A genome-wide association study identifies novel risk loci for type 2 diabetes

By R. Sladek, G. Rocheleau, Johan Rung, C. Dina, Lishuang Shen, D. Serre, P. Boutin, Daniel Vincent, Alexandre Bélisle, S. Hadjadj and 12 more. Nature, 2007

3,097 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d7439d599d11354a2987ead1aad0dd521978dae

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Citations in the corpus

The role of microglia in amyloid clearance from the AD brain (2010, background): “IDE binds insulin with high affinity (*100 nM) and degrades insulin into fragments which links it to the type 2 diabetes (Sladek et al. 2007).”
Consistent Association of Type 2 Diabetes Risk Variants Found in Europeans in Diverse Racial and Ethnic Groups (2010, background): “With the exception of the KCNQ1 locus which was identified in the Japanese population [1,2], all of the well-replicated risk variants were first identified in populations of Northern European ancestry [3–13].”
Why adaptively collected data have negative bias and how to correct for it (2017, methodology): “Data splitting dates back to Cox [1975] and has been discussed by in the context of identifying loci of interest in genetics [Sladek et al., 2007], and online search advertising [Xu et al.”
A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk (2009, methodology): “A GWA meta-analysis for FPG was done in nondiabetic French individuals with European ancestry from our recent genome-wide T2”
Adaptive Multi-Task Lasso: with Application to eQTL Detection (2010, background): “The most common genetic variations are single nucleotide polymorphisms (SNPs), and many association studies have been conducted to find SNPs that cause phenotypic variations such as diseases or gene-expression traits [1].”
The linkage disequilibrium and the selection of genetic markers for association studies in European populations (2007, background): “Current association studies use thousands or even tens of thousands of individuals to analyze the effect of disease causing alleles (Herbert et al. 2006; Sladek et al. 2007; Smyth et al. 2006).”