CHARGES Eric Boerwinkle Washington DC April 7 2010 Overall Objective This proposed research will leverage existing population laboratory and computational resources to identify susceptibility ID: 1043325
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1. Building on GWAS for HLB-disease: the US CHARGE Consortium(CHARGE-S)Eric BoerwinkleWashington DCApril 7, 2010
2. Overall Objective“This proposed research will leverage existing population, laboratory and computational resources to identify susceptibility genes and variants underlying selected well-replicated GWAS findings for heart, lung and blood diseases and their risk factors.”
3. OutlineCase-Cohort DesignCHARGE-S & Heart-GO coordinationUltra-deep resequencingA worry
4. PleiotropyPulmonaryInflammation / Oxidative StressAtrial FibrillationMusculoskeletalBlood Pressure/ HypertensionDiabetes/glucoseECHO/LV wall thicknessNeurology/CognitionKidneyHematologyRetinalBiomarker (CRP)ECG/Heart Rate/QT/PRTargeted Sequencing Working Groups
5. Sequencing Sampling Design CohortRandomSample CaseGroup#7BPKidneyCRP
6. Case-Cohort Sample Details 1,000 ARIC500 FHS500 CHS 200from clinic10050501005050100100
7. Case-Cohort AnalysesBreslow and DayCompared to GWAS, need to shift one’s focus from the SNP to the gene. The gene is the new unit of inference.Easy to analyze the data with counts.Possible to analyze the data with “regression-type” analyses and score statistics.Meta-analyze results across cohorts/samples.Weighted analyses.
8. Coordination betweenCHARGE-S and Heart-GOA person may be eligible for sampling via multiple paths. For example, one person from ARIC may be an early MI case and have high LDL-C.This is relevant for phenotype subcommittee coordination within HEART-GO and for CHARGE-S & Heart-GO coordination. Sequence once, but analyze multiple times. Gate keeper is the cohorts (labs and coordinating center)Although a minor point (in my opinion) we need to keep in mind that the results across phenotypes are not independent.
9. Cooperation betweenCHARGE-S and Heart-GOIncrease size of comparison groups. Remember from Shumil, under certain models, the power of an analysis is driven by the size of the comparison group.Is a two-way street!Will require coordination and compromise.
10. Slide10Feature Map of KCNJ11 (1 exon, chromosome 11) insertiondeletion12345kb173630001736350017364000173645001736500017365500173660001736650017367000CoordinateScale17363372173667825’3’17365040173662161736250017367500deletiondeletionEXONmRNA510 Variants12 Common VariantsUTRCoding-synonymous SNPCoding-non-synonymous SNPIntergenic SNPFeature Map of KCNJ11 (1 exon, chromosome 11) deletion(Expected Number: 261.7)CpG Islands (UCSC)17366268173660291736786317367013
11. HHEX in European-American
12. Slide12KCNJ11 Pronounced excess of rare variants compared to neutral theoryCount of minor allele
13. Slide13Human demographic models: fitted to older, common SNPs
14. Slide14Recent explosive population growthfits the site frequency spectrum well
15. Slide15Joint likelihood surface of mutation and growth rates (KCNJ11)r = 1.3%, m = 2.40 x 10-8
16. A little philosophy and a WorryGWAS is the first step, not the last.In a sample, there is no reason to think that SNPs with small p-values are functional, or even the best predictor in another sample.Statistics will not (alone) provide functional information.Genetic architecture Common variation in the GWAS-identified genes. Low frequency and rare variation in those genes. Low frequency and rare variation elsewhere in the genome.
17. Lancet 12/2008SLC2A9ABCG2SLC17A3 ABCG2 localizes to the brush border membrane of renal proximal tubule cells in native renal tissue
18. Effect of ABCG2 on intracellular urate accumulationH2O ABCG2 C14 - urate ABCG2 is a novel urateTransporter. However, because of the number of variants and LD, we were unable to identify functional variants or the variants underlying the GWAS signal.
19. Reduced urate efflux in ABCG2 Q141KUrate AccumulationQ141KWT At position 141 glutamine lysine substitution encoded by rs2231142 is a loss of function mutation**p<0.0001
20. Future NeedsIn my opinion, we should be working toward building high throughput cost-effective pipelines to assess functional significance of associated variants.E.g. transporters, growth factors, transcription factors.I think this need is even more urgent as a result large-scale sequencing efforts.The worry – I don’t know of anyone doing this, and it may be impossible.
21. OutlineCase-Cohort DesignCHARGE-S & Heart-GO coordinationUltra-deep resequencingA worry