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HERITAGE QTL3 Chromosome 13

HERITAGE QTL3 Chromosome 13. July 16 Video Conference. Previous Traits Primary phenotype + ~16 secondary Adjustments Age & Baseline within Sex and Gen Analysis methods FBAT & various QTDT. Current Traits Primary phenotype + ~8 secondary Adjustments

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HERITAGE QTL3 Chromosome 13

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  1. HERITAGEQTL3Chromosome 13 July 16 Video Conference

  2. Previous Traits Primary phenotype + ~16 secondary Adjustments Age & Baseline within Sex and Gen Analysismethods FBAT & various QTDT Current Traits Primary phenotype + ~8 secondary Adjustments Age, Baseline & Wt within Sex and Gen Analysismethods QTDT-orthogonal only Summary RESULTS: Both sets comparable, but quantitatively vary

  3. Analysis Variables • Response Traits • Target • VO260 • Correlated or secondary traits • VO280, VO2MX • WRK60, WRK80, WRKMX • HR50 • Q60 • SV60

  4. Adjustments • Computing / Adjusting the Responses • Change from Post-training to Pre-training is Post – Base difference • Adjusted for • age (polynomial) • respective baseline value • baseline weight • within sex by generation groups • Adjusted in both mean and variance • Final standardization to zero mean & unit variance

  5. Nomenclature (Deltas) • DVO260, DVO280 and DVO2MX (at 60%, 80% and maximum workloads) • “Volume of Oxygen Consumption (or Oxygen uptake)” • Volume of O2 consumed (per time at a given workload) by muscles during exercise • Measured • Absolute (liters O2 per min) or relative (ml O2 per kg wt per min) • During graded exercise test and based on ventilation of inhaled O2 and exhaled CO2 • At MAX: • Aerobic Capacity or Maximal Oxygen Consumption or Maximal Oxygen Uptake • At maximum workload, O2 consumption remains steady despite increases in the workload • Typically HIGHER with increased cardiovascular fitness • DWRK60, DWRK80, DWRKMX (at 60%, 80% and maximum) • “Workload” relative to the maximal workload • As above, maximal workload attained when O2 consumption no longer increases (remains steady) despite increases in the workload • Typically HIGHER with increased cardiovascular fitness • DHR50 (at absolute workload level of 50 Watts) • “Heart Rate” describes frequency of cardiac cycle (# heart beats in 1 minute) (bpm) • Typically LOWER with increased cardiovascular fitness • DSV60 (Stroke volume at 60% of maximum workload) • Amount of blood pumped by heart’s left ventricle in a single beat (typically 2/3rd of blood in chamber) • Typically HIGHER with increased fitness, which in turn can also decrease the HR • DQ60 (Q, Cardiac Output at 60% of maximum workload) • Volume of blood pumped by left ventricle in one minute (calculated as SV x HR)

  6. Methodology • Basic Association Analysis • QTDT Orthogonal Method • 1 Target trait • DVO260 • 8 correlated Delta traits • DVO280 DVO2MX DWRK60 DWRK80 DWRKMX DHR50 DSV60 DQ60 • 9 correlated Baseline traits Not included here • Selection of TARGET signals • Empirical p-values for target (p < 0.001) • Empirical p-values for target (p < 0.01) AND vote counting across 8 correlated traits (% > 25) • Construct regions around above “signals” • Possibly refine using LD (block) structure • In HERITAGE and HapMap • Multi-marker methods in Signal regions • Bayesian Network • Stepwise multiple regression • Construct haplotypes and perform analyses

  7. UNADJUSTED –log(p-values) One signal reaches 4.522 (p = 0.00003) RS9551180 at 24.615548 Mb

  8. Empirical P-values • Using Built-in utility in QTDT • Performed 100,000 permutations for target phenotype • Performed 10,000 permutations for remaining 8 correlated traits (& 9 baseline traits) • Will compare unadjusted and empirical p-values for target • ALL REMAINING slides use EMPIRICAL

  9. Compare Unadjusted and empirical p-values

  10. Clean up the Playing Field • Plot only signals that reach critical levels • VO260 p-values < 0.05, or –log(p) > 1.3 • VO260 p-values < 0.01, or –log(p) > 2.0

  11. Region by Region

  12. Validation: Across Correlated Delta Traits • Summary: VOTE COUNTING METHOD • Count number correlated traits that have “significant” result at each SNP

  13. N SNPs VOTE COUNTING SUMMARY Where are these 11 SNPs that are validated across 2 traits at P < 0.01 Level ? Number of Correlated Traits that are Significant (1-5 shown) at Given P-Value

  14. VOTE COUNTING PLOT 9 SNPs With 1 Trait P < 0.001 1 SNP With 1 Trait P < 0.0001 1 SNP With 1 Trait < 0.00001 5 traits Significant At P < 0.05 For this SNP 11 SNPs With 2 Traits P < 0.01

  15. Top SNPs Target p-value < 0.001 or Target p-value < 0.01 AND at least 25% correlated traits p-value < 0.05

  16. MultiMarker Analyses • Stepwise Regressions • Marker data recoded (0,1,2)

  17. Preliminary S17*S11 S14*S03 S18*S12 S10*S05 S22 S04*S21 S10*S21 S06*S09

  18. Top SNPs S17*S11 S14*S03 S18*S12 S10*S05 S22 S04*S21 S10*S21 S06*S09

  19. To Do • Provide Start-Stop values within these narrowed visual regions for Haploview analysis of LD structures • Using HapMap data • Using HERITAGE data • Use LD structures to hopefully refine (narrow) regions even more • Multimarker analyses • Select markers for input to Bayesian & Stepwise • All SNPs that • Target has p-value of at least 0.001, or • Target p-value is 0.01 and 25% correlated traits p-value of at least 0.05 • Construct HERITAGE haplotypes • Haplotype analysis (associations using haplotypes) • Combine results with previous evidence • Bioinformatics within the above regions that continue to “perform” • Candidate genes • Signals from other studies • Recommendations regarding denser typing

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