An exome-wide sequencing study of lipid response to high-fat meal and fenofibrate in Caucasians from the GOLDN cohort
2018; Elsevier BV; Volume: 59; Issue: 4 Linguagem: Inglês
10.1194/jlr.p080333
ISSN1539-7262
AutoresPeng Geng, Marguerite R. Irvin, Bertha Hidalgo, Stella Aslibekyan, Vinodh Srinivasasainagendra, Ping An, Alexis C. Wood, Hemant K. Tiwari, Tushar Dave, Kathleen A. Ryan, José M. Ordovás, Robert J. Straka, Mary F. Feitosa, Paul N. Hopkins, Ingrid B. Borecki, Michael A. Province, Braxton D. Mitchell, Donna K. Arnett, Degui Zhi,
Tópico(s)Diet and metabolism studies
ResumoOur understanding of genetic influences on the response of lipids to specific interventions is limited. In this study, we sought to elucidate effects of rare genetic variants on lipid response to a high-fat meal challenge and fenofibrate (FFB) therapy in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) cohort using an exome-wide sequencing-based association study. Our results showed that the rare coding variants in ITGA7, SIPA1L2, and CEP72 are significantly associated with fasting LDL cholesterol response to FFB (P = 1.24E-07), triglyceride postprandial area under the increase (AUI) (P = 2.31E-06), and triglyceride postprandial AUI response to FFB (P = 1.88E-06), respectively. We sought to replicate the association for SIPA1L2 in the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. The associated rare variants in GOLDN were not observed in the HAPI Heart study, and thus the gene-based result was not replicated. For functional validation, we found that gene transcript level of SIPA1L2 is associated with triglyceride postprandial AUI (P < 0.05) in GOLDN. Our study suggests unique genetic mechanisms contributing to the lipid response to the high-fat meal challenge and FFB therapy. Our understanding of genetic influences on the response of lipids to specific interventions is limited. In this study, we sought to elucidate effects of rare genetic variants on lipid response to a high-fat meal challenge and fenofibrate (FFB) therapy in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) cohort using an exome-wide sequencing-based association study. Our results showed that the rare coding variants in ITGA7, SIPA1L2, and CEP72 are significantly associated with fasting LDL cholesterol response to FFB (P = 1.24E-07), triglyceride postprandial area under the increase (AUI) (P = 2.31E-06), and triglyceride postprandial AUI response to FFB (P = 1.88E-06), respectively. We sought to replicate the association for SIPA1L2 in the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. The associated rare variants in GOLDN were not observed in the HAPI Heart study, and thus the gene-based result was not replicated. For functional validation, we found that gene transcript level of SIPA1L2 is associated with triglyceride postprandial AUI (P < 0.05) in GOLDN. Our study suggests unique genetic mechanisms contributing to the lipid response to the high-fat meal challenge and FFB therapy. Dyslipidemia, defined as abnormal levels of lipids and/or lipoproteins in the blood (1.Fodor G. Primary prevention of CVD: treating dyslipidemia.BMJ Clin. Evid. 2010; 2010: 0215PubMed Google Scholar), is a critical modifiable risk factor for chronic diseases, accounting for almost half of the population attributable risk for adverse cardiovascular events (2.Arsenault B.J. Boekholdt S.M. Kastelein J.J. Lipid parameters for measuring risk of cardiovascular disease.Nat. Rev. Cardiol. 2011; 8: 197-206Crossref PubMed Scopus (146) Google Scholar). Circulating lipid levels are influenced by both environment (e.g., diet, smoking, and prescription drugs) and genetic variation; twin studies estimate the genetic contribution to explain ∼60% of phenotypic variation (3.Beekman M. Heijmans B.T. Martin N.G. Pedersen N.L. Whitfield J.B. DeFaire U. van Baal G.C.M. Snieder H. Vogler G.P. Slagboom P.E. Heritabilities of apolipoprotein and lipid levels in three countries.Twin Res. 2002; 5: 87-97Crossref PubMed Scopus (122) Google Scholar). Until recently (4.Willer C.J. Schmidt E.M. Sengupta S. Peloso G.M. Gustafsson S. Kanoni S. Ganna A. Chen J. Buchkovich M.L. Mora S. et al.Discovery and refinement of loci associated with lipid levels.Nat. Genet. 2013; 45: 1274-1283Crossref PubMed Scopus (1875) Google Scholar), findings from genome-wide association studies have accounted for about 12% of blood lipid variance. The proportion of unexplained variance (∼48%) could be lessened with the inclusion of rare variant analyses, illustrating the important role of the low-frequency polymorphisms in the genetic architecture of lipid traits (5.Surakka I. Horikoshi M. Mägi R. Sarin A-P. Mahajan A. Lagou V. Marullo L. Ferreira T. Miraglio B. Timonen S. The impact of low-frequency and rare variants on lipid levels.Nat. Genet. 2015; 47: 589-597Crossref PubMed Scopus (226) Google Scholar). These rare variants, which were not covered by previous genome-wide association studies but contribute to lipid traits, could be located in coding regions, as well as introns, untranslated regions, and intergene regions. To date, such high-resolution genetic investigations of lipids have focused only on fasting phenotypes, while knowledge of genetic determinants of lipid response to interventions remains limited. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study cohort provides a unique opportunity to study the effects of rare genetic variants on response to two interventions, a high-fat meal (postprandial lipemia; PPL) and fenofibrate (FFB) treatment, due to its carefully controlled intervention by standardizing the environmental perturbation. The high-fat meal intervention provides a unique and impactful way to study dyslipidemia in a dynamic test, not only because humans spend most of their waking hours in the postprandial state, but also because of the high fat content of Western diets. Furthermore, an elevated postprandial lipid response, followed by delayed clearance, has been shown to predict future risk of cardiovascular disease (6.Borén J. Matikainen N. Adiels M. Taskinen M-R. Postprandial hypertriglyceridemia as a coronary risk factor.Clin. Chim. Acta. 2014; 431: 131-142Crossref PubMed Scopus (150) Google Scholar). Additionally, the second GOLDN intervention(the 3 week treatment with micronized FFB)creates an opportunity for pharmacogenomic discovery and a deeper understanding of individual variation to lipid-lowering drugs (7.Glasser S.P. Wojczynski M.K. Kabagambe E.K. Tsai M.Y. Ordovas J.M. Straka R.J. Arnett D.K. Comparison of postprandial responses to a high-fat meal in hypertriglyceridemic men and women before and after treatment with fenofibrate in the Genetics and Lipid Lowering Drugs and Diet Network (GOLDN) Study.Srx Pharmacol. 2010; 2010: 485146Crossref PubMed Google Scholar). Prior studies in GOLDN have scanned common variants to identify multiple promising determinants of response to both interventions (8.Irvin M.R. Rotroff D.M. Aslibekyan S. Zhi D. Hidalgo B. Motsinger-Reif A. Marvel S. Srinivasasainagendra V. Claas S.A. Buse J.B. A genome-wide study of lipid response to fenofibrate in Caucasians: a combined analysis of the GOLDN and ACCORD studies.Pharmacogenet. Genomics. 2016; 26: 324-333Crossref PubMed Scopus (11) Google Scholar, 9.Aslibekyan S. Goodarzi M.O. Frazier-Wood A.C. Yan X. Irvin M.R. Kim E. Tiwari H.K. Guo X. Straka R.J. Taylor K.D. Variants identified in a GWAS meta-analysis for blood lipids are associated with the lipid response to fenofibrate.PLoS One. 2012; 7: e48663Crossref PubMed Scopus (32) Google Scholar, 10.Wojczynski M.K. Parnell L.D. Pollin T.I. Lai C.Q. Feitosa M.F. O'Connell J.R. Frazier-Wood A.C. Gibson Q. Aslibekyan S. Ryan K.A. Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).Metabolism. 2015; 64: 1359-1371Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar); however, they were conducted prior to the availability of high-resolution genomic data. To augment the discoveries from the Genome-Wide Association Study (GWAS), account for further missing heritability, and identify additional functional loci contributing to variation in lipid response to a high-fat meal and treatment with FFB, we performed an exome-wide sequencing study in 894 European Americans from the GOLDN study. We sought to replicate the PPL result using the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. In addition, we sought to validate the associations for our findings using DNA methylation and RNA-sequencing (RNA-Seq) data previously collected in GOLDN. GOLDN (clinicaltrials.gov NCT00083369) was designed to characterize genetic factors that determine response of lipids to two environmental interventions: a 3 week FFB treatment and a high-fat meal challenge (8.Irvin M.R. Rotroff D.M. Aslibekyan S. Zhi D. Hidalgo B. Motsinger-Reif A. Marvel S. Srinivasasainagendra V. Claas S.A. Buse J.B. A genome-wide study of lipid response to fenofibrate in Caucasians: a combined analysis of the GOLDN and ACCORD studies.Pharmacogenet. Genomics. 2016; 26: 324-333Crossref PubMed Scopus (11) Google Scholar, 9.Aslibekyan S. Goodarzi M.O. Frazier-Wood A.C. Yan X. Irvin M.R. Kim E. Tiwari H.K. Guo X. Straka R.J. Taylor K.D. Variants identified in a GWAS meta-analysis for blood lipids are associated with the lipid response to fenofibrate.PLoS One. 2012; 7: e48663Crossref PubMed Scopus (32) Google Scholar, 11.Irvin M.R. Kabagambe E.K. Tiwari H.K. Parnell L.D. Straka R.J. Tsai M. Ordovas J.M. Arnett D.K. Apolipoprotein E polymorphisms and postprandial triglyceridemia before and after fenofibrate treatment in the Genetics of Lipid Lowering and Diet Network (GOLDN) Study.Circ Cardiovasc Genet. 2010; 3: 462-467Crossref PubMed Scopus (35) Google Scholar). Only Caucasian families with at least two siblings were included. Participants were asked to discontinue any lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit. GOLDN recruited and sequenced 894 subjects from 186 families recruited at two centers (Minneapolis, MN, and Salt Lake City, UT). Of the 894 subjects, 810 participants participated in the high-fat meal intervention. After that, 797 GOLDN participants received daily treatment with 160 mg micronized FFB for 3 weeks and were followed for treatment response. Finally, 715 participants participated in another high-fat meal intervention to investigate lipid response to the high-fat meal while treated with FFB. The population size indicates that we have statistical power ranging from 0.5 for h2locus= 0.02 to 1.00 for h2locus= 0.05 or above. Our study has been approved by the committee for the protection of human subjects in the University of Texas Health Science Center at Houston and the other institutions, and it abides by the Declaration of Helsinki principles. Informed consent was obtained from the human subjects. Clinical lipids including HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), and triglycerides (TGs) were measured in this study. TG was measured by using the glycerol-blanked enzymatic method on the Roche COBAS FARA centrifugal analyzer (Roche Diagnostics Corporation) (10.Wojczynski M.K. Parnell L.D. Pollin T.I. Lai C.Q. Feitosa M.F. O'Connell J.R. Frazier-Wood A.C. Gibson Q. Aslibekyan S. Ryan K.A. Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).Metabolism. 2015; 64: 1359-1371Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar). HDL-C was measured by using the same procedure as TG after precipitation of non-HDL cholesterol with magnesium/dextran. LDL-C was measured by using a homogeneous direct method (LDL Direct Liquid Select™ Cholesterol Reagent; Equal Diagnostics) on a Hitachi 911 Automatic Analyzer. The FFB treatment and the high-fat meal intervention in GOLDN have been described previously (10.Wojczynski M.K. Parnell L.D. Pollin T.I. Lai C.Q. Feitosa M.F. O'Connell J.R. Frazier-Wood A.C. Gibson Q. Aslibekyan S. Ryan K.A. Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).Metabolism. 2015; 64: 1359-1371Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 12.Liu Y. Ordovas J.M. Gao G. Province M. Straka R.J. Tsai M.Y. Lai C-Q. Zhang K. Borecki I. Hixson J.E. The SCARB1 gene is associated with lipid response to dietary and pharmacological interventions.J. Hum. Genet. 2008; 53: 709-717Crossref PubMed Scopus (26) Google Scholar). During the high-fat meal intervention, blood was collected three times for pre- and post-FFB treatment, respectively: draw 1 (fasting) immediately prior to the meal (0 h), draw 2 approximately 3.5 h after the meal, and draw 3 approximately 6 h after the meal. We excluded 14 individuals in pre-FFB and 20 individuals in post-FFB from our postprandial analyses who did not follow the protocol schedule, which resulted in large drawtime deviations (>1 h). HDL- and LDL-C were only assessed at draw 1 both before and after FFB treatment. TG was assessed at each blood draw, which allowed an analysis of response to the high-fat meal and FFB, and change in high-fat meal response with FFB. Genomic DNA from peripheral blood nucleated cells was extracted by using QIAmp 96 DNA Blood Kits (Qiagen, Hilden, Germany). The integrity and yield of native genomic DNA was verified by a PicoGreen assay for quantitation (Invitrogen, Life Technologies, Carlsbad, CA) and run on a 0.8% agarose gel for quality control (QC). Illumina paired-end small-fragment libraries were constructed according to the manufacturer's recommendations (Illumina Inc., San Diego, CA) with the following exceptions: 1) 500–1,000 ng of native genomic DNA was fragmented by using the Covaris E220 DNA Sonicator (Covaris, Inc., Woburn, MA) to a size range between 100 and 400 bp; 2) Illumina adaptor-ligated library fragments were amplified in four 50 µl PCR reactions for 18 cycles; and 3) solid-phase reversible immobilization bead clean-up was used for enzymatic purification throughout the library process, as well as for final library size selection targeting 300 to 500 bp fragments. Libraries were pooled precapture and hybridized to NimbleGen SeqCap EZ Human VCRome Library kits (Roche NimbleGen, Madison, WI) according to the manufacturer's protocol. The concentration of each captured library was determined through KAPA quantitative PCR (Kapa Biosystems, Inc., Woburn, MA) according to the manufacturer's protocol to produce cluster counts appropriate for the Illumina HiSeq 2000 platform. The libraries were run on a HiSeq 2000 V3 2×101-bp sequencing run according to manufacturer recommendations. Illumina sequencing data in FASTQ format were aligned to the GRCh37-lite reference sequence by using BWA (13.Li H. Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform.Bioinformatics. 2010; 26: 589-595Crossref PubMed Scopus (7013) Google Scholar) (version 0.5.9) with the parameters: -t 4 -q 5. Duplicates were marked in each Binary Alignment/Map (BAM) file by using Picard (http://broadinstitute.github.io/picard/) (version 1.46). If a sample was sequenced across multiple lanes, the aligned BAM files were merged by using Picard (version 1.46). Each BAM file was sorted with potential PCR duplications removed by using SAMtools (version 1.2.1) (14.Li H. Handsaker B. Wysoker A. Fennell T. Ruan J. Homer N. Marth G. Abecasis G. Durbin R. The sequence alignment/map format and SAMtools.Bioinformatics. 2009; 25: 2078-2079Crossref PubMed Scopus (31547) Google Scholar, 15.Danecek P. Auton A. Abecasis G. Albers C.A. Banks E. DePristo M.A. Handsaker R.E. Lunter G. Marth G.T. Sherry S.T. The variant call format and VCFtools.Bioinformatics. 2011; 27: 2156-2158Crossref PubMed Scopus (6614) Google Scholar). Subject-level single nucleotide variants (SNVs) were called by using the Atlas-SNP2 application with a variant call file (VCF) created for each subject (16.Challis D. Yu J. Evani U.S. Jackson A.R. Paithankar S. Coarfa C. Milosavljevic A. Gibbs R.A. Yu F. An integrative variant analysis suite for whole exome next-generation sequencing data.BMC Bioinformatics. 2012; 13: 8Crossref PubMed Scopus (198) Google Scholar). Furthermore, for each chromosome, a population-level VCF was created by using the SAMtools mpileup/BCFtools (13.Li H. Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform.Bioinformatics. 2010; 26: 589-595Crossref PubMed Scopus (7013) Google Scholar, 14.Li H. Handsaker B. Wysoker A. Fennell T. Ruan J. Homer N. Marth G. Abecasis G. Durbin R. The sequence alignment/map format and SAMtools.Bioinformatics. 2009; 25: 2078-2079Crossref PubMed Scopus (31547) Google Scholar), where the SNVs at all the discovered sites were called and backfilled in multiple samples by using the original BAMs from all the GOLDN subjects. Finally, a combined project-level VCF was created through merging all the 25 population-level VCF files (chromosomes 1–22, X, Y, and mitochondrial) by using the GATK-CatVariants (version 3.5) (17.McKenna A. Hanna M. Banks E. Sivachenko A. Cibulskis K. Kernytsky A. Garimella K. Altshuler D. Gabriel S. Daly M. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (14770) Google Scholar, 18.DePristo M.A. Banks E. Poplin R. Garimella K.V. Maguire J.R. Hartl C. Philippakis A.A. Del Angel G. Rivas M.A. Hanna M. A framework for variation discovery and genotyping using next-generation DNA sequencing data.Nat. Genet. 2011; 43: 491-498Crossref PubMed Scopus (7098) Google Scholar, 19.Auwera G.A. Carneiro M.O. Hartl C. Poplin R. del Angel G. Levy-Moonshine A. Jordan T. Shakir K. Roazen D. Thibault J. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline.Curr. Protoc. Bioinformatics. 2013; 11: 11.10.1-11.10.33Google Scholar). Only biallelic mutations were kept after filtered by using VCFtools (15.Danecek P. Auton A. Abecasis G. Albers C.A. Banks E. DePristo M.A. Handsaker R.E. Lunter G. Marth G.T. Sherry S.T. The variant call format and VCFtools.Bioinformatics. 2011; 27: 2156-2158Crossref PubMed Scopus (6614) Google Scholar). For the genotype-level QC, genotypes with read depth <20 or genotyping quality 5%. The project-level VCF was further annotated by using ANNOVAR (20.Wang K. Li M. Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.Nucleic Acids Res. 2010; 38: e164Crossref PubMed Scopus (7869) Google Scholar) according to hg19 genome assembly/dbSNP (version 138). Four classes of functional variants (splicing, nonsynonymous, stop-loss, and stop-gain) on chromosomes 1–22 were used for association tests (21.Lange L.A. Hu Y. Zhang H. Xue C. Schmidt E.M. Tang Z-Z. Bizon C. Lange E.M. Smith J.D. Turner E.H. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.Am. J. Hum. Genet. 2014; 94: 233-245Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar). Target region breadth by depth for each sample was evaluated by using RefCov (http://gmt.genome.wustl.edu/packages/refcov/) v0.3 with a Browser Extensible Data file of target regions provided by the exome kit manufacturer. We required that >70% of target bases were covered at >20x; samples below that threshold received additional (top-up) sequencing. To confirm sample purity and identity, we compared high-density SNP array genotypes (9.Aslibekyan S. Goodarzi M.O. Frazier-Wood A.C. Yan X. Irvin M.R. Kim E. Tiwari H.K. Guo X. Straka R.J. Taylor K.D. Variants identified in a GWAS meta-analysis for blood lipids are associated with the lipid response to fenofibrate.PLoS One. 2012; 7: e48663Crossref PubMed Scopus (32) Google Scholar) (Illumina OmniExpress) to the SNV calls obtained from the sequencing data by using SAMtools (15.Danecek P. Auton A. Abecasis G. Albers C.A. Banks E. DePristo M.A. Handsaker R.E. Lunter G. Marth G.T. Sherry S.T. The variant call format and VCFtools.Bioinformatics. 2011; 27: 2156-2158Crossref PubMed Scopus (6614) Google Scholar) version r963, and required >90% genotype concordance to pass a sample. No samples were excluded due to low concordance. All the lipid values were natural log-transformed to achieve normality of residuals. Baseline was defined as the value at 0 h (i.e., fasting) at the pre-FFB visit. Fasting-level response to FFB was defined as the difference of log-transformed lipid plasma concentrations at draw 1 between post- and pre-FFB. Three TG pre-FFB postprandial phenotypes were assessed, including uptake, clearance, and area under the increase (AUI). TG values at each blood draw were natural log-transformed first. Uptake and clearance were defined as the slope of the line of TG response from draw 1 to draw 2 and from draw 2 to draw 3, respectively. Next, AUI was calculated with the transformed value by using the trapezoid rule. Because draw time deviations from protocol could result in procedural errors in AUI measurement, TG values at 6 h were estimated by using the values and draw times of draws 2 and 3 by linear extrapolation. AUI was calculated with the measured values at draws 1 and 2, and the estimated value at 6 h to mitigate the effect caused by draw time deviation. The postprandial phenotype response to FFB were the change of PPL phenotype before and after FFB treatment. These phenotypes included uptake response to FFB, clearance response to FFB, and AUI response to FFB. Genetic associations were assessed by using linear mixed models using RAREMETALWORKER and RAREMETAL (version 4.13.6) (22.Feng S. Liu D. Zhan X. Wing M.K. Abecasis G.R. RAREMETAL: fast and powerful meta-analysis for rare variants.Bioinformatics. 2014; 30: 2828-2829Crossref PubMed Scopus (75) Google Scholar, 23.Liu D.J. Peloso G.M. Zhan X. Holmen O.L. Zawistowski M. Feng S. Nikpay M. Auer P.L. Goel A. Zhang H. Meta-analysis of gene-level tests for rare variant association.Nat. Genet. 2014; 46: 200-204Crossref PubMed Scopus (132) Google Scholar). We considered a minimal model and a fully adjusted model. In the minimal model, all the associations were adjusted for sex, age, age2, age3, and recruiting center as fixed effects (24.Irvin M.R. Zhi D. Joehanes R. Mendelson M. Aslibekyan S. Claas S.A. Thibeault K.S. Patel N. Day K. Jones L.W. Epigenome-wide association study of fasting blood lipids in the Genetics of Lipid Lowering Drugs and Diet Network study.Circulation. 2014; 130: 565-572Crossref PubMed Scopus (158) Google Scholar, 25.Frazier-Wood A.C. Ordovas J. Straka R. Hixson J. Borecki I. Tiwari H. Arnett D. The PPAR alpha gene is associated with triglyceride, low-density cholesterol and inflammation marker response to fenofibrate intervention: the GOLDN study.Pharmacogenomics J. 2013; 13: 312-317Crossref PubMed Scopus (29) Google Scholar). For the FFB analyses, an additional variable measuring the number of pills taken per day (to adjust for compliance) was included as a covariate. In addition, a kinship coefficient considered as a random effect was used to adjust for family relatedness. In the full model, apart from those covariates included in minimal models, additional related lipid levels were included as covariates. For the fasting-level response to FFB and pre-FFB postprandial AUI and uptake, respective baseline levels were included as covariates. For the pre-FFB postprandial clearance phenotype, draw 2 lipid level was used as a covariate. For the three postprandial lipid level response to FFB, their corresponding pre-FFB treatment level and fasting-level response to FFB were included as covariates (supplemental Table S1). For gene-based analyses, sequence kernel association test (SKAT), simple burden test, Madsen and Browning weighted burden test (MB), and variable threshold test were utilized with multiple frequency thresholds for variant inclusion (MAF < 5% and 1%) (26.Wu M.C. Lee S. Cai T. Li Y. Boehnke M. Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test.Am. J. Hum. Genet. 2011; 89: 82-93Abstract Full Text Full Text PDF PubMed Scopus (1573) Google Scholar, 27.Price A.L. Kryukov G.V. de Bakker P.I. Purcell S.M. Staples J. Wei L-J. Sunyaev S.R. Pooled association tests for rare variants in exon-resequencing studies.Am. J. Hum. Genet. 2010; 86: 832-838Abstract Full Text Full Text PDF PubMed Scopus (597) Google Scholar, 28.Madsen B.E. Browning S.R. A groupwise association test for rare mutations using a weighted sum statistic.PLoS Genet. 2009; 5: e1000384Crossref PubMed Scopus (823) Google Scholar). Bonferroni corrections were used for both single-variant and gene-based associations. The significant signals were filtered out if they were driven by variants carried by less than three individuals or one single family. We sought to replicate our associations for TG postprandial phenotypes using the HAPI Heart Study (29.Mitchell B.D. McArdle P.F. Shen H. Rampersaud E. Pollin T.I. Bielak L.F. Jaquish C. Douglas J.A. Roy-Gagnon M-H. Sack P. The genetic response to short-term interventions affecting cardiovascular function: rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study.Am. Heart J. 2008; 155: 823-828Crossref PubMed Scopus (90) Google Scholar), in which postprandial TG levels were measured at 0, 1, 2, 3, 4, and 6 h after 770 Old Order Amish participants underwent a high-fat feeding intervention identical to the one used in GOLDN. More study procedure details can be found in previous reports (10.Wojczynski M.K. Parnell L.D. Pollin T.I. Lai C.Q. Feitosa M.F. O'Connell J.R. Frazier-Wood A.C. Gibson Q. Aslibekyan S. Ryan K.A. Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).Metabolism. 2015; 64: 1359-1371Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 29.Mitchell B.D. McArdle P.F. Shen H. Rampersaud E. Pollin T.I. Bielak L.F. Jaquish C. Douglas J.A. Roy-Gagnon M-H. Sack P. The genetic response to short-term interventions affecting cardiovascular function: rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study.Am. Heart J. 2008; 155: 823-828Crossref PubMed Scopus (90) Google Scholar). HAPI Heart Study participants were genotyped as part of the Trans-Omics for Precision Medicine effort using whole-genome sequencing methods. TG postprandial phenotypes were defined in the HAPI Heart Study similarly as described above in GOLDN but calculated with measured values at 0, 3, and 6 h. Demographic and clinical characteristics of the HAPI Heart Study are listed in supplemental Table S2. The association of GOLDN top hits in the replication cohort was tested by using RAREMETALWORKER with an identical model to GOLDN. Next, we also performed a joint meta-analysis of GOLDN top hits across all participating cohorts using RAREMETAL. We sought to validate the associations for our findings using DNA methylation and RNA-Seq data previously collected in GOLDN. CpG site methylation was quantified by using the Illumina (San Diego, CA) Infinium Human Methylation450 Beadchip with 991 participants as described previously (24.Irvin M.R. Zhi D. Joehanes R. Mendelson M. Aslibekyan S. Claas S.A. Thibeault K.S. Patel N. Day K. Jones L.W. Epigenome-wide association study of fasting blood lipids in the Genetics of Lipid Lowering Drugs and Diet Network study.Circulation. 2014; 130: 565-572Crossref PubMed Scopus (158) Google Scholar). The CpG sites within the genes containing significantly associated variants and the intergenic CpG sites near these genes were examined to test whether their methylation levels were associated with lipid levels or not by using linear mixed models. For transcriptional profiling, 100 unrelated GOLDN participants were selected from the extremes of the BMI distribution. RNA was extracted from buffy coats by using the TRIzol method (ThermoFisher Scientific, Waltham, MA), and the quality was evaluated by using Bioanalyzer (Agilent Technologies, Santa Clara, CA). After sequencing and alignment, we fitted linear mixed models to test for associations between gene transcript level and lipid phenotypes. Demographic and clinical characteristics of the study subjects are listed in Table 1 and supplemental Table S2. From 186 families, we included 435 males and 459 females from the GOLDN study. Levels of TG, LDL-C, and HDL-C after FFB treatment were significantly different from pretreatment levels (P ≤ 0.05) (Table 1). As described before (25.Frazier-Wood A.C. Ordovas J. Straka R. Hixson J. Borecki I. Tiwari H. Arnett D. The PPAR alpha gene is associated with triglyceride, low-density cholesterol and inflammation marker response to fenofibrate intervention: the GOLDN study.Pharmacogenomics J. 2013; 13: 312-317Crossref PubMed Scopus (29) Google Scholar), TG levels increased significantly during the first 3.5 h and decreased significantly from 3.5 to 6 h after both high-fat meals (pre- and post-FFB) (P ≤ 0.05). Moreover, the post-FFB clearance was significantly different than the pre-FFB clearance (P ≤ 0.05).TABLE 1Clinical characteristics of samples in GOLDNPhenotypePre-FFBPost-FFBMeanSDMeanSDBMI, kg/m228.55.6——Glucose, mg/dl101.5118.7499.4419.05Systolic blood pressure, mmHg116.0816.82——Diastolic blood pressure, mmHg68.579.6——HDL, 0 h, log(mg/dl)3.810.273.860.26LDL, 0 h, log(mg/dl)4.780.274.600.31TG, 0 h, log(mg/dl)4.750.594.360.52TG, 3.5 h, log(mg/dl)5.380.574.990.55TG, 6 h, log(mg/dl)5.230.704.800.61TG uptake, log(mg/(dl*h))0.170.080.170.09TG clearance, log(mg/(dl*h))−0.060.13−0.080.13TG AUI, mg*h/dl6.71E-043.37E-046.65E-043.16E-04The values were log-transformed. Open table in a new tab The values were log-transformed. On average, 61,235,513 (SD = 11,606,103) reads per sample were generated, and 98.9% reads were mapped to the reference genome. In
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