ß2-adrenergic receptor Thr164Ile polymorphism, obesity, and diabetes: comparison with FTO, MC4R, and TMEM18 polymorphisms in more than 64,000 individuals.
The ß(2)-adrenergic receptor (ADRB2) influences regulation of energy balance by stimulating catecholamine-induced lipolysis in adipose tissue. The rare functional ADRB2rs1800888(Thr164Ile) polymorphism could therefore influence risk of obesity and subsequently diabetes.
We tested the hypothesis that the ADRB2rs1800888(Thr164Ile) polymorphism associates with risk of obesity and diabetes and compared effect sizes with those of FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238).
We conducted a population-based cohort study in Copenhagen, Denmark.
We genotyped more than 64,000 individuals from the Danish general population.
We evaluated body mass index (BMI), obesity (BMI =30 kg/m(2)), and diabetes.
Rare allele frequencies were 0.02 for T for ADRB2rs1800888(Thr164Ile), 0.40 for A for FTOrs9939609, 0.25 for C for MC4Rrs17782313, and 0.20 for T for TMEM18rs6548238. For rare vs. common homozygotes, odds ratio for obesity was 3.32 (95% confidence interval = 1.08-10.19) for ADRB2rs1800888(Thr164Ile), 1.42 (1.35-1.52) for FTOrs9939609, 1.18 (1.06-1.30) for MC4Rrs17782313, and 1.28 (1.10-1.50) for TMEM18rs6548238 (common vs. rare). Corresponding odds ratios for diabetes were 1.85 (0.24-14.29), 1.22 (1.07-1.39), 0.96 (0.80-1.16), and 1.61 (1.17-2.22), respectively. After adjustment for BMI, only TMEM18rs6548238 remained associated with diabetes. BMI was increased in rare vs. common homozygotes in FTOrs9939609, MC4Rrs17782313, and TMEM18rs6548238 (common vs. rare) but not in ADRB2rs1800888(Thr164Ile).
Our results suggest that ADRB2rs1800888(Thr164Ile) rare vs. common homozygotes are not significantly associated with an increase in BMI measured continuously but may be associated with an increased risk of obesity. Also, TMEM18rs6548238 associated with risk of diabetes after adjustment for BMI. These findings need confirmation in other studies.
BACKGROUND AND OBJECTIVE: Many environmental and genetic factors influence the development of chemoresistance. The goal of this study was to characterize the genetic variation in the ABCB1, GSTM1, GSTT1 and GSTP1 genes, as well as the haplotype structure in the ABCB1 gene. METHODS: Variants in these genes were studied in 109 healthy controls and 93 breast cancer cases, both of Caucasian origin. The cases were analyzed in relation to TP53 mutation status and response to doxorubicin. Both single and multiple single nucleotide polymorphism analyses were performed. RESULTS: Chi-square analyses revealed a significant association between TP53 mutation status and both the GA genotype of ABCB1 exon 11 (Ser400Asn) and the GG genotype of GSTP1 (Ile105Val; P
The first genome-wide association study for BMI identified a polymorphism, rs7566605, 10 kb upstream of the insulin-induced gene 2 (INSIG2) transcription start site, as the most significantly associated variant in children and adults. Subsequent studies, however, showed inconsistent association of this polymorphism with obesity traits. This polymorphism has been hypothesized to alter INSIG2 expression leading to inhibition of fatty acid and cholesterol synthesis. Hence, we investigated the association of the INSIG2 rs7566605 polymorphism with obesity- and lipid-related traits in Danish and Estonian children (930 boys and 1,073 girls) from the European Youth Heart Study (EYHS), a school-based, cross-sectional study of pre- and early pubertal children. The association between the polymorphism and obesity traits was tested using additive and recessive models adjusted for age, age-group, gender, maturity and country. Interactions were tested by including the interaction terms in the model. Despite having sufficient power (98%) to detect the previously reported effect size for association with BMI, we did not find significant effects of rs7566605 on BMI (additive, P = 0.68; recessive, P = 0.24). Accordingly, the polymorphism was not associated with overweight (P = 0.87) or obesity (P = 0.34). We also did not find association with waist circumference (WC), sum of four skinfolds, or with total cholesterol, triglycerides, low-density lipoprotein, or high-density lipoprotein. There were no gender-specific (P = 0.55), age-group-specific (P = 0.63) or country-specific (P = 0.56) effects. There was also no evidence of interaction between genotype and physical activity (P = 0.95). Despite an adequately powered study, our findings suggest that rs7566605 is not associated with obesity-related traits and lipids in the EYHS.
Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.
Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package.
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Cites: Genetics. 1971 Aug;68(4):581-975166069
Cites: Proc Biol Sci. 2013 Oct 7;280(1768):2013133923926150
Cites: Am J Hum Genet. 2013 Nov 7;93(5):840-5124207118
High serum triglyceride (TG) levels is an established risk factor for coronary heart disease (CHD). Fat is stored in the form of TGs in human adipose tissue. We hypothesized that gene co-expression networks in human adipose tissue may be correlated with serum TG levels and help reveal novel genes involved in TG regulation.
Gene co-expression networks were constructed from two Finnish and one Mexican study sample using the blockwiseModules R function in Weighted Gene Co-expression Network Analysis (WGCNA). Overlap between TG-associated networks from each of the three study samples were calculated using a Fisher's Exact test. Gene ontology was used to determine known pathways enriched in each TG-associated network.
We measured gene expression in adipose samples from two Finnish and one Mexican study sample. In each study sample, we observed a gene co-expression network that was significantly associated with serum TG levels. The TG modules observed in Finns and Mexicans significantly overlapped and shared 34 genes. Seven of the 34 genes (ARHGAP30, CCR1, CXCL16, FERMT3, HCST, RNASET2, SELPG) were identified as the key hub genes of all three TG modules. Furthermore, two of the 34 genes (ARHGAP9, LST1) reside in previous TG GWAS regions, suggesting them as the regional candidates underlying the GWAS signals.
This study presents a novel adipose gene co-expression network with 34 genes significantly correlated with serum TG across populations.
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Cites: Ann Clin Res. 1973 Jun;5(3):109-414584134
Cites: Genome Biol. 2011;12(3):R2221410973
Cites: Nat Genet. 2006 Feb;38(2):218-2216429159
Cites: PLoS Genet. 2006 Aug 18;2(8):e13016934000
Cites: Am J Hum Genet. 2007 Jun;80(6):1024-3617503322
Cites: Am J Physiol Gastrointest Liver Physiol. 2007 Jul;293(1):G1-417218471
Cites: Diabetologia. 2008 Jan;51(1):62-917972059
Cites: Bioinformatics. 2008 Mar 1;24(5):719-2018024473
Cites: Genome Res. 2008 May;18(5):706-1618347327
Cites: Am J Hum Genet. 2008 Aug;83(2):180-9218674750
Alpha 1-antitrypsin (A1AT) deficiency, one of the most common inborn errors of metabolism in Caucasians, is characterized by a low serum concentration of A1AT and a high risk of pulmonary emphysema and liver disease. The allelic frequency for the most common protease inhibitor (PI) Z mutation in the SERPINA1 gene is 2-5% in Caucasians of European descent. The objective of our study was to estimate the PI Z mutation age using molecular analysis in Latvian and Swedish populations, which have the highest frequency of PI Z mutation. DNA samples of heterozygous and homozygous PI Z allele carriers from Latvia (n = 21) and Sweden (n = 65) were analysed; 113 unrelated healthy donors from Latvia were used as a control group. MALDI-TOF analysis was performed on all samples. Pairwise Fst was computed to compare the PI Z mutation ages between the two populations and controls. A p value less than 0.05 was considered significant. Analysis of non-recombinant SNPs revealed that the PI Z mutation age was 2902 years in Latvia (SD 1983) and 2362 years in Sweden (SD 1614) which correlates with previous studies based on microsatellite analysis.
In addition to APOE and FOXO3, AKT1 has recently been suggested as a third consistent longevity gene, with variants in AKT1 found to be associated with human lifespan in two previous studies. Here, we evaluated AKT1 as a longevity-associated gene across populations by attempting to replicate the previously identified variant rs3803304 as well as by analyzing six additional AKT1 single-nucleotide polymorphisms, thus capturing more of the common variation in the gene. The study population was 2996 long-lived individuals (nonagenarians and centenarians) and 1840 younger controls of Danish and German ancestry. None of the seven SNPs tested were significantly associated with longevity in either a case-control or a longitudinal setting, although a supportive nominal indication of a disadvantageous effect of rs3803304 was found in a restricted group of Danish centenarian men. Overall, our results do not support AKT1 as a universal longevity-associated gene.
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Cites: Nature. 2010 Mar 25;464(7288):504-1220336132
Allele and genotype frequencies of the -174G/C polymorphism (rs1800795) in the regulatory region of the IL6 gene, which encode anti-inflammatory cytokine interleukin 6, were determined in seven populations representing five ethnic groups from the European part of Russia (440 individuals), as well as in small cohorts that represent populations from 24 countries of Africa and Eurasia (365 individuals). The maps of the geographic distribution of the -174G/C allele frequencies were constructed based on personal (22 populations) and the literature data (66 populations), and the data from dbSNP database obtained by the HapMap project (10 populations). The frequency of the -174G allele varied from 45 to 100% and was characterized by nonrandom geographic distribution. These data could reflect the adaptive load of the alleles examined, which was different in different regions of the world. It is suggested that the level of pathogen prevalence is one of the environmental factors that determine different adaptive values of the IL6*--174G/C alleles. This suggestion is supported by a positive correlation between the -174G allele frequency and level of pathogen prevalence calculated based on historical data (R = 0.768; p
