Dataset Information

Genetic association analysis of LARS2 with type 2 diabetes.

ABSTRACT:

Aims/hypothesis

LARS2 has been previously identified as a potential type 2 diabetes susceptibility gene through the low-frequency H324Q (rs71645922) variant (minor allele frequency [MAF] 3.0%). However, this association did not achieve genome-wide levels of significance. The aim of this study was to establish the true contribution of this variant and common variants in LARS2 (MAF > 5%) to type 2 diabetes risk.

Methods

We combined genome-wide association data (n = 10,128) from the DIAGRAM consortium with independent data derived from a tagging single nucleotide polymorphism (SNP) approach in Dutch individuals (n = 999) and took forward two SNPs of interest to replication in up to 11,163 Dutch participants (rs17637703 and rs952621). In addition, because inspection of genome-wide association study data identified a cluster of low-frequency variants with evidence of type 2 diabetes association, we attempted replication of rs9825041 (a proxy for this group) and the previously identified H324Q variant in up to 35,715 participants of European descent.

Results

No association between the common SNPs in LARS2 and type 2 diabetes was found. Our replication studies for the two low-frequency variants, rs9825041 and H324Q, failed to confirm an association with type 2 diabetes in Dutch, Scandinavian and UK samples (OR 1.03 [95% CI 0.95-1.12], p = 0.45, n = 31,962 and OR 0.99 [0.90-1.08], p = 0.78, n = 35,715 respectively).

Conclusions/interpretation

In this study, the largest study examining the role of sequence variants in LARS2 in type 2 diabetes susceptibility, we found no evidence to support previous data indicating a role in type 2 diabetes susceptibility.

SUBMITTER: Reiling E

PROVIDER: S-EPMC2789927 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic association analysis of LARS2 with type 2 diabetes.

Reiling E E Jafar-Mohammadi B B van 't Riet E E Weedon M N MN van Vliet-Ostaptchouk J V JV Hansen T T Saxena R R van Haeften T W TW Arp P A PA Das S S Nijpels G G Groenewoud M J MJ van Hove E C EC Uitterlinden A G AG Smit J W A JW Morris A D AD Doney A S F AS Palmer C N A CN Guiducci C C Hattersley A T AT Frayling T M TM Pedersen O O Slagboom P E PE Altshuler D M DM Groop L L Romijn J A JA Maassen J A JA Hofker M H MH Dekker J M JM McCarthy M I MI 't Hart L M LM

Diabetologia 20091022 1

<h4>Aims/hypothesis</h4>LARS2 has been previously identified as a potential type 2 diabetes susceptibility gene through the low-frequency H324Q (rs71645922) variant (minor allele frequency [MAF] 3.0%). However, this association did not achieve genome-wide levels of significance. The aim of this study was to establish the true contribution of this variant and common variants in LARS2 (MAF > 5%) to type 2 diabetes risk.<h4>Methods</h4>We combined genome-wide association data (n = 10,128) from the ...[more]

PMID: 19847392

Similar Datasets

Project description:Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently.MethodsUsing publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases.ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect.Conclusions/interpretationFour of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases.

Project description:SIRT1 has been demonstrated in nutrient-sensing and insulin-signaling pathways in in vivo and in vitro experiments, but there is minimal information concerning the association between gene polymorphisms of SIRT1 and type 2 diabetes mellitus (T2DM) in a Chinese Han population. Using case-control design, we recruited 310 unrelated T2DM patients from inpatients at Shanghai Jiao Tong University Affiliated Sixth People's Hospital, while 301 healthy controls were volunteers from the community for regular medical checkup. All participants were genotyped within the SIRT1 region. The following five SNPs rs10509291, rs12778366, rs10997870, rs10823112, and rs4746720 cover 100% of common genetic variations (minor allele frequency≥0.05) within the SIRT1 gene (r2≥0.8). The genotypes of SIRT1 gene polymorphisms were analyzed by the Snapshot assay and DNA sequencing. The resulting data show that there was significant genetic differentiation in rs10823112 [p=0.003; OR (95% CI)=1.515 (1.152-1.994) for genotype], rs4746720 [p=0.024; OR (95% CI)=1.37 (1.037-1.674) for genotype], and rs10509291 [p=0.002; OR (95% CI)=1.551 (1.179-2.04) for genotype] between T2DM and control subjects. However, the result of rs4746720 was no longer significant after correction for multiple testing (p after Bonferroni correction=0.12); the results of rs10509291and rs10823112 were still significantly different between the two groups (p after Bonferroni correction=0.01 and 0.015, respectively). Linear regression analyses adjusting for age, gender, and body mass index (BMI) showed that HbA1c and HOMA-IR in subjects with rs10509291 AA genotype were higher than those with TT genotype in T2DM group (p=0.045, p=0.035, respectively). Together, our data show that genetic variation of the SIRT1 gene is related to insulin resistance and increase risk of T2DM in Chinese Han population. The risk allele A at SIRT1 rs10509291 was closely associated with T2DM, and subjects who were homozygous of the A allele were more likely to develop T2DM.

Dataset Information

Genetic association analysis of LARS2 with type 2 diabetes.

Aims/hypothesis

Methods

Results

Conclusions/interpretation

Publications

Genetic association analysis of LARS2 with type 2 diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets