Dataset Information

Genetic association of LPL rs326 with BMI among the Kuwaiti population

ABSTRACT: Supplemental Digital Content is available in the text. Lipoprotein lipase is a key enzyme in lipid metabolism with reported variants associated with obesity, hypertension, type 2 diabetes, and coronary heart disease. This study was performed to investigate the association between common lipoprotein lipase single nucleotide polymorphisms and metabolic disorders in a sample of Kuwaiti cohort (n = 494). Five lipoprotein lipase variants (rs1801177, rs295, rs326, ss2137497749, and ss2137497750) across the lipoprotein lipase gene were genotyped by real-time PCR employing the TaqMan allele discrimination assay. Genotype, allelic frequencies, and Hardy-Weinberg Equilibrium were determined for each variant in the cohort followed by multivariate and logistic regression analysis. A novel finding was observed for the G allele of single nucleotide polymorphism rs326 which was associated with increased BMI after adjusting for age and sex (β = 1.04; 95% confidence interval = 0.15–1.94; P = 0.02). Moreover, a significant difference in the distribution of the minor C allele of rs295 among coronary heart disease subjects compared with noncoronary heart disease, however, this significance was diminished after controlling for age, sex, and BMI. This study demonstrated that lipoprotein lipase rs326 may be indicative for the increased risk of obesity and possibly rs295 for coronary heart disease. The findings are also in agreement with other reports suggesting that intronic variants are important genetic markers in association studies. The findings warrant further studies in a large cohort to confirm and validate the results presented.

SUBMITTER: Malek S

PROVIDER: S-EPMC8575433 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The State of Kuwait is characterized by settlers from Saudi Arabia, Iran, and other regions of the Arabian Peninsula. The settlements and subsequent admixtures have shaped the genetics of Kuwait. High prevalence of recessive disorders and metabolic syndromes (that increase risk of diabetes) is seen in the peninsula. Understanding the genetic structure of its population will aid studies designed to decipher the underlying causes of these disorders. In this study, we analyzed 572,366 SNP markers from 273 Kuwaiti natives genotyped using the illumina HumanOmniExpress BeadChip. Model-based clustering identified three genetic subgroups with different levels of admixture. A high level of concordance (Mantel test, p=0.0001 for 9999 repeats) was observed between the derived genetic clusters and the surname-based ancestries. Use of Human Genome Diversity Project (HGDP) data to understand admixtures in each group reveals the following: the first group (Kuwait P) is largely of West Asian ancestry, representing Persians with European admixture; the second group (Kuwait S) is predominantly of city-dwelling Saudi Arabian tribe ancestry, and the third group (Kuwait B) includes most of the tent-dwelling Bedouin surnames and is characterized by the presence of 17% African ancestry. Identity by Descent and Homozygosity analyses find Kuwait's population to be heterogeneous (placed between populations that have large amount of ROH and the ones with low ROH) with Kuwait S as highly endogamous, and Kuwait B as diverse. Population differentiation FST estimates place Kuwait P near Asian populations, Kuwait S near Negev Bedouin tribes, and Kuwait B near the Mozabite population. FST distances between the groups are in the range of 0.005 to 0.008; distances of this magnitude are known to cause false positives in disease association studies. Results of analysis for genetic features such as linkage disequilibrium decay patterns conform to Kuwait's geographical location at the nexus of Africa, Europe, and Asia.

Project description:BackgroundThe timing of associations between common genetic variants and changes in growth patterns over childhood may provide insight into the development of obesity in later life. To address this question, it is important to define appropriate statistical models to allow for the detection of genetic effects influencing longitudinal childhood growth.Methods and resultsChildren from The Western Australian Pregnancy Cohort (Raine; n=1,506) Study were genotyped at 17 genetic loci shown to be associated with childhood obesity (FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, SEC16B, LYPLAL1, TFAP2B, MTCH2, BCDIN3D, NRXN3, SH2B1, MRSA) and an obesity-risk-allele-score was calculated as the total number of 'risk alleles' possessed by each individual. To determine the statistical method that fits these data and has the ability to detect genetic differences in BMI growth profile, four methods were investigated: linear mixed effects model, linear mixed effects model with skew-t random errors, semi-parametric linear mixed models and a non-linear mixed effects model. Of the four methods, the semi-parametric linear mixed model method was the most efficient for modelling childhood growth to detect modest genetic effects in this cohort. Using this method, three of the 17 loci were significantly associated with BMI intercept or trajectory in females and four in males. Additionally, the obesity-risk-allele score was associated with increased average BMI (female: ?=0.0049, P=0.0181; male: ?=0.0071, P=0.0001) and rate of growth (female: ?=0.0012, P=0.0006; male: ?=0.0008, P=0.0068) throughout childhood.ConclusionsUsing statistical models appropriate to detect genetic variants, variations in adult obesity genes were associated with childhood growth. There were also differences between males and females. This study provides evidence of genetic effects that may identify individuals early in life that are more likely to rapidly increase their BMI through childhood, which provides some insight into the biology of childhood growth.

Dataset Information

Genetic association of LPL rs326 with BMI among the Kuwaiti population

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets