into the regulation of leptin production by adipose tissue and open new avenues for examining the influence of variation in leptin levels on adiposity and metabolic health.
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http://dx.doi.org/10.1038/ncomms10494DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740377PMC
February 2016

Three Approaches to Modeling Gene-Environment Interactions in Longitudinal Family Data: Gene-Smoking Interactions in Blood Pressure.

Genet Epidemiol 2016 Jan 1;40(1):73-80. Epub 2015 Dec 1.

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America.

Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene-smoking interactions have detected novel BP loci in cross-sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene-smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree-based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single-visit analyses. The pedigree-based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene-smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data.
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http://dx.doi.org/10.1002/gepi.21941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867544PMC
January 2016

Genomic and transcriptomic predictors of triglyceride response to regular exercise.

Br J Sports Med 2015 Dec 21;49(23):1524-31. Epub 2015 Oct 21.

Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.

Aim: We performed genome-wide and transcriptome-wide profiling to identify genes and single nucleotide polymorphisms (SNPs) associated with the response of triglycerides (TG) to exercise training.

Methods: Plasma TG levels were measured before and after a 20-week endurance training programme in 478 white participants from the HERITAGE Family Study. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. Affymetrix HG-U133+2 arrays were used to quantitate gene expression levels from baseline muscle biopsies of a subset of participants (N=52). Genome-wide association study (GWAS) analysis was performed using MERLIN, while transcriptomic predictor models were developed using the R-package GALGO.

Results: The GWAS results showed that eight SNPs were associated with TG training-response (ΔTG) at p<9.9×10(-6), while another 31 SNPs showed p values <1×10(-4). In multivariate regression models, the top 10 SNPs explained 32.0% of the variance in ΔTG, while conditional heritability analysis showed that four SNPs statistically accounted for all of the heritability of ΔTG. A molecular signature based on the baseline expression of 11 genes predicted 27% of ΔTG in HERITAGE, which was validated in an independent study. A composite SNP score based on the top four SNPs, each from the genomic and transcriptomic analyses, was the strongest predictor of ΔTG (R(2)=0.14, p=3.0×10(-68)).

Conclusions: Our results indicate that skeletal muscle transcript abundance at 11 genes and SNPs at a number of loci contribute to TG response to exercise training. Combining data from genomics and transcriptomics analyses identified a SNP-based gene signature that should be further tested in independent samples.
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http://dx.doi.org/10.1136/bjsports-2015-095179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672639PMC
December 2015

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

PLoS Genet 2015 Oct 1;11(10):e1005378. Epub 2015 Oct 1.

HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, United States of America.

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.
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http://dx.doi.org/10.1371/journal.pgen.1005378DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4591371PMC
October 2015

The medical diagnostic approaches with phylogenetic analysis for rare Brucella spp. diagnosis in Taiwan.

Biomedicine (Taipei) 2015 Jun 6;5(2). Epub 2015 Jun 6.

Department of Laboratory Medicine, China Medical University Hospital, 404, Taichung, Taiwan.

Brucellosis is a bacterial zoonotic disease which can be easy to misdiagnose in clinical microbiology laboratories. In the present study, we have tried to improve the current clinical method for detecting Brucella spp. and its antibiotic characteristics. Our method begins with detecting the clinical isolate through traditional biochemical methods and automatic identification systems. Then, we move on to editing the sequence for BLAST allows us to compare 16s rRNA sequences with sequences from other species, allowing the gene level to be determined. Next, the phylogenetic analysis of multiple genetic loci is able to determine the evolutionary relationships between our bacteria strain and those from other locations. Finally, an anti-microbial susceptibility test hones in on the level of antibacterial activity that the bacteria displays. Employing these four steps in concert is extremely effective in identifying rare bacteria. Thus, when attempting to determine the identity of rare bacteria such as Brucella, utilizing these four steps from our research should be highly effective and ultimately prevent further identification errors and misdiagnoses. The standards we have suggested to identify rare bacteria strains is applicable not only to Brucella, but also to other rarely encountered bacteria.
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http://dx.doi.org/10.7603/s40681-015-0009-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4502044PMC
June 2015

Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions.

Genet Epidemiol 2015 Sep 3;39(6):480-488. Epub 2015 May 3.

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.

Background: Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific variants. Interactions between genes and BP-associated factors may explain some "missing heritability." Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown.

Methods: We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers ("ever smokers," N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations.

Results: Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD > 10) but decreased SBP (7 mmHg) in light smokers (CPD ≤ 10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached P < 1 × 10(-6).

Discussion: Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking.
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http://dx.doi.org/10.1002/gepi.21904DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4543586PMC
September 2015

PCSK9 variation and association with blood pressure in African Americans: preliminary findings from the HyperGEN and REGARDS studies.

Front Genet 2015 8;6:136. Epub 2015 Apr 8.

Department of Epidemiology, University of Alabama at Birmingham Birmingham, AL, USA.

Proprotein convertase subtilisin/kexin type 9 (encoded by PCSK9) plays a well-known role in the regulation of low-density lipoprotein (LDL) receptors, and an inhibitor of this enzyme is a promising new therapeutic for hyperlipidemia. Recently, animal and human studies also implicate PCSK9 genetic variation in the regulation of blood pressure. The goal of this study was to examine if common and rare polymorphisms in PCSK9 are associated with blood pressure in an African-American population at high risk for cardiovascular disease. Using genomic data assayed on the Affymetrix 6.0 array (n = 1199) and the Illumina HumanExome Beadchip (n = 1966) from the Hypertension Genetic Epidemiology Network (HyperGEN), we tested the association of PCSK9 polymorphisms with blood pressure. We used linear mixed models and the sequence kernel association test (SKAT) to assess the association of 31 common and 19 rare variants with blood pressure. The models were adjusted for age, sex, center, smoking status, principal components for ancestry and diabetes as fixed effects and family as a random effect. The results showed a marginally significant effect of two genome-wide association study (GWAS) single-nucleotide polymorphisms (SNPs) (rs12048828: β = 1.8, P = 0.05 and rs9730100: β = 1.0, P = 0.05) with diastolic blood pressure (DBP); however these results were not significant after correction for multiple testing. Rare variants were cumulatively associated with DBP (P = 0.04), an effect that was strengthened by restriction to non-synonymous or stop-gain SNPs (P = 0.02). While gene-based results for DBP did not replicate (P = 0.36), we found an association with SBP (P = 0.04) in the Reasons for Geographic And Racial Differences in Stroke study (REGARDS). The findings here suggest rare variants in PCSK9 may influence blood pressure among African Americans, laying the ground work for further validation studies.
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http://dx.doi.org/10.3389/fgene.2015.00136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4389541PMC
April 2015

The role of rare variants in systolic blood pressure: analysis of ExomeChip data in HyperGEN African Americans.

Hum Hered 2015 ;79(1):20-7

Cardiovascular diseases are among the most significant health problems in the United States today, with their major risk factor, hypertension, disproportionately affecting African Americans (AAs). Although GWAS have identified dozens of common variants associated with blood pressure (BP) and hypertension in European Americans, these variants collectively explain <2.5% of BP variance, and most of the genetic variants remain yet to be identified. Here, we report the results from rare-variant analysis of systolic BP using 94,595 rare and low-frequency variants (minor allele frequency, MAF, <5%) from the Illumina exome array genotyped in 2,045 HyperGEN AAs. In addition to single-variant analysis, 4 gene-level association tests were used for analysis: burden and family-based SKAT tests using MAF cutoffs of 1 and 5%. The gene-based methods often provided lower p values than the single-variant approach. Some consistency was observed across these 4 gene-based analysis options. While neither the gene-based analyses nor the single-variant analysis produced genome-wide significant results, the top signals, which had supporting evidence from multiple gene-based methods, were of borderline significance. Though additional molecular validations are required, 6 of the 16 most promising genes are biologically plausible with physiological connections to BP regulation.
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http://dx.doi.org/10.1159/000375373DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374048PMC
September 2015
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