Genomics in the Scientific Literature [16] - Diabetes

Genomics in the Scientific Literature
Topics in the Scientific Literature

Diabetes
1. Getting biological about the genetics of diabetes
Newgard CB & Attie AD
Nat Med 2010 Apr;16(4):388-91

Nat Med. 2010 Apr;16(4):388-91.

Getting biological about the genetics of diabetes.
Newgard CB, Attie AD.

Sarah W. Stedman Nutrition and Metabolism Center and Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA. newga002@mc.duke.edu

Abstract
New technology has provided methods for collecting large amounts of data reflecting gene expression, metabolite and protein abundance, and post-translational modification of proteins. Integration of these various data sets enable the genetic mapping of many new phenotypes and facilitates the creation of network models that link genetic variation with intermediate traits leading to human disease. The first round of genome-wide association studies has not accounted for common human diseases to the extent that was expected. New phenotyping approaches and methods of data integration should bring these studies closer to their promised goals.

PMID: 20376050 [PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/20376050?dopt=Abstract


2. In Silico Replication of the Genome-wide Association Results of the Type 1 Diabetes Genetics Consortium
Qu HQ, et al.
Hum Mol Genet 2010 Apr

Hum Mol Genet. 2010 Apr 8. [Epub ahead of print]

In Silico Replication of the Genome-wide Association Results of the Type 1 Diabetes Genetics Consortium.
Qu HQ, Bradfield JP, Li Q, Kim C, Frackelton E, Grant SF, Hakonarson H, Polychronakos C.

Departments of Pediatrics and Human Genetics, McGill University, Montreal H4H 2P4, Québec, Canada;

Abstract
Background: Recently, the Type 1 Diabetes Genetics Consortium (T1DGC) reported 22 novel type 1 diabetes associated loci identified by the meta-analysis of three genome-wide association studies (GWAS) with case-control design. However, the association of ten of these 22 reported loci was not confirmed in the T1DGC family cohort (P>0.1). To address concerns about potential bias from population stratification, this study aims to replicate the association in three independent GWAS cohorts, one of which was based on the stratification-proof transmission disequilibrium analysis. Research Design and Methods: Three European-descent population samples were included in this study: 483 cases and both parents, a case-control cohort of 514 cases and 2,027 controls, and an additional cohort of 1,078 cases and 341 controls from the dbGaP database. Results: Among the 22 SNPs reported by the T1DGC, we had high-quality genotypes for fifteen; the remaining were imputed. Type 1 diabetes association was replicated in seven loci after Bonferroni correction for 22 independent hypotheses. An additional eight loci had nominal (one-sided) significance of P<0.1 in the same direction, giving a false discovery rate (FDR) =3.35%. The genetic susceptibility conferred by non-HLA loci in our family cohort with one affected offspring was higher than the T1DGC multiplex families. Reciprocally the frequency of strongly predisposing HLA alleles in the multiplex families was higher. Conclusions: This study replicated type 1 diabetes association with at least as many of these novel loci as expected from the power of our sample size, thus supporting the validity of the new discoveries.

PMID: 20378605 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/20378605?dopt=Abstract