Genome–wide association studies provide new insights into type 2 diabetes aetiology

By T. Frayling. Nature reviews genetics, 2007

617 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Surgery

View Paper: https://www.semanticscholar.org/paper/a47fa9a369863d97ac4d7bd026286a274ea239c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Endocrinology, Diabetes and Metabolism

1. E. Gilbert
2. Zhuo Fu
3. Dongmin Liu
4. Elizabeth T. Cirulli
5. David B. Goldstein
6. R. Huxley

In Genetics

1. Elizabeth T. Cirulli
2. Eric J. Topol
3. Haiyuan Yu
4. E. Gilbert
5. Marylyn DeRiggi Ritchie
6. Idan Menashe
7. Nicholas J. Schork
8. Zhuo Fu

In Surgery

1. E. Gilbert
2. Zhuo Fu
3. Dongmin Liu
4. Elizabeth T. Cirulli
5. David B. Goldstein
6. R. Huxley

Citations in the corpus

• PGA: power calculator for case-control genetic association analyses (2008, background): “This tool may become extremely important in the current phase of genetic association studies where a large number of diseases-susceptibility genomic loci are revealed by genome-wide association studies (GWAS) [21-23].”
• Pathophysiology of diabetes: An overview (2020, background): “However, unlike T1DM, no association of this disease has been found with genes involved in the immune response including autoimmunity and consequently there is no immune-mediated pancreatic β-cell destruction.[32,33]”
• Network-based methods for human disease gene prediction. (2011, background): “It has been reported that the genes established to be associated with diseases such as cancer and type 2 diabetes only represent a very small proportion of the incidences [2, 3].”
• Uncovering the roles of rare variants in common disease through whole-genome sequencing (2010, background): “led to the general assumption that some as-yet-unidentified common variant is responsible for the associations seen in most GWA studie”
• Quality Control Procedures for Genome Wide Association Studies (2011, methodology): “Many studies routinely genotype DNA samples from the HapMap cell lines (International HapMap Consortium, 2003, 2007).”
• Birth weight and risk of type 2 diabetes: a systematic review. (2008, background): “Type 2 diabetes has a multifactorial etiology, with important contributions from obesity, lifestyle, and genetic factors.(4,5) In 1991, Hales et al(6) reported a graded inverse association between birth weight and risk of type 2 diabetes, with the highest risks of type 2 diabetes occurring at the lo”
• Human genetic variation and its contribution to complex traits (2009, background): “For example, the genetic propensity to develop type 2 diabetes (T2D) seems to involve genes in several different pathways that affect pancreatic β-cell formation and function, as well as pathways affecting fasting glucose levels and obesit”
• A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, background): “A significant number of research investigations have been devoted to it, notably by means of genome-wide association study (GWAS) and phenome-wide association study (PheWAS) in hope of detecting more associations between genotypes and phenotypes [3-10, 23-26, 36].”
• Regulation of Insulin Synthesis and Secretion and Pancreatic Beta-Cell Dysfunction in Diabetes (2012, positive): “Other genes with variants associated with reduced insulin secretion in diabetic patients include TCF7L2 ([497]; transcription factor 7-like 2), SLC30A8 ([498, 499]; solute carrier family 30; zinc transporter, member 8), and CDKAL1 ([500]; CDK5 regulatory subunit associated protein 1-like 1) [495].”