title: A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets.
abstract: OBJECTIVE
  To use genome-wide fixed marker arrays and improved analytical tools to detect genetic associations with type 2 diabetes in a carefully phenotyped human sample.
  
  
  METHODS
  A total of 1,087 Framingham Heart Study (FHS) family members were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array and examined for association with incident diabetes and six diabetes-related quantitative traits. Quality control filters yielded 66,543 SNPs for association testing. We used two complementary SNP selection strategies (a "lowest P value" strategy and a "multiple related trait" strategy) to prioritize 763 SNPs for replication. We genotyped a subset of 150 SNPs in a nonoverlapping sample of 1,465 FHS unrelated subjects and examined all 763 SNPs for in silico replication in three other 100K and one 500K genome-wide association (GWA) datasets.
  
  
  RESULTS
  We replicated associations of 13 SNPs with one or more traits in the FHS unrelated sample (16 expected under the null); none of them showed convincing in silico replication in 100K scans. Seventy-eight SNPs were nominally associated with diabetes in one other 100K GWA scan, and two (rs2863389 and rs7935082) in more than one. Twenty-five SNPs showed promising associations with diabetes-related traits in 500K GWA data; one of them (rs952635) replicated in FHS. Five previously reported associations were confirmed in our initial dataset.
  
  
  CONCLUSIONS
  The FHS 100K GWA resource is useful for follow-up of genetic associations with diabetes-related quantitative traits. Discovery of new diabetes genes will require larger samples and a denser array combined with well-powered replication strategies.
authors.affiliation: Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
authors.forename: Jose C
authors.initials: JC
authors.lastname: Florez
authors.forename: Alisa K
authors.initials: AK
authors.lastname: Manning
authors.forename: Josée
authors.initials: J
authors.lastname: Dupuis
authors.forename: Jarred
authors.initials: J
authors.lastname: McAteer
authors.forename: Kathryn
authors.initials: K
authors.lastname: Irenze
authors.forename: Lauren
authors.initials: L
authors.lastname: Gianniny
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Mirel
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.forename: L Adrienne
authors.initials: LA
authors.lastname: Cupples
authors.forename: James B
authors.initials: JB
authors.lastname: Meigs
mesh_terms: D000328:Adult; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D003920:Diabetes Mellitus / Q000235:genetics*; D005260:Female; D015894:Genome, Human; D006801:Humans; D007328:Insulin / Q000097:blood; D008137:Longitudinal Studies; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors
chemical_list: D007328:Insulin