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Published byEduardo Sowden Modified about 1 year ago

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HERITAGE QTL3 Chromosome 13 July 16 Video Conference

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2 Summary Previous TraitsTraits –Primary phenotype + ~16 secondary AdjustmentsAdjustments –Age & Baseline within Sex and Gen Analysis methodsAnalysis methods –FBAT & various QTDT Current TraitsTraits –Primary phenotype + ~8 secondary AdjustmentsAdjustments –Age, Baseline & Wt within Sex and Gen Analysis methodsAnalysis methods –QTDT-orthogonal only RESULTS: Both sets comparable, but quantitatively vary

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

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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

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5 Nomenclature (Deltas) DVO260, DVO280 and DVO2MX (at 60%, 80% and maximum workloads)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)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)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)DSV60 (Stroke volume at 60% of maximum workload) –Amount of blood pumped by heart’s left ventricle in a single beat (typically 2/3 rd 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)DQ60 (Q, Cardiac Output at 60% of maximum workload) –Volume of blood pumped by left ventricle in one minute (calculated as SV x HR)

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6 Methodology Basic Association AnalysisBasic Association Analysis –QTDT Orthogonal Method 1 Target trait1 Target trait –DVO260 8 correlated Delta traits8 correlated Delta traits –DVO280 DVO2MX DWRK60 DWRK80 DWRKMX DHR50 DSV60 DQ60 9 correlated Baseline traits Not included here9 correlated Baseline traits Not included here Selection of TARGET signalsSelection of TARGET signals –Empirical p-values for target (p < 0.001) –Empirical p-values for target (p 25) Construct regions around above “signals”Construct regions around above “signals” –Possibly refine using LD (block) structure In HERITAGE and HapMapIn HERITAGE and HapMap Multi-marker methods in Signal regionsMulti-marker methods in Signal regions –Bayesian Network –Stepwise multiple regression –Construct haplotypes and perform analyses

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7 UNADJUSTED –log(p-values) One signal reaches (p = ) RS at Mb

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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

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9 Compare Unadjusted and empirical p-values

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10 Clean up the Playing Field Plot only signals that reach critical levels –VO260 p-values 1.3 –VO260 p-values 2.0

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14 Region by Region

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

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

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25 VOTE COUNTING PLOT 5 traits Significant At P < 0.05 For this SNP 9 SNPs With 1 Trait P < SNP With 1 Trait P < SNP With 1 Trait < SNPs With 2 Traits P < 0.01

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

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29 MultiMarker Analyses Stepwise Regressions –Marker data recoded (0,1,2)

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

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

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34 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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