heritage qtl3 chromosome 13 july 16 video conference
TRANSCRIPT
HERITAGEHERITAGEQTL3QTL3
Chromosome 13Chromosome 13
July 16 Video ConferenceJuly 16 Video Conference
2
SummarySummaryPreviousPrevious
• TraitsTraits– Primary phenotype + Primary phenotype +
~16 secondary~16 secondary
• AdjustmentsAdjustments– Age & Baseline Age & Baseline
within Sex and Genwithin Sex and Gen
• AnalysisAnalysis methodsmethods– FBAT & various QTDTFBAT & various QTDT
CurrentCurrent
• TraitsTraits– Primary phenotype + Primary phenotype +
~8 secondary~8 secondary
• AdjustmentsAdjustments– Age, Baseline & Wt Age, Baseline & Wt
within Sex and Genwithin Sex and Gen
• AnalysisAnalysis methodsmethods– QTDT-orthogonal onlyQTDT-orthogonal only
RESULTSRESULTS: Both sets comparable, but quantitatively vary: Both sets comparable, but quantitatively vary
3
Analysis VariablesAnalysis Variables
Response TraitsResponse Traits Target
• VO260
Correlated or secondary traits• VO280, VO2MX• WRK60, WRK80, WRKMX• HR50• Q60• SV60
4
AdjustmentsAdjustments
• 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)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)
6
MethodologyMethodology• Basic Association AnalysisBasic Association Analysis
– QTDT Orthogonal MethodQTDT Orthogonal Method• 1 Target trait 1 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 < 0.01) AND
vote counting across 8 correlated traits (% > 25)
• Construct regions around above “signals”Construct regions around above “signals”– Possibly refine using LD (block) structurePossibly 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
7
DVO260
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
21.1
35
21.4
35
21.7
35
22.0
35
22.3
35
22.6
35
22.9
35
23.2
35
23.5
35
23.8
35
24.1
35
24.4
35
24.7
35
25.0
35
25.3
35
25.6
35
25.9
35
26.2
35
26.5
35
26.8
35
27.1
35
27.4
35
27.7
35
28.0
35
28.3
35
28.6
35
28.9
35
Mega Bases
- L
og
P
QTDT Orthogonal Method
UNADJUSTED –log(p-values)UNADJUSTED –log(p-values)
One signal reaches 4.522 (p = 0.00003)One signal reaches 4.522 (p = 0.00003)RS9551180 at 24.615548 MbRS9551180 at 24.615548 Mb
8
Empirical P-valuesEmpirical 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
DVO260
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
21.1
35
21.4
35
21.7
35
22.0
35
22.3
35
22.6
35
22.9
35
23.2
35
23.5
35
23.8
35
24.1
35
24.4
35
24.7
35
25.0
35
25.3
35
25.6
35
25.9
35
26.2
35
26.5
35
26.8
35
27.1
35
27.4
35
27.7
35
28.0
35
28.3
35
28.6
35
28.9
35
Mega Bases
- L
og
P
QTDT Orthogonal Method
Empirical (100,000 Permutations)
Compare Unadjusted and empirical p-valuesCompare 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
1
2
3
4
521
.00
21.2
521
.50
21.7
522
.00
22.2
522
.50
22.7
523
.00
23.2
523
.50
23.7
524
.00
24.2
524
.50
24.7
525
.00
25.2
525
.50
25.7
526
.00
26.2
526
.50
26.7
527
.00
27.2
527
.50
27.7
528
.00
28.2
528
.50
28.7
529
.00
RVO260
12
1
2
3
4
521
.00
21.2
521
.50
21.7
522
.00
22.2
522
.50
22.7
523
.00
23.2
523
.50
23.7
524
.00
24.2
524
.50
24.7
525
.00
25.2
525
.50
25.7
526
.00
26.2
526
.50
26.7
527
.00
27.2
527
.50
27.7
528
.00
28.2
528
.50
28.7
529
.00
RVO260
13
1
2
3
4
521
.00
21.2
521
.50
21.7
522
.00
22.2
522
.50
22.7
523
.00
23.2
523
.50
23.7
524
.00
24.2
524
.50
24.7
525
.00
25.2
525
.50
25.7
526
.00
26.2
526
.50
26.7
527
.00
27.2
527
.50
27.7
528
.00
28.2
528
.50
28.7
529
.00
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
1 3 24 567
14
Region by Region
15
1
2
3
4
521
.695
21.7
20
21.7
45
21.7
70
21.7
95
21.8
20
21.8
45
21.8
70
21.8
95
21.9
20
21.9
45
21.9
70
21.9
95
22.0
20
22.0
45
22.0
70
22.0
95
22.1
20
22.1
45
22.1
70
22.1
95
22.2
20
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 1
1N1.11.2
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
1all 141 95 11 7.80 8.42 0.00030 0.000201N 27 43 10 37.04 19.91 0.00030 0.000201.1 5 11 1 20.00 27.50 0.00110 0.000201.2 20 31 9 45.00 19.38 0.00030 0.00030
16
1
2
3
4
522
.060
22.0
65
22.0
70
22.0
75
22.0
80
22.0
85
22.0
90
22.0
95
22.1
00
22.1
05
22.1
10
22.1
15
22.1
20
22.1
25
22.1
30
22.1
35
22.1
40
22.1
45
22.1
50
22.1
55
22.1
60
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 1N
17
1
2
3
4
524
.480
24.4
8524
.490
24.4
9524
.500
24.5
0524
.510
24.5
1524
.520
24.5
2524
.530
24.5
3524
.540
24.5
4524
.550
24.5
5524
.560
24.5
6524
.570
24.5
7524
.580
24.5
8524
.590
24.5
9524
.600
24.6
0524
.610
24.6
1524
.620
24.6
2524
.630
24.6
3524
.640
24.6
4524
.650
24.6
5524
.660
24.6
6524
.670
24.6
7524
.680
24.6
8524
.690
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 2
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
2all 61 35 3 4.92 7.17 0.00006 0.000902N 15 9 3 20.00 7.50 0.00006 0.01140
2N
18
1
2
3
4
522
.333
22.3
4122
.349
22.3
5722
.365
22.3
7322
.381
22.3
8922
.397
22.4
0522
.413
22.4
2122
.429
22.4
3722
.445
22.4
5322
.461
22.4
6922
.477
22.4
8522
.493
22.5
0122
.509
22.5
1722
.525
22.5
3322
.541
22.5
4922
.557
22.5
6522
.573
22.5
8122
.589
22.5
9722
.605
22.6
1322
.621
22.6
2922
.637
22.6
4522
.653
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 3
3N
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
3all 143 60 4 2.80 5.24 0.001000 0.0029003N 101 51 4 3.96 6.31 0.001000 0.002900
19
1
2
3
4
523
.294
23.3
0423
.314
23.3
2423
.334
23.3
4423
.354
23.3
6423
.374
23.3
8423
.394
23.4
0423
.414
23.4
2423
.434
23.4
4423
.454
23.4
6423
.474
23.4
8423
.494
23.5
0423
.514
23.5
2423
.534
23.5
4423
.554
23.5
6423
.574
23.5
8423
.594
23.6
0423
.614
23.6
2423
.634
23.6
4423
.654
23.6
6423
.674
23.6
8423
.694
23.7
0423
.714
23.7
2423
.734
23.7
4423
.754
23.7
6423
.774
23.7
84
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 4
4N
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
4all 196 52 2 1.02 3.32 0.001200 0.0006004N 77 23 2 2.60 3.73 0.001200 0.000600
20
1
2
3
4
528
.738
28.7
41
28.7
43
28.7
46
28.7
48
28.7
51
28.7
53
28.7
56
28.7
58
28.7
61
28.7
63
28.7
66
28.7
68
28.7
71
28.7
73
28.7
76
28.7
78
28.7
81
28.7
83
28.7
86
28.7
88
28.7
91
28.7
93
28.7
96
28.7
98
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 5
5N
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
5all 27 14 1 3.70 6.48 0.00190 0.000505N 21 12 1 4.76 7.14 0.00190 0.00050
21
1
2
3
4
527
.617
27.6
3227
.647
27.6
6227
.677
27.6
9227
.707
27.7
2227
.737
27.7
5227
.767
27.7
8227
.797
27.8
1227
.827
27.8
4227
.857
27.8
7227
.887
27.9
0227
.917
27.9
3227
.947
27.9
6227
.977
27.9
9228
.007
28.0
2228
.037
28.0
5228
.067
28.0
8228
.097
28.1
1228
.127
28.1
4228
.157
28.1
7228
.187
28.2
02
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 6
6N
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
6all 120 71 2 1.67 7.40 0.00270 0.000906N 102 67 2 1.96 8.21 0.00270 0.00090
22
1
2
3
4
526
.205
26.2
15
26.2
25
26.2
35
26.2
45
26.2
55
26.2
65
26.2
75
26.2
85
26.2
95
26.3
05
26.3
15
26.3
25
26.3
35
26.3
45
26.3
55
26.3
65
26.3
75
26.3
85
26.3
95
26.4
05
26.4
15
26.4
25
26.4
35
26.4
45
26.4
55
26.4
65
26.4
75
26.4
85
26.4
95
26.5
05
26.5
15
26.5
25
26.5
35
26.5
45
RVO260 RVO280 RVO2MX
RWRK60 RWRK80 RWRKMX
RHR50 RQ60 RSV60
Region 7
7N
RegionN
MarkersN Correlated Traits < 0.05
N Targets < 0.01
Percent Target
Percent Correlated
Mininum Target P-value
Minimum Correlated
P-value
7all 143 36 1 0.70 3.15 0.00380 0.002807N 81 22 1 1.23 3.40 0.00380 0.00280
23
Validation: Across Correlated Delta Traits
• Summary: VOTE COUNTING METHOD– Count number correlated traits that have
“significant” result at each SNP
24
300
85
20
5 1
69
11
0 0 0
9
0 0 0 01 0 0 0 01 0 0 0 0
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
1 2 3 4 5
0.01 < p <= 0.05
0.001 < p <= 0.01
0.0001 < p <= 0.001
0.00001 < p <= 0.0001
p <= 0.00001
Interpretation (Empirical P-values):
300 SNPs are significant at the 0.05-level for a single correlated trait 85 SNPs are significant at the 0.05-level for two correlated traits 20 SNPs are significant at the 0.05-level for three correlated traits 5 SNPs are significant at the 0.05-level for four correlated traits 1 SNP is significant at the 0.05-level for five correlated traits 69 SNPs are significant at the 0.01-level for a single correlated trait 11 SNPs are significant at the 0.01-level for two correlated traits
9 SNPs are significant at the 0.001-level for a single correlated trait 1 SNP is significant at the 0.0001-level for a single correlated trait
1 SNP is significant at the 0.00001-level for a single correlated trait
Number of Correlated Traits that are Significant (1-5 shown) at Given P-Value
N SNPs VOTE COUNTING SUMMARY
Where are these 11 SNPs Where are these 11 SNPs that are validated across 2 traits that are validated across 2 traits
at P < 0.01 Level ?at P < 0.01 Level ?
25
0
1
2
3
4
5
621
.135
21.4
35
21.7
35
22.0
35
22.3
35
22.6
35
22.9
35
23.2
35
23.5
35
23.8
35
24.1
35
24.4
35
24.7
35
25.0
35
25.3
35
25.6
35
25.9
35
26.2
35
26.5
35
26.8
35
27.1
35
27.4
35
27.7
35
28.0
35
28.3
35
28.6
35
28.9
35
0.01 < p < 0.05
0.001 < p < 0.01
0.0001 < p < 0.001 (N=9)
0.00001 < p < 0.0001 (N=1)
p < 0.00001 (N=1)
VOTE COUNTING PLOT
5 traitsSignificantAt P < 0.05
For this SNP
9 SNPs With 1 Trait P < 0.0019 SNPs With 1 Trait P < 0.0011 SNP With 1 Trait P < 0.00011 SNP With 1 Trait P < 0.00011 SNP With 1 Trait < 0.000011 SNP With 1 Trait < 0.00001
11 SNPs With 2 Traits P < 0.01
26
Top SNPsSNP# Ord SNP Mb VO260 VO280 VO2MX WRK60 WRK80 WRKMx HR50 Q60 SV60 Region %Corr
Corr <0.05
1 1 RS2503339 21.7023120 0.00710 0.00720 0.01640 0.01850 0.04580 0.28960 0.93760 0.00840 0.14030 1 62.50 52 1 RS9317097 22.0683740 0.00110 0.04420 0.57360 0.00020 0.00570 0.21740 0.19750 0.00270 0.18830 1 50.00 43 1 RS7342452 22.1099440 0.00170 0.23360 0.67410 0.00040 0.02310 0.36960 0.48200 0.01930 0.41030 1 37.50 34 1 RS1889195 22.1162530 0.00400 0.09430 0.05820 0.00120 0.06180 0.01950 0.84960 0.02420 0.18000 1 37.50 35 1 RS9578484 22.1210900 0.00260 0.05710 0.08380 0.02790 0.26910 0.49060 0.82820 0.04550 0.25860 1 25.00 26 1 RS2334946 22.1260600 0.00030 0.02390 0.44940 0.01180 0.18990 0.31010 0.86300 0.04000 0.43580 1 37.50 37 1 RS1034200 22.1266910 0.00040 0.02290 0.42930 0.00840 0.15500 0.20240 0.93280 0.04150 0.43680 1 37.50 38 1 RS2151499 22.1271530 0.00080 0.08410 0.05640 0.01210 0.16870 0.36560 0.42770 0.09220 0.35970 1 12.50 19 1 RS9550855 22.1347920 0.00030 0.01840 0.38520 0.00860 0.17090 0.17510 0.99480 0.04600 0.40930 1 37.50 3
10 1 RS9510292 22.1497370 0.00210 0.01500 0.59050 0.00590 0.05170 0.37720 0.50870 0.13570 0.63540 1 25.00 211 1 RS9506903 22.1501460 0.00560 0.00440 0.27600 0.04370 0.28320 0.29770 0.27240 0.56480 0.50640 1 25.00 2
12 1 RS971206 22.3813300 0.00730 0.03690 0.03280 0.22570 0.24000 0.28630 0.67680 0.01300 0.05000 3 37.50 313 1 RS7328002 22.4495040 0.00320 0.02350 0.00870 0.01890 0.03760 0.40730 0.13660 0.06320 0.45820 3 50.00 414 1 RS7335200 22.5555180 0.00100 0.00600 0.04210 0.00380 0.02920 0.02550 0.19950 0.02670 0.56780 3 75.00 615 1 RS1984163 22.5568640 0.00120 0.01930 0.00550 0.00290 0.06890 0.07530 0.03360 0.10470 0.25790 3 50.00 4
16 1 RS1325670 23.3889210 0.00120 0.00340 0.04300 0.02030 0.00660 0.10910 0.78380 0.54050 0.80440 4 50.00 417 1 RS9553166 23.5657010 0.00500 0.21010 0.48110 0.00790 0.11480 0.52330 0.15150 0.00510 0.03100 4 37.50 3
18 1 RS6650259 24.6102490 0.00570 0.18020 0.66110 0.11300 0.59820 0.91930 0.04580 0.04690 0.45830 2 25.00 219 1 RS9551180 24.6155480 0.00006 0.02740 0.17720 0.03550 0.25430 0.63320 0.03600 0.03640 0.97800 2 50.00 4
20 1 RS17085051 26.4622790 0.00380 0.02790 0.04060 0.06910 0.07120 0.05740 0.54320 0.04150 0.12500 7 37.50 321 1 RS9508005 27.6877940 0.00490 0.00280 0.04070 0.00880 0.01630 0.02130 0.85410 0.21940 0.65000 6 62.50 522 1 RS11840712 28.7874420 0.00190 0.06810 0.07240 0.00050 0.00740 0.03380 0.07150 0.00360 0.09960 5 50.00 4
Target p-value < 0.001 orTarget p-value < 0.01 AND at least 25% correlated traits p-value < 0.05
27
SNP# Ord SNP Mb VO260 VO280 VO2MX WRK60 WRK80 WRKMx HR50 Q60 SV60 Region %CorrCorr
<0.05
23 2 RS912325 21.7049950 0.03760 0.38500 0.01220 0.05870 0.26750 0.18700 0.96460 0.30750 0.76000 1 12.50 124 2 RS1408051 21.7257520 0.02110 0.09220 0.33090 0.03090 0.04330 0.13000 0.82950 0.29830 0.81480 1 25.00 225 2 RS4770283 21.9787910 0.02360 0.06980 0.13530 0.00660 0.06770 0.02220 0.20220 0.06480 0.30880 1 25.00 226 2 RS4770284 21.9826110 0.02120 0.19160 0.99400 0.00230 0.04100 0.21650 0.13990 0.04580 0.39480 1 37.50 327 2 RS4770286 22.0100390 0.02360 0.06980 0.13530 0.00660 0.06770 0.02220 0.20220 0.06480 0.30880 1 25.00 228 2 RS9578482 22.0685360 0.01360 0.00430 0.03830 0.07140 0.08520 0.38240 0.44620 0.93070 0.38760 1 25.00 229 2 RS9510250 22.0717450 0.02850 0.01800 0.05520 0.08250 0.12190 0.36060 0.43560 0.50650 0.25690 1 12.50 130 2 RS4436648 22.1182040 0.01760 0.60590 0.97150 0.01780 0.37520 0.66810 0.43090 0.00030 0.04240 1 37.50 331 2 RS9510267 22.1200210 0.03360 0.04240 0.80580 0.03870 0.23950 0.11120 0.66410 0.08920 0.28990 1 25.00 232 2 RS17266994 22.1550440 0.01030 0.01820 0.12080 0.00580 0.04460 0.22580 0.29660 0.12420 0.50110 1 37.50 333 2 RS9580461 22.3521120 0.04900 0.30620 0.04060 0.14520 0.14050 0.87720 0.76370 0.03270 0.37940 3 25.00 234 2 RS9510428 22.3973330 0.01690 0.06160 0.08450 0.22760 0.37610 0.38100 0.31250 0.04860 0.14250 3 12.50 135 2 RS9580491 22.4487230 0.01170 0.10190 0.01540 0.06150 0.19560 0.84760 0.05050 0.01680 0.15250 3 25.00 236 2 RS9580513 22.4887020 0.01110 0.00490 0.01180 0.02990 0.01730 0.17450 0.92660 0.33100 0.96060 3 50.00 437 2 RS6490774 22.5293400 0.02880 0.31440 0.12450 0.13880 0.29450 0.45890 0.21630 0.02690 0.48890 3 12.50 138 2 RS17078161 22.5482980 0.04720 0.01060 0.27460 0.24870 0.22750 0.89530 0.67910 0.07770 0.97680 3 12.50 139 2 RS9510573 22.5789900 0.03180 0.07240 0.13950 0.03610 0.20210 0.03600 0.69980 0.80850 0.13850 3 25.00 240 2 RS7319068 22.5796610 0.01090 0.07050 0.30080 0.00970 0.06920 0.04110 0.45230 0.59540 0.18770 3 25.00 241 2 RS4770391 22.5843360 0.02230 0.01350 0.18180 0.13450 0.17360 0.16830 0.22170 0.36790 0.82360 3 12.50 142 2 RS7323735 22.9300010 0.04520 0.02490 0.02080 0.32880 0.67690 0.97710 0.78610 0.26970 0.08280 0 25.00 243 2 RS17330522 22.9622690 0.02730 0.04390 0.21260 0.13090 0.21130 0.84800 0.71480 0.82280 0.45060 0 12.50 144 2 RS12873773 22.9922800 0.02840 0.00580 0.13370 0.01240 0.01880 0.47550 0.56840 0.18930 0.89350 0 37.50 345 2 RS6490808 23.0647290 0.03820 0.00010 0.63860 0.01310 0.01700 0.53850 0.53700 0.80480 0.51140 0 37.50 346 2 RS7986433 23.0998830 0.04410 0.00520 0.57740 0.01280 0.03270 0.69770 0.71200 0.75430 0.66530 0 37.50 347 2 RS7987909 23.1068290 0.04410 0.00520 0.57740 0.01280 0.03270 0.69770 0.71200 0.75430 0.66530 0 37.50 348 2 RS9553204 23.6367460 0.02920 0.43030 0.33330 0.04630 0.54500 0.39010 0.54040 0.05780 0.52740 4 12.50 149 2 RS11617616 23.7051850 0.04720 0.07140 0.95120 0.02600 0.08930 0.12640 0.48870 0.23430 0.56170 4 12.50 150 2 RS9551084 23.7121070 0.01450 0.14690 0.37160 0.02930 0.27540 0.15960 0.52780 0.05370 0.53870 4 12.50 151 2 RS1220593 23.7137400 0.04620 0.18670 0.38640 0.13060 0.30040 0.61550 0.78950 0.04460 0.28800 4 12.50 152 2 RS7334443 24.1325210 0.04470 0.07630 0.00270 0.13250 0.29080 0.33310 0.67120 0.22740 0.31190 0 12.50 153 2 RS17397030 24.1661070 0.04340 0.01210 0.50660 0.09790 0.09520 0.41300 0.25080 0.00290 0.00670 0 37.50 354 2 RS4770771 24.4851210 0.01600 0.05970 0.50150 0.02820 0.02030 0.31700 0.11920 0.21340 0.54550 2 25.00 255 2 RS2487591 24.5032690 0.04280 0.03270 0.45090 0.09860 0.09610 0.07400 0.93120 0.04640 0.32890 2 25.00 256 2 RS9581240 24.5214540 0.02390 0.01060 0.08390 0.03240 0.00090 0.09740 0.38320 0.27380 0.55210 2 37.50 357 2 RS7336509 24.5783970 0.03550 0.03520 0.27590 0.32210 0.12400 0.58220 0.17250 0.46640 0.72660 2 12.50 158 2 RS9581268 24.5924350 0.04120 0.18490 0.62690 0.09660 0.04850 0.15560 0.59270 0.93920 0.40320 2 12.50 159 2 RS17081845 24.6145000 0.02720 0.42290 0.47940 0.29630 0.39130 0.74960 0.04310 0.55130 0.69710 2 12.50 160 2 RS1960061 25.5683740 0.01760 0.02430 0.60190 0.03370 0.08930 0.15130 0.80710 0.01080 0.15130 0 37.50 361 2 RS474535 26.0607670 0.04280 0.10210 0.38500 0.01130 0.04980 0.20930 0.91490 0.01590 0.17980 0 37.50 362 2 RS9507769 26.1710930 0.03370 0.09640 0.03300 0.40640 0.99120 0.38880 0.90840 0.03290 0.14970 0 25.00 263 2 RS521643 26.3427750 0.02940 0.00280 0.07730 0.01420 0.00980 0.05170 0.91960 0.65640 0.48460 7 37.50 364 2 RS9551338 26.3622510 0.01970 0.03950 0.09980 0.02070 0.02830 0.06520 0.23760 0.61490 0.62130 7 37.50 365 2 RS1576168 26.4002790 0.01710 0.01410 0.15900 0.05880 0.04570 0.01710 0.31230 0.96630 0.05740 7 37.50 366 2 RS9652090 26.8813670 0.04540 0.00750 0.26960 0.19150 0.25960 0.10710 0.64850 0.52290 0.52280 0 12.50 167 2 RS9512730 26.9751440 0.01300 0.04020 0.16960 0.06420 0.23810 0.05040 0.79950 0.08450 0.08420 0 12.50 168 2 RS1218894 26.9819050 0.01960 0.03260 0.18330 0.10020 0.25780 0.05150 0.87840 0.10710 0.04470 0 25.00 269 2 RS3751395 27.8569550 0.02580 0.68480 0.12020 0.03430 0.83770 0.09780 0.94970 0.47900 0.77490 6 12.50 170 2 RS4771249 27.9114140 0.02220 0.13510 0.04030 0.00780 0.15730 0.15340 0.57670 0.02090 0.06100 6 37.50 371 2 RS1886323 28.0322250 0.01520 0.21270 0.08420 0.02400 0.25030 0.56990 0.38190 0.02150 0.04760 6 37.50 372 2 RS12428170 28.0698900 0.02510 0.56070 0.58440 0.13220 0.09730 0.20110 0.49060 0.01620 0.12550 6 12.50 173 2 RS974527 28.1014930 0.00270 0.26680 0.99980 0.03230 0.15530 0.40560 0.51840 0.05010 0.19460 6 12.50 1
28
SNP# Ord SNP Mb VO260 VO280 VO2MX WRK60 WRK80 WRKMx HR50 Q60 SV60 Region %CorrCorr
<0.05
74 2 RS4769714 28.7842590 0.03920 0.08140 0.00920 0.05180 0.06260 0.10830 0.84240 0.09370 0.30760 5 12.50 175 3 RS7983498 21.7131150 0.04610 0.10480 0.73690 0.10720 0.21200 0.07080 0.59400 0.70220 0.61400 1 0.00 076 3 RS4640030 21.9469930 0.02900 0.27490 0.49490 0.18800 0.37200 0.84150 0.19880 0.09820 0.97640 1 0.00 077 3 RS2042708 22.1469080 0.02760 0.56280 0.06320 0.05380 0.26150 0.75590 0.69150 0.23180 0.87730 1 0.00 078 3 RS9552766 22.3782670 0.01140 0.26720 0.11960 0.07330 0.26980 0.51370 0.77690 0.28090 0.38000 3 0.00 079 3 RS9506969 22.3949970 0.01360 0.28380 0.11060 0.05500 0.28500 0.30530 0.83550 0.33630 0.43230 3 0.00 080 3 RS1926845 22.4848090 0.04720 0.11840 0.31150 0.39500 0.73360 0.27880 0.44880 0.59040 0.62160 3 0.00 081 3 RS7337589 22.5067180 0.03580 notest notest 0.07660 notest notest 0.23860 0.41520 0.81130 3 0.00 082 3 RS4770474 23.1533840 0.04430 0.16040 0.66690 0.15230 0.39460 0.93690 0.36200 0.85600 0.13740 0 0.00 083 3 RS9578659 23.4010180 0.04620 0.16840 0.65670 0.39080 0.52380 0.89500 0.67740 0.31900 0.98100 4 0.00 084 3 RS2765114 23.4261640 0.01550 0.28930 0.13540 0.12610 0.22160 0.68560 0.09830 0.76310 0.46410 4 0.00 085 3 RS12018351 24.1063020 0.04390 0.22620 0.18330 0.21920 0.42960 0.98810 0.67360 0.05660 0.22350 0 0.00 086 3 RS17081865 24.6246490 0.00610 0.17200 0.42360 0.06050 0.05510 0.46070 0.54470 0.24290 0.81570 2 0.00 087 3 RS9553542 24.6264520 0.03270 0.25090 0.12970 0.14620 0.86370 0.74350 0.83800 0.11750 0.32900 2 0.00 088 3 RS754100 25.4364550 0.03840 0.27970 0.22960 0.14040 0.98570 0.75030 0.55850 0.10460 0.41120 0 0.00 089 3 RS813222 26.0761680 0.04370 0.93190 0.12920 0.05940 0.19710 0.23840 0.44820 0.22850 0.72730 0 0.00 090 3 RS17566649 26.3272020 0.02650 0.17920 0.18120 0.21930 0.56190 0.24170 0.42040 0.13240 0.80790 7 0.00 091 3 RS3892442 26.3746670 0.04250 0.16900 0.09670 0.16590 0.21470 0.27840 0.84780 0.41530 0.98460 7 0.00 092 3 RS17085040 26.4593180 0.04780 0.16530 0.69080 0.15380 0.07010 0.11890 0.48770 0.45920 0.88290 7 0.00 093 3 RS12429729 26.4665860 0.02900 0.15790 0.16540 0.08470 0.48860 0.41590 0.57580 0.83930 0.28300 7 0.00 094 3 RS9553949 26.4732870 0.04880 0.34760 0.57100 0.21570 0.75020 0.80540 0.23940 0.14310 0.86490 7 0.00 095 3 RS17523405 26.7776070 0.04590 0.92820 0.14310 0.05280 0.39680 0.09310 0.66060 0.36850 0.59360 0 0.00 096 3 RS7317833 27.3812730 0.01700 0.10200 0.91730 0.09330 0.31760 0.35400 0.45390 0.67610 0.81670 0 0.00 097 3 RS4581569 27.3956210 0.02390 0.15170 0.96410 0.10410 0.39380 0.47520 0.39400 0.87750 0.58030 0 0.00 098 3 RS1609806 28.0993980 0.01200 0.69800 0.92730 0.09600 0.48770 0.28970 0.21330 0.06710 0.18310 6 0.00 0
29
MultiMarker Analyses
• Stepwise Regressions– Marker data recoded (0,1,2)
30
S17*S11S14*S03S18*S12S10*S05S22S04*S21S10*S21S06*S09
Preliminary
31
Top SNPs
SNP# Ord SNP Mb VO260 VO280 VO2MX WRK60 WRK80 WRKMx HR50 Q60 SV60 Region %CorrCorr
<0.05
1 1 RS2503339 21.7023120 0.00710 0.00720 0.01640 0.01850 0.04580 0.28960 0.93760 0.00840 0.14030 1 62.50 52 1 RS9317097 22.0683740 0.00110 0.04420 0.57360 0.00020 0.00570 0.21740 0.19750 0.00270 0.18830 1 50.00 43 1 RS7342452 22.1099440 0.00170 0.23360 0.67410 0.00040 0.02310 0.36960 0.48200 0.01930 0.41030 1 37.50 34 1 RS1889195 22.1162530 0.00400 0.09430 0.05820 0.00120 0.06180 0.01950 0.84960 0.02420 0.18000 1 37.50 35 1 RS9578484 22.1210900 0.00260 0.05710 0.08380 0.02790 0.26910 0.49060 0.82820 0.04550 0.25860 1 25.00 26 1 RS2334946 22.1260600 0.00030 0.02390 0.44940 0.01180 0.18990 0.31010 0.86300 0.04000 0.43580 1 37.50 37 1 RS1034200 22.1266910 0.00040 0.02290 0.42930 0.00840 0.15500 0.20240 0.93280 0.04150 0.43680 1 37.50 38 1 RS2151499 22.1271530 0.00080 0.08410 0.05640 0.01210 0.16870 0.36560 0.42770 0.09220 0.35970 1 12.50 19 1 RS9550855 22.1347920 0.00030 0.01840 0.38520 0.00860 0.17090 0.17510 0.99480 0.04600 0.40930 1 37.50 3
10 1 RS9510292 22.1497370 0.00210 0.01500 0.59050 0.00590 0.05170 0.37720 0.50870 0.13570 0.63540 1 25.00 211 1 RS9506903 22.1501460 0.00560 0.00440 0.27600 0.04370 0.28320 0.29770 0.27240 0.56480 0.50640 1 25.00 2
12 1 RS971206 22.3813300 0.00730 0.03690 0.03280 0.22570 0.24000 0.28630 0.67680 0.01300 0.05000 3 37.50 313 1 RS7328002 22.4495040 0.00320 0.02350 0.00870 0.01890 0.03760 0.40730 0.13660 0.06320 0.45820 3 50.00 414 1 RS7335200 22.5555180 0.00100 0.00600 0.04210 0.00380 0.02920 0.02550 0.19950 0.02670 0.56780 3 75.00 615 1 RS1984163 22.5568640 0.00120 0.01930 0.00550 0.00290 0.06890 0.07530 0.03360 0.10470 0.25790 3 50.00 4
16 1 RS1325670 23.3889210 0.00120 0.00340 0.04300 0.02030 0.00660 0.10910 0.78380 0.54050 0.80440 4 50.00 417 1 RS9553166 23.5657010 0.00500 0.21010 0.48110 0.00790 0.11480 0.52330 0.15150 0.00510 0.03100 4 37.50 3
18 1 RS6650259 24.6102490 0.00570 0.18020 0.66110 0.11300 0.59820 0.91930 0.04580 0.04690 0.45830 2 25.00 219 1 RS9551180 24.6155480 0.00006 0.02740 0.17720 0.03550 0.25430 0.63320 0.03600 0.03640 0.97800 2 50.00 4
20 1 RS17085051 26.4622790 0.00380 0.02790 0.04060 0.06910 0.07120 0.05740 0.54320 0.04150 0.12500 7 37.50 321 1 RS9508005 27.6877940 0.00490 0.00280 0.04070 0.00880 0.01630 0.02130 0.85410 0.21940 0.65000 6 62.50 522 1 RS11840712 28.7874420 0.00190 0.06810 0.07240 0.00050 0.00740 0.03380 0.07150 0.00360 0.09960 5 50.00 4
S17*S11S14*S03S18*S12S10*S05S22S04*S21S10*S21S06*S09
32
1
2
3
4
521
.00
21.2
521
.50
21.7
522
.00
22.2
522
.50
22.7
523
.00
23.2
523
.50
23.7
524
.00
24.2
524
.50
24.7
525
.00
25.2
525
.50
25.7
526
.00
26.2
526
.50
26.7
527
.00
27.2
527
.50
27.7
528
.00
28.2
528
.50
28.7
529
.00
RVO260
snp2*snp3
snp10*snp6
snp19*snp20
snp21*snp24
snp12*snp13
snp17*snp18
snp12*snp18
33
34
To DoTo 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