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Genome-wide association study identifies novel recessive genetic variants for high TGs in an Arab population

2018; Elsevier BV; Volume: 59; Issue: 10 Linguagem: Inglês

10.1194/jlr.p080218

1539-7262

Prashantha Hebbar, Rasheeba Nizam, Motasem Melhem, Fadi Alkayal, Naser Elkum, Sumi Elsa John, Jaakko Tuomilehto, Osama Alsmadi, Thangavel Alphonse Thanaraj,

Epigenetics and DNA Methylation

Abnormal blood lipid levels are influenced by genetic and lifestyle/dietary factors. Although many genetic variants associated with blood lipid traits have been identified in Europeans, similar data in Middle Eastern populations are limited. We performed a genome-wide association study with Arab individuals (discovery cohort: 1,353; replication cohort: 1,176) from Kuwait to identify possible associations of genetic variants with high lipid levels. We used Illumina HumanOmniExpress BeadChip and candidate SNP genotyping in the discovery and replication phases, respectively. For association tests, we used genetic models that were based on additive and recessive modes of inheritance. High triglycerides (TGs) were recessively associated with six risk variants (rs1002487/RPS6KA1, rs11805972/LAD1) rs7761746/Or5v1, rs39745/CTTNBP2-LSM8, rs2934952/PGAP3, and rs9626773/RP11-191L9.4-CERK) at genome-wide significance (P ≤ 6.12E-09), and another six variants (rs10873925/ST6GALNAC5, rs4663379/SPP2-ARL4C, rs10033119/NPY1R, rs17709449/LINC00911-FLRT2, rs11654954/CDK12-NEUROD2, and rs9972882/STARD3) were associated at borderline significance (P ≤ 5.0E-08). High TG was also additively associated with rs11654954. All of the 12 identified markers are novel and are harbored in runs of homozygosity. Literature evidence supports the involvement of these gene loci in lipid-related processes. This study in an Arab population augments international efforts to identify genetic regulation of lipid traits. Abnormal blood lipid levels are influenced by genetic and lifestyle/dietary factors. Although many genetic variants associated with blood lipid traits have been identified in Europeans, similar data in Middle Eastern populations are limited. We performed a genome-wide association study with Arab individuals (discovery cohort: 1,353; replication cohort: 1,176) from Kuwait to identify possible associations of genetic variants with high lipid levels. We used Illumina HumanOmniExpress BeadChip and candidate SNP genotyping in the discovery and replication phases, respectively. For association tests, we used genetic models that were based on additive and recessive modes of inheritance. High triglycerides (TGs) were recessively associated with six risk variants (rs1002487/RPS6KA1, rs11805972/LAD1) rs7761746/Or5v1, rs39745/CTTNBP2-LSM8, rs2934952/PGAP3, and rs9626773/RP11-191L9.4-CERK) at genome-wide significance (P ≤ 6.12E-09), and another six variants (rs10873925/ST6GALNAC5, rs4663379/SPP2-ARL4C, rs10033119/NPY1R, rs17709449/LINC00911-FLRT2, rs11654954/CDK12-NEUROD2, and rs9972882/STARD3) were associated at borderline significance (P ≤ 5.0E-08). High TG was also additively associated with rs11654954. All of the 12 identified markers are novel and are harbored in runs of homozygosity. Literature evidence supports the involvement of these gene loci in lipid-related processes. This study in an Arab population augments international efforts to identify genetic regulation of lipid traits. Abnormal blood lipid levels are risk factors for cardiovascular disorders (1.Stamler J. Dyer A.R. Shekelle R.B. Neaton J. Stamler R. 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The reasons for heterogeneity and lack of generalization include differences in effect sizes, allele frequencies, and linkage disequilibrium (LD) (25.Dumitrescu L. Carty C.L. Taylor K. Schumacher F.R. Hindorff L.A. Ambite J.L. Anderson G. Best L.G. Brown-Gentry K. Buzkova P. Genetic determinants of lipid traits in diverse populations from the population architecture using genomics and epidemiology (PAGE) study.PLoS Genet. 2011; 7: e1002138Crossref PubMed Scopus (117) Google Scholar); it is also the case that sample sizes in previous studies conducted in the Arab region are probably underpowered to detect the established associations from other populations. The native population of Kuwait is unique in many ways and offers a potential to detect novel genetic associations. The Arabian Peninsula is at the nexus of Africa, Europe, and Asia; and has been presumed to be part of early human migration. The population is well-structured into three groups: the KWP group that is largely of West Asian ancestry with European admixture; the KWS group that is predominantly of city-dwelling Saudi Arabian tribe ancestry; and the KWB group that is comprised of tent-dwelling nomadic Bedouins with a characteristic presence of 17% African ancestry (33.Alsmadi O. Thareja G. Alkayal F. Rajagopalan R. John S.E. Hebbar P. Behbehani K. Thanaraj T.A. Genetic substructure of Kuwaiti population reveals migration history.PLoS One. 2013; 8: e74913Crossref PubMed Scopus (39) Google Scholar). The population of Kuwait, like other states in the Peninsula, has been practicing consanguineous marriages (involving first or second cousins or relatives within the large family or the same tribe); the rate of consanguineous marriages can be as high as 54.3% (34.Al-Awadi S.A. Moussa M.A. Naguib K.K. Farag T.I. Teebi A.S. el-Khalifa M. el-Dossary L. Consanguinity among the Kuwaiti population.Clin. 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The curse of wealth - Middle Eastern countries need to address the rapidly rising burden of diabetes.Int. J. Health Policy Manag. 2014; 2: 109-114Crossref PubMed Scopus (51) Google Scholar), and familial hypercholesterolemia (40.Bamimore M.A. Zaid A. Banerjee Y. Al-Sarraf A. Abifadel M. Seidah N.G. Al-Waili K. Al-Rasadi K. Awan Z. Familial hypercholesterolemia mutations in the Middle Eastern and North African region: a need for a national registry.J. Clin. Lipidol. 2015; 9: 187-194Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar); these disorders, along with the practice of consanguineous marriages, have resulted in a pattern of dyslipidemia [low HDL cholesterol and high triglycerides (TGs)] that is different from many other regions of the world (41.Al Rasadi K. Almahmeed W. AlHabib K.F. Abifadel M. Farhan H.A. AlSifri S. Jambart S. Zubaid M. Awan Z. Al-Waili K. 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Globalization of diabetes: the role of diet, lifestyle, and genes.Diabetes Care. 2011; 34: 1249-1257Crossref PubMed Scopus (1257) Google Scholar). A number of SNPs and genes have been implicated in gene-environment interactions that mediate the blood lipid phenotypes (44.Morabia A. Cayanis E. Costanza M.C. Ross B.M. Flaherty M.S. Alvin G.B. Das K. Gilliam T.C. Association of extreme blood lipid profile phenotypic variation with 11 reverse cholesterol transport genes and 10 non-genetic cardiovascular disease risk factors.Hum. Mol. Genet. 2003; 12: 2733-2743Crossref PubMed Scopus (34) Google Scholar, 45.Parnell L.D. Blokker B.A. Dashti H.S. Nesbeth P.D. Cooper B.E. Ma Y. Lee Y.C. Hou R. Lai C.Q. Richardson K. CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits.BioData Min. 2014; 7: 21Crossref PubMed Scopus (48) Google Scholar). The most common environmental factors associated with lipid phenotypes are physical activity [with HDL and total cholesterol (TC)], high fat challenge (with TG), and dietary saturated fat (with LDL) (46.Lee Y.C. Lai C.Q. Ordovas J.M. Parnell L.D. A database of gene-environment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes.J. Data Mining Genomics Proteomics. 2011; 2: 106Crossref PubMed Google Scholar). Studying the Arab population, which has seen rapid lifestyle transitions, thus has the potential to identify novel preexisting susceptibility gene loci triggered by environmental factors. As stated above, the population of Kuwait is composed of three distinct genetic groups that fall firmly into declared ancestral/tribal backgrounds; association tests, appropriately controlled for population stratification to avoid false positive findings, can provide opportunities for discovery of novel associations that are not seen in other populations. In the present study, we perform a GWAS on a cohort of native Arab individuals from Kuwait to elucidate novel genetic markers underlying the lipid traits, TG, LDL, HDL, and TC. The study was reviewed and approved by the International Scientific Advisory Board and Ethical Review Committee at Dasman Diabetes Institute. A total of 3,145 participants from Kuwait were recruited under protocols in accordance with guidelines laid in place by the institutional Ethical Review Committee. The study cohort included two groups. The first group comprised a random representative sample of adults (age >18 years) of Arab ethnicity from the six governorates of Kuwait. A stratified random sampling technique was used to select participants from the computerized register of the Public Authority of Civil Information, which maintains records of personal information for both Kuwaiti citizens and expatriates from other Arab and non-Arab countries. The second group comprised Arab individuals seeking tertiary medical care for diabetes/prediabetes-related disorders at our clinics, visitors to our nutrition programs and fitness center, visitors to our Open Day Events (that offer various diagnostic services), and visitors to our campaigns at primary health centers and blood banks in each of the six governorates of Kuwait. Such visitors interested to participate in our research programs were invited to the institute at a later date to give samples after fasting overnight. At the time of recruitment, ethnicity was confirmed through detailed questioning on parental lineage up to three generations; data on age, sex, and illness (e.g., diabetes and cardiovascular complications) were recorded. Baseline characteristics and vital signs, such as height, weight, waist circumference (WC), and blood pressure, were recorded. Signed informed consent was obtained from each of the participants. Details on medications taken by the participants for lowering lipid levels, diabetes, and hypertension were collected and used in correction procedures with the association statistics. A participant was regarded as affected by type 2 diabetes if the diagnosis was known to the participant (self-declaration) or, in accordance with the ADA guidelines, if fasting serum glucose was ≥7 mmol/l (126 mg/dl) or if glycated hemoglobin was ≥6.5% (48 mmol/mol). When in doubt, the recorded details on anti-diabetes medication were used; and for participants recruited through our clinics or our campaigns (constituting the above-mentioned second group), the clinician's notes were used. The discovery cohort was drawn largely, but not exclusively, from the second group and the replication cohort was drawn largely, but not exclusively, from the first group. Recruitment into the two groups was carried out in parallel with one another; genotyping for the discovery phase was also performed in parallel to the recruitment process. As a result, in order to make up the numbers for genotyping batches and runs during the course of the discovery phase, randomly chosen samples from the first group had to be added to the discovery cohort (a random set of 200 samples from second group were part of discovery cohort). A total of 1,913 samples were considered for the discovery phase and 1,176 for the replication phase. Upon confirming that the participants had fasted overnight, signed consent forms and blood samples were collected. The guidelines of the institutional Ethical Review Committee were followed for the collection of blood samples and measurement of vital signs. A Gentra Puregene® kit (Qiagen, Valencia, CA) was used to extract DNA. Quantification of DNA was performed using a Quant-iT™ PicoGreen® dsDNA assay kit (Life Technologies, Grand Island, NY) and an Epoch microplate spectrophotometer; only samples with a ratio in the range of 1.8–2.1 for absorbance at 260 nm to absorbance at 280 nm were used. DNA stocks were then frozen. Prior to genotyping, frozen DNA was diluted to a working concentration of 50 ng/μl, as recommended by Illumina (San Diego, CA). Two types of power calculations were performed. The first one was to estimate the sample size and its potential to detect variability in quantitative traits with 80% power and a P-value threshold of 5.0E-08; the second one was to determine the number of samples required to achieve 80% power in a two-stage (discovery and replication) design. For the first calculation, Quanto software (http://biostats.usc.edu/Quanto.html) (47.Gauderman W.J. Sample size requirements for association studies of gene-gene interaction.Am. J. Epidemiol. 2002; 155: 478-484Crossref PubMed Scopus (508) Google Scholar) was used and both additive and recessive models were considered. "Gene only" hypothesis was used. Power for the analysis was set at 80%, and a type 1 error at a P-value of 5.0E-08 was considered significant. The marginal genetic effect estimate (RG2) was set to range from 0.01 to 0.04 in increments of 0.001 to enable the detection of a genetic effect that explained at least 1–4% of trait variance. For each lipid trait, the population (mean ± SD) of the quantitative trait was used. By using Quanto to estimate the power of our study over a range of percent variance of the trait, it was found that the discovery cohort size had 80% power to detect associations with genetic variants (under additive or recessive models) that explained 2.9% variance of the trait. The sample sizes required to detect various RG2 values in the discovery phase were denoted as (RG2; sample size): (0.0100; 3,940), (0.0110; 3,580), (0.0120; 3,280), (0.0130; 3,026), (0.0140; 2,809), (0.0150; 2,620), (0.0160; 2,455), (0.0170; 2,310), (0.0180; 2,180), (0.0190; 2,064), (0.0200; 1,960), (0.0210; 1,866), (0.0220; 1,780), (0.0230; 1,702), (0.0240; 1,630), (0.0250; 1,564), (0.0260; 1,503), (0.0270; 1,447), (0.0280; 1,394), (0.0290; 1,346), (0.0300; 1,300), (0.0310; 1,258), and (0.0320; 1,218). The acceptable effect sizes for associations between the trait of TG and SNP markers in the discovery phase at different allele frequencies are presented in supplemental Table S1. For the second calculation, QPowR software (https://msu.edu/~steibelj/JP_files/QpowR.html) was used with the following parameters: total sample size = 2,532; total heritability = 0.05; samples genotyped in the first or second stage = approximately 50% of 2,532; markers typed in the second stage = typically 0.2% of the markers typed in the first stage; and type I error rate = 5.0E-08. Whole-genome genotyping was performed on DNA samples from the discovery cohort of 1,913 participants. Illumina HumanOmniExpress arrays utilizing Infinium® HD Assay Ultra genotyping assay methods were used to genotype. Assays included whole-genome amplification, fragmentation, hybridization, staining, and imaging of the HumanOmniExpress arrays using the Illumina iSCAN system. Genotyping was performed in batches; Illumina HumanOmniExpress-12v1-Multi_H (730,525 markers) was used to genotype 1,097 participants in 20 batches, HumanOmniExpress-12v1-1_B (719,665 markers) to genotype 336 participants in five batches, and HumanOmniExpress-24v1-0_a (716,503 markers) to genotype 480 participants in six batches. The number of markers in these three versions of OmniExpress BeadChips differed from one another by at most ∼10,000; otherwise the markers were common between the three chip versions. Top markers identified in the discovery phase were selected for replication in a cohort consisting of 1,176 unrelated participants. Candidate SNP genotyping was performed using TaqMan® SNP genotyping assays (Applied Biosystems, Foster City, CA) and ABI 7500 real-time PCR system (Applied Biosystems). Each PCR sample contained 10 ng of genomic DNA, 5× FIREPol® Master Mix (Solis BioDyne, Tartu, Estonia), and 1 μl of 20× TaqMan® SNP genotyping assay (Applied Biosystems). The thermal cycling conditions were 60°C for 1 min, 95°C for 15 min, and then 40 cycles of 95°C for 15 s and 60°C for 1 min. Genotypes ascribed by real-time PCR were validated by direct Sanger sequencing of the PCR products for selected cases of homozygotes and heterozygotes. Sequencing was performed using the BigDye™ Terminator v3.1 cycle sequencing FS ready reaction kit (Applied Biosystems), according to the manufacturer's recommendations, on an Applied Biosystems 3730xl DNA analyzer (Applied B