fastJT: Compute the Jonckheere-Terpstra Test Statistics
In fastJT: Efficient Jonckheere-Terpstra Test Statistics
Description
Usage
Arguments
Value
Note
References
Examples
Description
A method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. The calculation of the standardized test statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.
Usage
1
Arguments
Y
A matrix of continuous values, representing dependent variables, e.g. marker levels or other observed values. Row names should be sample IDs, and column names should be variable names. Required.
X
A matrix of integer values, representing independent variables, e.g. SNP counts or other classification features. Row names should be sample IDs, and column names should be feature IDs. Required.
outTopN
An integer to indicate the number of top independent variables to be reported for each dependent variable, based on the standardized Jonckheere-Terpstra test statistics. Optional. The default value is 15L. If set to NA, all results are returned.
numThreads
A integer to indicate the number of threads used in the computation. Optional. The default value is 1L (sequential computation).
standardized
A boolean to specify whether to return standardized Jonckheere-Terpstra statistics (TRUE) or non-standardized statistics (FALSE). Optional. The default value is TRUE.
Value
A list with two objects
J
A matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics,
depending on the value of the standardized argument.
XIDs
If outTopN was specified, this object is a matrix of the column IDs of X corresponding to the top standardized Jonckheere-Terpstra test statistics for each dependent variable. Otherwise this is a vector of the column IDs of X.
Note
Rows (samples) are assumed to be in the same order in X and Y.
References
Hollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.
Examples
1
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14
# Generate dummy data
num_sample <- 100
num_marker <- 10
num_SNP <- 500
set.seed(12345)
Mark <- matrix(rnorm(num_sample*num_marker), num_sample, num_marker)
Geno <- matrix(rbinom(num_sample*num_SNP, 2, 0.5), num_sample, num_SNP)
colnames(Mark) <- paste0("Mrk:",1:num_marker)
colnames(Geno) <- paste0("SNP:",1:num_SNP)
res <- fastJT(Y=Mark, X=Geno, outTopN=5)
res
res <- fastJT(Y=Mark, X=Geno, outTopN=NA)
head(res)
Example output
$J
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5 Mrk:6 Mrk:7
[1,] 3.112041 3.034869 -3.144459 -3.136629 -2.955938 -3.113772 3.356456
[2,] -2.981145 3.022007 -3.025032 -2.899474 -2.868004 2.803891 -3.208241
[3,] 2.890056 -2.819824 -2.717341 2.824749 2.748499 2.772179 -2.779656
[4,] -2.825261 2.627437 2.613547 2.754090 2.741551 -2.687242 2.736467
[5,] -2.781120 -2.577385 -2.496440 -2.746955 -2.705899 -2.667335 2.550120
Mrk:8 Mrk:9 Mrk:10
[1,] -3.154481 3.785688 3.519938
[2,] 2.940487 -3.107839 3.199772
[3,] -2.854041 2.765425 2.842571
[4,] -2.683991 -2.746846 -2.826604
[5,] 2.400626 -2.664774 -2.695368
$XIDs
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5 Mrk:6 Mrk:7
[1,] "SNP:452" "SNP:145" "SNP:156" "SNP:190" "SNP:410" "SNP:352" "SNP:245"
[2,] "SNP:153" "SNP:434" "SNP:125" "SNP:127" "SNP:279" "SNP:493" "SNP:28"
[3,] "SNP:66" "SNP:83" "SNP:466" "SNP:174" "SNP:497" "SNP:477" "SNP:174"
[4,] "SNP:44" "SNP:398" "SNP:160" "SNP:420" "SNP:369" "SNP:36" "SNP:131"
[5,] "SNP:396" "SNP:275" "SNP:434" "SNP:124" "SNP:117" "SNP:24" "SNP:466"
Mrk:8 Mrk:9 Mrk:10
[1,] "SNP:475" "SNP:228" "SNP:401"
[2,] "SNP:321" "SNP:145" "SNP:175"
[3,] "SNP:496" "SNP:91" "SNP:367"
[4,] "SNP:389" "SNP:495" "SNP:434"
[5,] "SNP:12" "SNP:116" "SNP:472"
attr(,"class")
[1] "fastJT"
attr(,"outTopN")
[1] 5
attr(,"standardized")
[1] TRUE
$J
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5
SNP:1 -0.585529803 -1.295844646 -0.483142078 -0.521537475 1.219053852
SNP:2 1.706446168 -0.205556613 -0.075044478 0.453529670 -0.447004063
SNP:3 -0.953022364 0.249448807 -0.179091451 -1.048964213 -0.345390655
SNP:4 1.500706773 0.103945058 0.747105104 1.955466402 2.150363385
SNP:5 -2.387837095 -0.397425431 0.009853523 -1.928006018 -0.180647923
SNP:6 0.865080431 1.362178887 0.019367472 0.096837362 -2.440301514
SNP:7 -0.037729763 0.305268080 1.004983679 -1.450880875 -0.298408123
SNP:8 -0.041560139 -0.444373795 2.349746327 0.482737000 1.601663823
SNP:9 -0.240345526 0.003204607 0.016023035 -0.887676141 -0.842811643
SNP:10 -0.205986294 -0.167363864 0.663018383 -0.875441748 0.341164799
SNP:11 -0.376847915 0.576545101 0.357522381 2.000192781 1.014590541
SNP:12 0.699764147 -0.110489076 -1.556891523 1.121631528 0.746638300
SNP:13 0.916247931 0.249297140 1.026327188 -0.320524895 -0.035613877
SNP:14 0.258644092 -0.939631067 -2.072426708 0.834863840 1.077138052
SNP:15 -1.813406518 -0.281332226 -0.491484010 0.511821279 1.230404796
SNP:16 1.812502537 1.137634571 0.102837023 1.253326223 0.392066152
SNP:17 0.387635388 1.432008419 -0.016017991 -0.618294463 0.810510358
SNP:18 0.411402707 0.774214542 -1.661807784 1.208292989 1.733074394
SNP:19 -0.846786383 -0.453980532 0.408904451 0.138447964 -0.009659160
SNP:20 -1.368723327 0.490432480 -0.958426873 -0.310927781 1.195629510
SNP:21 -0.232495277 -0.949355716 0.516656172 -0.846024482 -0.749151450
SNP:22 -0.603066480 0.576411608 0.229898271 2.395606623 -0.476455837
SNP:23 -2.390201171 -2.330529019 0.619927349 -0.301675876 0.818834520
SNP:24 -0.077127768 -0.764850362 0.700577222 1.574691921 -0.668440652
SNP:25 -0.579851284 0.810510358 -0.816917554 1.675481886 -0.868175126
SNP:26 1.211079238 -0.870775981 0.190169467 -0.417038305 0.243550370
SNP:27 0.452223877 -0.211037809 1.430367375 -0.070345936 0.043547484
SNP:28 0.497243364 1.675642020 -0.313331435 0.619851316 1.662018914
SNP:29 -1.094966041 1.133955930 -1.250925596 1.400386836 0.724562099
SNP:30 -1.050538886 0.619718523 -1.574151327 -0.288318243 0.301574254
SNP:31 0.158643635 0.728465669 -0.048564378 0.016188126 -0.650762665
SNP:32 -0.554559148 0.165047365 0.270677679 -0.336696626 0.290483363
SNP:33 0.800129674 0.087463568 0.249433137 0.443796621 -0.871396284
SNP:34 1.339056120 1.692645511 0.572945772 2.504591520 -0.428890835
SNP:35 -0.592593090 -0.132766758 1.913784242 -2.088647777 0.748027343
SNP:36 -0.010039511 -0.244294761 -1.482501082 -0.451777982 -1.127771704
SNP:37 -0.612712148 -0.364948766 -2.105988749 1.503321062 1.349305987
SNP:38 1.022541435 -0.897600681 -0.305776056 0.923903997 0.838418218
SNP:39 1.164275868 1.022369668 -0.299938104 1.261030095 -0.867562904
SNP:40 -0.239456978 -1.631500211 -0.775840609 -0.252228017 1.146200735
SNP:41 0.504829538 0.584018485 0.973364142 -0.234267302 0.042894013
SNP:42 0.389586131 0.080493002 -0.138447964 -1.104363990 -2.038082815
SNP:43 1.156041309 0.783539109 0.867030982 1.323025054 -1.008324919
SNP:44 -2.825261290 0.041595013 -0.771107555 0.329560490 0.310362792
SNP:45 -0.440061939 -0.466732360 0.986805560 1.026811191 -0.353383072
SNP:46 -1.627477780 0.074122750 1.807950563 -1.820841476 -0.048340924
SNP:47 0.563865020 -1.078840009 -0.765943813 -2.512947573 1.509072278
SNP:48 0.189057413 1.111126900 1.044790965 0.799348008 -2.278639344
SNP:49 -2.353752131 1.034579528 -0.324770920 1.878314290 -0.746638300
SNP:50 0.085766086 -0.752102602 -0.125350434 -0.468414779 0.910439992
SNP:51 0.407622344 0.341342288 0.878210741 -0.288318243 0.314830265
SNP:52 0.917328168 1.127754701 1.029117264 1.318453747 -0.844994047
SNP:53 0.089200930 -0.318574750 0.828294350 1.108640130 -0.783693885
SNP:54 -0.370304269 -0.786084500 -0.896526124 0.669146310 -1.156388769
SNP:55 -1.218086560 -0.693678722 1.928360466 -0.554279170 -0.680402574
SNP:56 1.320337558 2.017630742 -0.694064975 -0.345418383 1.720635497
SNP:57 -1.039107521 -1.485390879 -0.133218913 0.293081608 -1.045768466
SNP:58 -0.788684268 1.252237470 -1.619217088 -0.151298615 -1.599902371
SNP:59 -0.826495922 -0.521826837 0.716296466 -1.792361745 -2.032207620
SNP:60 1.709759558 -1.613705651 0.000000000 -1.312736740 -0.672377354
SNP:61 0.026004929 0.845160208 1.410767425 -0.624118308 -1.066202109
SNP:62 1.371582449 -1.204551331 1.063217308 1.178854236 -0.343698646
SNP:63 0.720231018 0.102890145 1.569904476 0.175908958 0.633936057
SNP:64 0.356259127 0.475012170 -0.131947825 0.672933907 -0.950024340
SNP:65 0.864721535 0.923903997 0.489899273 -0.220290277 -2.206190684
SNP:66 2.890056157 0.624252130 1.027208531 1.449982460 -0.492135277
SNP:67 -1.405520305 1.346821082 -0.309801459 -0.557642627 0.172836604
SNP:68 -0.384353749 0.450055244 0.482905992 0.036135823 -0.068986570
SNP:69 1.150219361 1.543291523 -1.556179135 1.620617195 1.614173389
SNP:70 0.204170256 -0.204170256 -0.639287196 0.953910214 -1.375638940
SNP:71 0.244336121 -0.809165076 0.187218586 -0.574348544 0.307800048
SNP:72 -1.240309555 2.064983460 -0.131947825 0.468414779 -1.906646071
SNP:73 0.758309089 2.313322665 0.278366628 1.167859990 -1.052673799
SNP:74 2.053171734 0.003285075 0.364643300 1.291034387 -0.160968664
SNP:75 0.561819540 -0.718448017 -1.344961928 1.331342061 1.058944708
SNP:76 -0.969790209 0.276165424 0.455994072 0.854186078 -1.175308664
SNP:77 -0.663908414 -0.240546527 0.586933525 -0.073767602 0.477885767
SNP:78 -0.964999746 -0.783046932 -0.939006487 0.620589062 -1.224932337
SNP:79 1.169550779 -0.589608244 0.041884739 0.505838766 -0.570276826
SNP:80 0.134664816 0.325166262 1.185707278 1.422191833 -0.075543677
SNP:81 -0.881471348 0.504162579 0.621258404 0.009757985 -1.005072496
SNP:82 -1.370684886 0.016125705 0.628902477 1.267480377 -0.158031905
SNP:83 0.427048552 -2.819824405 0.427048552 -0.629163133 1.737533420
SNP:84 1.055682739 -1.087384923 -0.466022110 -0.320192062 0.339213373
SNP:85 2.263040980 -0.672795967 0.569784144 -0.930325524 -0.273625154
SNP:86 -0.276974860 -0.563725303 0.641929969 -0.107531416 -0.772271080
SNP:87 -0.482493828 -1.641774299 0.398300274 -0.871079460 1.680632862
SNP:88 -0.378924651 -0.905565692 1.310180150 -1.701949705 -2.074451904
SNP:89 -1.286605064 -1.170228726 -0.012930704 1.713318301 1.163763374
SNP:90 -0.168377323 1.458211158 -0.155669601 -0.358993161 -0.003176931
SNP:91 -0.380245909 1.361971710 -1.721476934 -0.062222058 -1.313576777
SNP:92 -1.138845242 0.019635263 0.890131913 -0.222532978 0.746139986
SNP:93 1.438152641 1.134363095 0.830573548 0.559102038 -0.313484958
SNP:94 -0.638740906 -2.391266205 -0.118760872 -0.792809064 0.831326104
SNP:95 0.746392904 -0.799945758 0.525487381 -0.391605246 -1.127956989
SNP:96 -0.653237211 -0.511081963 0.098154814 -0.091385517 0.003384649
SNP:97 1.785441577 -1.772596674 1.509276153 -0.006422452 -0.468838975
SNP:98 -0.566584410 -0.573059660 -1.194683698 1.039277688 -0.229871389
SNP:99 -0.048890031 1.613371010 1.672039047 0.902835899 -0.074964714
SNP:100 -0.887273497 -0.194606455 0.662981312 -1.718473948 0.135234994
SNP:101 1.246211315 -0.787248580 0.022310689 0.551392730 1.163343044
SNP:102 0.249351915 0.690512996 -2.199411765 0.025574555 0.811992134
SNP:103 0.966765064 0.603815722 -0.155078355 1.507889536 -1.065751247
SNP:104 -1.639871115 0.854580722 0.722599143 -2.082009403 -0.023096776
SNP:105 1.110707267 -0.780409479 1.026513714 0.385347419 0.709168780
SNP:106 -0.816268873 0.032136570 0.494903175 -0.295656442 0.437057349
SNP:107 0.016048766 0.420477682 0.227892484 -0.792809064 -0.125180379
SNP:108 -0.031980616 0.812307652 0.940230117 -0.172695328 1.368770375
SNP:109 -1.562476900 -0.531498290 1.703355965 -1.030978610 -0.416233600
SNP:110 0.221637445 -0.337274373 0.208788897 0.838367726 0.619942418
SNP:111 -0.229202852 -0.448721077 -0.777998414 0.203377179 -0.584305863
SNP:112 -0.145376514 0.326289509 -1.237315664 1.650831082 -0.862567317
SNP:113 1.100730579 -0.098279516 -0.642092838 0.910723515 -0.779684160
SNP:114 -0.458635986 0.709400985 0.049493092 0.953566906 -0.478433223
SNP:115 0.833450642 1.140160478 -0.706766144 0.493402780 -0.126684498
SNP:116 -1.548981987 -0.826998857 0.945141551 -1.719632544 -1.476783674
SNP:117 -1.709665514 0.494903175 1.073361432 0.147828221 -2.705899178
SNP:118 0.066018946 -0.739412197 -0.336696626 -1.016691771 -0.330094731
SNP:119 0.746356610 0.983086460 0.410989323 -0.726629122 -1.265847114
SNP:120 0.759114467 -0.864273181 -0.233320897 0.200458799 0.108444924
SNP:121 0.094225564 0.458131192 -0.354158156 -0.152710397 -0.711565469
SNP:122 0.158445471 0.290483363 0.079222735 -0.330094731 0.099028419
SNP:123 -0.839726435 0.227220094 0.780451628 0.418216695 0.582868937
SNP:124 -2.034411227 -2.427308977 0.735850869 -2.746954603 -0.203108159
SNP:125 0.252907093 -0.022991554 -3.025031589 2.111938449 0.463115585
SNP:126 -0.257092558 -1.009088292 0.199246733 0.520612431 0.507757803
SNP:127 1.112664892 0.922857351 0.746139986 -2.899473806 -0.045815613
SNP:128 -0.116382737 0.568612803 1.865449019 -1.453121607 0.488807497
SNP:129 1.816763934 0.019396056 0.103445633 -1.571080555 1.053852389
SNP:130 -1.409135255 -0.217039223 -1.311953513 2.361516324 -0.022675740
SNP:131 0.082126869 0.515756740 0.371213450 0.443485095 -0.909965713
SNP:132 -0.205702657 0.715061618 1.309313739 -0.271005088 -0.734652347
SNP:133 -1.633727584 0.544575861 1.468125568 -0.633746178 0.130570821
SNP:134 -0.100421133 0.372530010 1.292517165 -0.586329842 -2.296728496
SNP:135 0.369904974 -1.064683012 -1.997486859 -0.254108634 0.691561473
SNP:136 1.467444300 0.288305834 -0.152251395 0.437317838 -0.184645309
SNP:137 -1.412793864 -1.765176629 -1.008206244 0.564464985 -0.055467657
SNP:138 -1.717881773 -1.272265824 1.931002444 0.316451905 0.051665617
SNP:139 -0.317881313 0.131647817 0.176600730 0.420630829 -1.647203168
SNP:140 0.390384400 1.761490586 1.228282625 0.339602690 -1.621840882
SNP:141 -0.311645883 -0.838552323 0.298794506 0.780721129 0.093172480
SNP:142 -0.351440605 -0.097064548 0.418381673 0.190782043 -1.783979452
SNP:143 1.424179492 -0.690808903 0.520562159 0.036013734 0.658069144
SNP:144 0.372888894 -0.193103177 1.098690490 1.564801607 0.559333340
SNP:145 -1.077958933 3.034868995 0.043118357 0.082919918 -0.985088624
SNP:146 -1.151960332 0.033293651 -0.499404768 -0.619261913 0.266349210
SNP:147 -1.855573968 -0.675062559 0.831107170 -0.396896077 0.159436886
SNP:148 0.246380608 -0.509186590 0.344932851 0.174108963 -1.291034387
SNP:149 0.929367305 0.113337476 -0.981178723 0.087431767 -0.832220897
SNP:150 -1.769981842 0.474394394 -1.213795311 0.631435297 1.619484310
SNP:151 0.477730984 -0.755331421 -0.406716919 -1.594588555 0.516465929
SNP:152 -0.399298532 -0.974288419 0.616516934 0.130969919 0.105414813
SNP:153 -2.981145318 -0.386566096 0.766580225 -1.369361256 -0.740372354
SNP:154 -2.680554849 1.757794150 1.354898633 -1.055976153 -1.328905374
SNP:155 1.305302089 0.930479826 -1.943157517 1.653821034 -0.029591231
SNP:156 -0.394299938 0.268389033 -3.144459169 0.712390644 0.772032652
SNP:157 -1.985286778 -0.211894319 1.515859362 -1.183348276 -0.466167503
SNP:158 1.870914645 -0.643228944 -0.597517243 -0.982801585 1.093815717
SNP:159 1.181947967 0.702692353 -1.181947967 1.363287929 -0.896985169
SNP:160 -0.306905974 -1.883433505 2.613546665 -0.629964894 -0.597659002
SNP:161 -0.280115648 -0.209281806 -0.827468062 -0.544132695 -0.396025571
SNP:162 0.101493251 -0.101493251 0.245548188 -1.195001183 0.677712999
SNP:163 -1.442989456 -1.585028687 1.507552743 0.332500926 -0.222743339
SNP:164 -1.120017125 -2.000265187 0.292321185 1.448467894 0.594495893
SNP:165 1.941129034 0.640604772 2.327423369 -0.698548923 -1.014022630
SNP:166 -1.362698602 -1.235468757 -0.719853070 0.981009067 -1.576980446
SNP:167 1.743808358 0.657608780 1.429726552 0.003271685 -0.225746298
SNP:168 -1.066202109 -0.637120772 -0.468088731 -1.059700877 -1.833347529
SNP:169 0.667126426 0.467984210 0.428155766 -0.507812653 -1.696027880
SNP:170 1.732714357 0.394386477 -0.109910985 -0.051722817 0.168099154
SNP:171 -0.116247639 0.271244490 -1.214142005 0.846024482 1.181850994
SNP:172 1.421757110 -0.782465857 -1.541624220 -0.462820230 -0.203108159
SNP:173 0.534004271 1.223897988 -0.553905051 0.076286324 -1.217264395
SNP:174 0.009662767 1.323799087 0.209359953 2.824748903 -0.447708207
SNP:175 1.456888805 -0.555165480 -0.312911452 1.147341992 0.514789809
SNP:176 -2.211947700 -0.106250763 -0.511935494 0.132008524 -0.434662212
SNP:177 -1.279423126 0.679995666 0.744450232 -0.679995666 -1.691932345
SNP:178 -0.189928048 0.788684268 -0.247872198 0.074039748 0.022533836
SNP:179 -0.640383778 1.221350505 1.241156188 -0.864848195 -0.165047365
SNP:180 1.885795029 0.548947183 -1.323931441 -0.058123819 -0.968730323
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SNP:462 -0.716000064 -1.237327300 -2.246986376 -0.320055328 0.194672828
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SNP:464 -1.062996579 -0.794045638 -0.518691102 2.010728469 -0.678780948
SNP:465 -0.863974666 -1.192482143 -0.903395563 0.614308983 2.046601585
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SNP:487 0.654708914 0.267989659 -0.946444492 -0.132298692 -1.244964618
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[365] "SNP:365" "SNP:366" "SNP:367" "SNP:368" "SNP:369" "SNP:370" "SNP:371"
[372] "SNP:372" "SNP:373" "SNP:374" "SNP:375" "SNP:376" "SNP:377" "SNP:378"
[379] "SNP:379" "SNP:380" "SNP:381" "SNP:382" "SNP:383" "SNP:384" "SNP:385"
[386] "SNP:386" "SNP:387" "SNP:388" "SNP:389" "SNP:390" "SNP:391" "SNP:392"
[393] "SNP:393" "SNP:394" "SNP:395" "SNP:396" "SNP:397" "SNP:398" "SNP:399"
[400] "SNP:400" "SNP:401" "SNP:402" "SNP:403" "SNP:404" "SNP:405" "SNP:406"
[407] "SNP:407" "SNP:408" "SNP:409" "SNP:410" "SNP:411" "SNP:412" "SNP:413"
[414] "SNP:414" "SNP:415" "SNP:416" "SNP:417" "SNP:418" "SNP:419" "SNP:420"
[421] "SNP:421" "SNP:422" "SNP:423" "SNP:424" "SNP:425" "SNP:426" "SNP:427"
[428] "SNP:428" "SNP:429" "SNP:430" "SNP:431" "SNP:432" "SNP:433" "SNP:434"
[435] "SNP:435" "SNP:436" "SNP:437" "SNP:438" "SNP:439" "SNP:440" "SNP:441"
[442] "SNP:442" "SNP:443" "SNP:444" "SNP:445" "SNP:446" "SNP:447" "SNP:448"
[449] "SNP:449" "SNP:450" "SNP:451" "SNP:452" "SNP:453" "SNP:454" "SNP:455"
[456] "SNP:456" "SNP:457" "SNP:458" "SNP:459" "SNP:460" "SNP:461" "SNP:462"
[463] "SNP:463" "SNP:464" "SNP:465" "SNP:466" "SNP:467" "SNP:468" "SNP:469"
[470] "SNP:470" "SNP:471" "SNP:472" "SNP:473" "SNP:474" "SNP:475" "SNP:476"
[477] "SNP:477" "SNP:478" "SNP:479" "SNP:480" "SNP:481" "SNP:482" "SNP:483"
[484] "SNP:484" "SNP:485" "SNP:486" "SNP:487" "SNP:488" "SNP:489" "SNP:490"
[491] "SNP:491" "SNP:492" "SNP:493" "SNP:494" "SNP:495" "SNP:496" "SNP:497"
[498] "SNP:498" "SNP:499" "SNP:500"
fastJT documentation built on Nov. 10, 2020, 3:52 p.m.
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Description Usage Arguments Value Note References Examples
Description
A method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. The calculation of the standardized test statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.
Usage
1 |
Arguments
Y |
A matrix of continuous values, representing dependent variables, e.g. marker levels or other observed values. Row names should be sample IDs, and column names should be variable names. Required. |
X |
A matrix of integer values, representing independent variables, e.g. SNP counts or other classification features. Row names should be sample IDs, and column names should be feature IDs. Required. |
outTopN |
An integer to indicate the number of top independent variables to be reported for each dependent variable, based on the standardized Jonckheere-Terpstra test statistics. Optional. The default value is |
numThreads |
A integer to indicate the number of threads used in the computation. Optional. The default value is |
standardized |
A boolean to specify whether to return standardized Jonckheere-Terpstra statistics ( |
Value
A list with two objects
J |
A matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics,
depending on the value of the |
XIDs |
If |
Note
Rows (samples) are assumed to be in the same order in X and Y.
References
Hollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Generate dummy data
num_sample <- 100
num_marker <- 10
num_SNP <- 500
set.seed(12345)
Mark <- matrix(rnorm(num_sample*num_marker), num_sample, num_marker)
Geno <- matrix(rbinom(num_sample*num_SNP, 2, 0.5), num_sample, num_SNP)
colnames(Mark) <- paste0("Mrk:",1:num_marker)
colnames(Geno) <- paste0("SNP:",1:num_SNP)
res <- fastJT(Y=Mark, X=Geno, outTopN=5)
res
res <- fastJT(Y=Mark, X=Geno, outTopN=NA)
head(res)
|
Example output
$J
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5 Mrk:6 Mrk:7
[1,] 3.112041 3.034869 -3.144459 -3.136629 -2.955938 -3.113772 3.356456
[2,] -2.981145 3.022007 -3.025032 -2.899474 -2.868004 2.803891 -3.208241
[3,] 2.890056 -2.819824 -2.717341 2.824749 2.748499 2.772179 -2.779656
[4,] -2.825261 2.627437 2.613547 2.754090 2.741551 -2.687242 2.736467
[5,] -2.781120 -2.577385 -2.496440 -2.746955 -2.705899 -2.667335 2.550120
Mrk:8 Mrk:9 Mrk:10
[1,] -3.154481 3.785688 3.519938
[2,] 2.940487 -3.107839 3.199772
[3,] -2.854041 2.765425 2.842571
[4,] -2.683991 -2.746846 -2.826604
[5,] 2.400626 -2.664774 -2.695368
$XIDs
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5 Mrk:6 Mrk:7
[1,] "SNP:452" "SNP:145" "SNP:156" "SNP:190" "SNP:410" "SNP:352" "SNP:245"
[2,] "SNP:153" "SNP:434" "SNP:125" "SNP:127" "SNP:279" "SNP:493" "SNP:28"
[3,] "SNP:66" "SNP:83" "SNP:466" "SNP:174" "SNP:497" "SNP:477" "SNP:174"
[4,] "SNP:44" "SNP:398" "SNP:160" "SNP:420" "SNP:369" "SNP:36" "SNP:131"
[5,] "SNP:396" "SNP:275" "SNP:434" "SNP:124" "SNP:117" "SNP:24" "SNP:466"
Mrk:8 Mrk:9 Mrk:10
[1,] "SNP:475" "SNP:228" "SNP:401"
[2,] "SNP:321" "SNP:145" "SNP:175"
[3,] "SNP:496" "SNP:91" "SNP:367"
[4,] "SNP:389" "SNP:495" "SNP:434"
[5,] "SNP:12" "SNP:116" "SNP:472"
attr(,"class")
[1] "fastJT"
attr(,"outTopN")
[1] 5
attr(,"standardized")
[1] TRUE
$J
Mrk:1 Mrk:2 Mrk:3 Mrk:4 Mrk:5
SNP:1 -0.585529803 -1.295844646 -0.483142078 -0.521537475 1.219053852
SNP:2 1.706446168 -0.205556613 -0.075044478 0.453529670 -0.447004063
SNP:3 -0.953022364 0.249448807 -0.179091451 -1.048964213 -0.345390655
SNP:4 1.500706773 0.103945058 0.747105104 1.955466402 2.150363385
SNP:5 -2.387837095 -0.397425431 0.009853523 -1.928006018 -0.180647923
SNP:6 0.865080431 1.362178887 0.019367472 0.096837362 -2.440301514
SNP:7 -0.037729763 0.305268080 1.004983679 -1.450880875 -0.298408123
SNP:8 -0.041560139 -0.444373795 2.349746327 0.482737000 1.601663823
SNP:9 -0.240345526 0.003204607 0.016023035 -0.887676141 -0.842811643
SNP:10 -0.205986294 -0.167363864 0.663018383 -0.875441748 0.341164799
SNP:11 -0.376847915 0.576545101 0.357522381 2.000192781 1.014590541
SNP:12 0.699764147 -0.110489076 -1.556891523 1.121631528 0.746638300
SNP:13 0.916247931 0.249297140 1.026327188 -0.320524895 -0.035613877
SNP:14 0.258644092 -0.939631067 -2.072426708 0.834863840 1.077138052
SNP:15 -1.813406518 -0.281332226 -0.491484010 0.511821279 1.230404796
SNP:16 1.812502537 1.137634571 0.102837023 1.253326223 0.392066152
SNP:17 0.387635388 1.432008419 -0.016017991 -0.618294463 0.810510358
SNP:18 0.411402707 0.774214542 -1.661807784 1.208292989 1.733074394
SNP:19 -0.846786383 -0.453980532 0.408904451 0.138447964 -0.009659160
SNP:20 -1.368723327 0.490432480 -0.958426873 -0.310927781 1.195629510
SNP:21 -0.232495277 -0.949355716 0.516656172 -0.846024482 -0.749151450
SNP:22 -0.603066480 0.576411608 0.229898271 2.395606623 -0.476455837
SNP:23 -2.390201171 -2.330529019 0.619927349 -0.301675876 0.818834520
SNP:24 -0.077127768 -0.764850362 0.700577222 1.574691921 -0.668440652
SNP:25 -0.579851284 0.810510358 -0.816917554 1.675481886 -0.868175126
SNP:26 1.211079238 -0.870775981 0.190169467 -0.417038305 0.243550370
SNP:27 0.452223877 -0.211037809 1.430367375 -0.070345936 0.043547484
SNP:28 0.497243364 1.675642020 -0.313331435 0.619851316 1.662018914
SNP:29 -1.094966041 1.133955930 -1.250925596 1.400386836 0.724562099
SNP:30 -1.050538886 0.619718523 -1.574151327 -0.288318243 0.301574254
SNP:31 0.158643635 0.728465669 -0.048564378 0.016188126 -0.650762665
SNP:32 -0.554559148 0.165047365 0.270677679 -0.336696626 0.290483363
SNP:33 0.800129674 0.087463568 0.249433137 0.443796621 -0.871396284
SNP:34 1.339056120 1.692645511 0.572945772 2.504591520 -0.428890835
SNP:35 -0.592593090 -0.132766758 1.913784242 -2.088647777 0.748027343
SNP:36 -0.010039511 -0.244294761 -1.482501082 -0.451777982 -1.127771704
SNP:37 -0.612712148 -0.364948766 -2.105988749 1.503321062 1.349305987
SNP:38 1.022541435 -0.897600681 -0.305776056 0.923903997 0.838418218
SNP:39 1.164275868 1.022369668 -0.299938104 1.261030095 -0.867562904
SNP:40 -0.239456978 -1.631500211 -0.775840609 -0.252228017 1.146200735
SNP:41 0.504829538 0.584018485 0.973364142 -0.234267302 0.042894013
SNP:42 0.389586131 0.080493002 -0.138447964 -1.104363990 -2.038082815
SNP:43 1.156041309 0.783539109 0.867030982 1.323025054 -1.008324919
SNP:44 -2.825261290 0.041595013 -0.771107555 0.329560490 0.310362792
SNP:45 -0.440061939 -0.466732360 0.986805560 1.026811191 -0.353383072
SNP:46 -1.627477780 0.074122750 1.807950563 -1.820841476 -0.048340924
SNP:47 0.563865020 -1.078840009 -0.765943813 -2.512947573 1.509072278
SNP:48 0.189057413 1.111126900 1.044790965 0.799348008 -2.278639344
SNP:49 -2.353752131 1.034579528 -0.324770920 1.878314290 -0.746638300
SNP:50 0.085766086 -0.752102602 -0.125350434 -0.468414779 0.910439992
SNP:51 0.407622344 0.341342288 0.878210741 -0.288318243 0.314830265
SNP:52 0.917328168 1.127754701 1.029117264 1.318453747 -0.844994047
SNP:53 0.089200930 -0.318574750 0.828294350 1.108640130 -0.783693885
SNP:54 -0.370304269 -0.786084500 -0.896526124 0.669146310 -1.156388769
SNP:55 -1.218086560 -0.693678722 1.928360466 -0.554279170 -0.680402574
SNP:56 1.320337558 2.017630742 -0.694064975 -0.345418383 1.720635497
SNP:57 -1.039107521 -1.485390879 -0.133218913 0.293081608 -1.045768466
SNP:58 -0.788684268 1.252237470 -1.619217088 -0.151298615 -1.599902371
SNP:59 -0.826495922 -0.521826837 0.716296466 -1.792361745 -2.032207620
SNP:60 1.709759558 -1.613705651 0.000000000 -1.312736740 -0.672377354
SNP:61 0.026004929 0.845160208 1.410767425 -0.624118308 -1.066202109
SNP:62 1.371582449 -1.204551331 1.063217308 1.178854236 -0.343698646
SNP:63 0.720231018 0.102890145 1.569904476 0.175908958 0.633936057
SNP:64 0.356259127 0.475012170 -0.131947825 0.672933907 -0.950024340
SNP:65 0.864721535 0.923903997 0.489899273 -0.220290277 -2.206190684
SNP:66 2.890056157 0.624252130 1.027208531 1.449982460 -0.492135277
SNP:67 -1.405520305 1.346821082 -0.309801459 -0.557642627 0.172836604
SNP:68 -0.384353749 0.450055244 0.482905992 0.036135823 -0.068986570
SNP:69 1.150219361 1.543291523 -1.556179135 1.620617195 1.614173389
SNP:70 0.204170256 -0.204170256 -0.639287196 0.953910214 -1.375638940
SNP:71 0.244336121 -0.809165076 0.187218586 -0.574348544 0.307800048
SNP:72 -1.240309555 2.064983460 -0.131947825 0.468414779 -1.906646071
SNP:73 0.758309089 2.313322665 0.278366628 1.167859990 -1.052673799
SNP:74 2.053171734 0.003285075 0.364643300 1.291034387 -0.160968664
SNP:75 0.561819540 -0.718448017 -1.344961928 1.331342061 1.058944708
SNP:76 -0.969790209 0.276165424 0.455994072 0.854186078 -1.175308664
SNP:77 -0.663908414 -0.240546527 0.586933525 -0.073767602 0.477885767
SNP:78 -0.964999746 -0.783046932 -0.939006487 0.620589062 -1.224932337
SNP:79 1.169550779 -0.589608244 0.041884739 0.505838766 -0.570276826
SNP:80 0.134664816 0.325166262 1.185707278 1.422191833 -0.075543677
SNP:81 -0.881471348 0.504162579 0.621258404 0.009757985 -1.005072496
SNP:82 -1.370684886 0.016125705 0.628902477 1.267480377 -0.158031905
SNP:83 0.427048552 -2.819824405 0.427048552 -0.629163133 1.737533420
SNP:84 1.055682739 -1.087384923 -0.466022110 -0.320192062 0.339213373
SNP:85 2.263040980 -0.672795967 0.569784144 -0.930325524 -0.273625154
SNP:86 -0.276974860 -0.563725303 0.641929969 -0.107531416 -0.772271080
SNP:87 -0.482493828 -1.641774299 0.398300274 -0.871079460 1.680632862
SNP:88 -0.378924651 -0.905565692 1.310180150 -1.701949705 -2.074451904
SNP:89 -1.286605064 -1.170228726 -0.012930704 1.713318301 1.163763374
SNP:90 -0.168377323 1.458211158 -0.155669601 -0.358993161 -0.003176931
SNP:91 -0.380245909 1.361971710 -1.721476934 -0.062222058 -1.313576777
SNP:92 -1.138845242 0.019635263 0.890131913 -0.222532978 0.746139986
SNP:93 1.438152641 1.134363095 0.830573548 0.559102038 -0.313484958
SNP:94 -0.638740906 -2.391266205 -0.118760872 -0.792809064 0.831326104
SNP:95 0.746392904 -0.799945758 0.525487381 -0.391605246 -1.127956989
SNP:96 -0.653237211 -0.511081963 0.098154814 -0.091385517 0.003384649
SNP:97 1.785441577 -1.772596674 1.509276153 -0.006422452 -0.468838975
SNP:98 -0.566584410 -0.573059660 -1.194683698 1.039277688 -0.229871389
SNP:99 -0.048890031 1.613371010 1.672039047 0.902835899 -0.074964714
SNP:100 -0.887273497 -0.194606455 0.662981312 -1.718473948 0.135234994
SNP:101 1.246211315 -0.787248580 0.022310689 0.551392730 1.163343044
SNP:102 0.249351915 0.690512996 -2.199411765 0.025574555 0.811992134
SNP:103 0.966765064 0.603815722 -0.155078355 1.507889536 -1.065751247
SNP:104 -1.639871115 0.854580722 0.722599143 -2.082009403 -0.023096776
SNP:105 1.110707267 -0.780409479 1.026513714 0.385347419 0.709168780
SNP:106 -0.816268873 0.032136570 0.494903175 -0.295656442 0.437057349
SNP:107 0.016048766 0.420477682 0.227892484 -0.792809064 -0.125180379
SNP:108 -0.031980616 0.812307652 0.940230117 -0.172695328 1.368770375
SNP:109 -1.562476900 -0.531498290 1.703355965 -1.030978610 -0.416233600
SNP:110 0.221637445 -0.337274373 0.208788897 0.838367726 0.619942418
SNP:111 -0.229202852 -0.448721077 -0.777998414 0.203377179 -0.584305863
SNP:112 -0.145376514 0.326289509 -1.237315664 1.650831082 -0.862567317
SNP:113 1.100730579 -0.098279516 -0.642092838 0.910723515 -0.779684160
SNP:114 -0.458635986 0.709400985 0.049493092 0.953566906 -0.478433223
SNP:115 0.833450642 1.140160478 -0.706766144 0.493402780 -0.126684498
SNP:116 -1.548981987 -0.826998857 0.945141551 -1.719632544 -1.476783674
SNP:117 -1.709665514 0.494903175 1.073361432 0.147828221 -2.705899178
SNP:118 0.066018946 -0.739412197 -0.336696626 -1.016691771 -0.330094731
SNP:119 0.746356610 0.983086460 0.410989323 -0.726629122 -1.265847114
SNP:120 0.759114467 -0.864273181 -0.233320897 0.200458799 0.108444924
SNP:121 0.094225564 0.458131192 -0.354158156 -0.152710397 -0.711565469
SNP:122 0.158445471 0.290483363 0.079222735 -0.330094731 0.099028419
SNP:123 -0.839726435 0.227220094 0.780451628 0.418216695 0.582868937
SNP:124 -2.034411227 -2.427308977 0.735850869 -2.746954603 -0.203108159
SNP:125 0.252907093 -0.022991554 -3.025031589 2.111938449 0.463115585
SNP:126 -0.257092558 -1.009088292 0.199246733 0.520612431 0.507757803
SNP:127 1.112664892 0.922857351 0.746139986 -2.899473806 -0.045815613
SNP:128 -0.116382737 0.568612803 1.865449019 -1.453121607 0.488807497
SNP:129 1.816763934 0.019396056 0.103445633 -1.571080555 1.053852389
SNP:130 -1.409135255 -0.217039223 -1.311953513 2.361516324 -0.022675740
SNP:131 0.082126869 0.515756740 0.371213450 0.443485095 -0.909965713
SNP:132 -0.205702657 0.715061618 1.309313739 -0.271005088 -0.734652347
SNP:133 -1.633727584 0.544575861 1.468125568 -0.633746178 0.130570821
SNP:134 -0.100421133 0.372530010 1.292517165 -0.586329842 -2.296728496
SNP:135 0.369904974 -1.064683012 -1.997486859 -0.254108634 0.691561473
SNP:136 1.467444300 0.288305834 -0.152251395 0.437317838 -0.184645309
SNP:137 -1.412793864 -1.765176629 -1.008206244 0.564464985 -0.055467657
SNP:138 -1.717881773 -1.272265824 1.931002444 0.316451905 0.051665617
SNP:139 -0.317881313 0.131647817 0.176600730 0.420630829 -1.647203168
SNP:140 0.390384400 1.761490586 1.228282625 0.339602690 -1.621840882
SNP:141 -0.311645883 -0.838552323 0.298794506 0.780721129 0.093172480
SNP:142 -0.351440605 -0.097064548 0.418381673 0.190782043 -1.783979452
SNP:143 1.424179492 -0.690808903 0.520562159 0.036013734 0.658069144
SNP:144 0.372888894 -0.193103177 1.098690490 1.564801607 0.559333340
SNP:145 -1.077958933 3.034868995 0.043118357 0.082919918 -0.985088624
SNP:146 -1.151960332 0.033293651 -0.499404768 -0.619261913 0.266349210
SNP:147 -1.855573968 -0.675062559 0.831107170 -0.396896077 0.159436886
SNP:148 0.246380608 -0.509186590 0.344932851 0.174108963 -1.291034387
SNP:149 0.929367305 0.113337476 -0.981178723 0.087431767 -0.832220897
SNP:150 -1.769981842 0.474394394 -1.213795311 0.631435297 1.619484310
SNP:151 0.477730984 -0.755331421 -0.406716919 -1.594588555 0.516465929
SNP:152 -0.399298532 -0.974288419 0.616516934 0.130969919 0.105414813
SNP:153 -2.981145318 -0.386566096 0.766580225 -1.369361256 -0.740372354
SNP:154 -2.680554849 1.757794150 1.354898633 -1.055976153 -1.328905374
SNP:155 1.305302089 0.930479826 -1.943157517 1.653821034 -0.029591231
SNP:156 -0.394299938 0.268389033 -3.144459169 0.712390644 0.772032652
SNP:157 -1.985286778 -0.211894319 1.515859362 -1.183348276 -0.466167503
SNP:158 1.870914645 -0.643228944 -0.597517243 -0.982801585 1.093815717
SNP:159 1.181947967 0.702692353 -1.181947967 1.363287929 -0.896985169
SNP:160 -0.306905974 -1.883433505 2.613546665 -0.629964894 -0.597659002
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SNP:462 -0.716000064 -1.237327300 -2.246986376 -0.320055328 0.194672828
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SNP:464 -1.062996579 -0.794045638 -0.518691102 2.010728469 -0.678780948
SNP:465 -0.863974666 -1.192482143 -0.903395563 0.614308983 2.046601585
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SNP:487 0.654708914 0.267989659 -0.946444492 -0.132298692 -1.244964618
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SNP:500 -1.039844637 -1.545189694 0.482669317 0.754778194 0.042112088
$XIDs
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[8] "SNP:8" "SNP:9" "SNP:10" "SNP:11" "SNP:12" "SNP:13" "SNP:14"
[15] "SNP:15" "SNP:16" "SNP:17" "SNP:18" "SNP:19" "SNP:20" "SNP:21"
[22] "SNP:22" "SNP:23" "SNP:24" "SNP:25" "SNP:26" "SNP:27" "SNP:28"
[29] "SNP:29" "SNP:30" "SNP:31" "SNP:32" "SNP:33" "SNP:34" "SNP:35"
[36] "SNP:36" "SNP:37" "SNP:38" "SNP:39" "SNP:40" "SNP:41" "SNP:42"
[43] "SNP:43" "SNP:44" "SNP:45" "SNP:46" "SNP:47" "SNP:48" "SNP:49"
[50] "SNP:50" "SNP:51" "SNP:52" "SNP:53" "SNP:54" "SNP:55" "SNP:56"
[57] "SNP:57" "SNP:58" "SNP:59" "SNP:60" "SNP:61" "SNP:62" "SNP:63"
[64] "SNP:64" "SNP:65" "SNP:66" "SNP:67" "SNP:68" "SNP:69" "SNP:70"
[71] "SNP:71" "SNP:72" "SNP:73" "SNP:74" "SNP:75" "SNP:76" "SNP:77"
[78] "SNP:78" "SNP:79" "SNP:80" "SNP:81" "SNP:82" "SNP:83" "SNP:84"
[85] "SNP:85" "SNP:86" "SNP:87" "SNP:88" "SNP:89" "SNP:90" "SNP:91"
[92] "SNP:92" "SNP:93" "SNP:94" "SNP:95" "SNP:96" "SNP:97" "SNP:98"
[99] "SNP:99" "SNP:100" "SNP:101" "SNP:102" "SNP:103" "SNP:104" "SNP:105"
[106] "SNP:106" "SNP:107" "SNP:108" "SNP:109" "SNP:110" "SNP:111" "SNP:112"
[113] "SNP:113" "SNP:114" "SNP:115" "SNP:116" "SNP:117" "SNP:118" "SNP:119"
[120] "SNP:120" "SNP:121" "SNP:122" "SNP:123" "SNP:124" "SNP:125" "SNP:126"
[127] "SNP:127" "SNP:128" "SNP:129" "SNP:130" "SNP:131" "SNP:132" "SNP:133"
[134] "SNP:134" "SNP:135" "SNP:136" "SNP:137" "SNP:138" "SNP:139" "SNP:140"
[141] "SNP:141" "SNP:142" "SNP:143" "SNP:144" "SNP:145" "SNP:146" "SNP:147"
[148] "SNP:148" "SNP:149" "SNP:150" "SNP:151" "SNP:152" "SNP:153" "SNP:154"
[155] "SNP:155" "SNP:156" "SNP:157" "SNP:158" "SNP:159" "SNP:160" "SNP:161"
[162] "SNP:162" "SNP:163" "SNP:164" "SNP:165" "SNP:166" "SNP:167" "SNP:168"
[169] "SNP:169" "SNP:170" "SNP:171" "SNP:172" "SNP:173" "SNP:174" "SNP:175"
[176] "SNP:176" "SNP:177" "SNP:178" "SNP:179" "SNP:180" "SNP:181" "SNP:182"
[183] "SNP:183" "SNP:184" "SNP:185" "SNP:186" "SNP:187" "SNP:188" "SNP:189"
[190] "SNP:190" "SNP:191" "SNP:192" "SNP:193" "SNP:194" "SNP:195" "SNP:196"
[197] "SNP:197" "SNP:198" "SNP:199" "SNP:200" "SNP:201" "SNP:202" "SNP:203"
[204] "SNP:204" "SNP:205" "SNP:206" "SNP:207" "SNP:208" "SNP:209" "SNP:210"
[211] "SNP:211" "SNP:212" "SNP:213" "SNP:214" "SNP:215" "SNP:216" "SNP:217"
[218] "SNP:218" "SNP:219" "SNP:220" "SNP:221" "SNP:222" "SNP:223" "SNP:224"
[225] "SNP:225" "SNP:226" "SNP:227" "SNP:228" "SNP:229" "SNP:230" "SNP:231"
[232] "SNP:232" "SNP:233" "SNP:234" "SNP:235" "SNP:236" "SNP:237" "SNP:238"
[239] "SNP:239" "SNP:240" "SNP:241" "SNP:242" "SNP:243" "SNP:244" "SNP:245"
[246] "SNP:246" "SNP:247" "SNP:248" "SNP:249" "SNP:250" "SNP:251" "SNP:252"
[253] "SNP:253" "SNP:254" "SNP:255" "SNP:256" "SNP:257" "SNP:258" "SNP:259"
[260] "SNP:260" "SNP:261" "SNP:262" "SNP:263" "SNP:264" "SNP:265" "SNP:266"
[267] "SNP:267" "SNP:268" "SNP:269" "SNP:270" "SNP:271" "SNP:272" "SNP:273"
[274] "SNP:274" "SNP:275" "SNP:276" "SNP:277" "SNP:278" "SNP:279" "SNP:280"
[281] "SNP:281" "SNP:282" "SNP:283" "SNP:284" "SNP:285" "SNP:286" "SNP:287"
[288] "SNP:288" "SNP:289" "SNP:290" "SNP:291" "SNP:292" "SNP:293" "SNP:294"
[295] "SNP:295" "SNP:296" "SNP:297" "SNP:298" "SNP:299" "SNP:300" "SNP:301"
[302] "SNP:302" "SNP:303" "SNP:304" "SNP:305" "SNP:306" "SNP:307" "SNP:308"
[309] "SNP:309" "SNP:310" "SNP:311" "SNP:312" "SNP:313" "SNP:314" "SNP:315"
[316] "SNP:316" "SNP:317" "SNP:318" "SNP:319" "SNP:320" "SNP:321" "SNP:322"
[323] "SNP:323" "SNP:324" "SNP:325" "SNP:326" "SNP:327" "SNP:328" "SNP:329"
[330] "SNP:330" "SNP:331" "SNP:332" "SNP:333" "SNP:334" "SNP:335" "SNP:336"
[337] "SNP:337" "SNP:338" "SNP:339" "SNP:340" "SNP:341" "SNP:342" "SNP:343"
[344] "SNP:344" "SNP:345" "SNP:346" "SNP:347" "SNP:348" "SNP:349" "SNP:350"
[351] "SNP:351" "SNP:352" "SNP:353" "SNP:354" "SNP:355" "SNP:356" "SNP:357"
[358] "SNP:358" "SNP:359" "SNP:360" "SNP:361" "SNP:362" "SNP:363" "SNP:364"
[365] "SNP:365" "SNP:366" "SNP:367" "SNP:368" "SNP:369" "SNP:370" "SNP:371"
[372] "SNP:372" "SNP:373" "SNP:374" "SNP:375" "SNP:376" "SNP:377" "SNP:378"
[379] "SNP:379" "SNP:380" "SNP:381" "SNP:382" "SNP:383" "SNP:384" "SNP:385"
[386] "SNP:386" "SNP:387" "SNP:388" "SNP:389" "SNP:390" "SNP:391" "SNP:392"
[393] "SNP:393" "SNP:394" "SNP:395" "SNP:396" "SNP:397" "SNP:398" "SNP:399"
[400] "SNP:400" "SNP:401" "SNP:402" "SNP:403" "SNP:404" "SNP:405" "SNP:406"
[407] "SNP:407" "SNP:408" "SNP:409" "SNP:410" "SNP:411" "SNP:412" "SNP:413"
[414] "SNP:414" "SNP:415" "SNP:416" "SNP:417" "SNP:418" "SNP:419" "SNP:420"
[421] "SNP:421" "SNP:422" "SNP:423" "SNP:424" "SNP:425" "SNP:426" "SNP:427"
[428] "SNP:428" "SNP:429" "SNP:430" "SNP:431" "SNP:432" "SNP:433" "SNP:434"
[435] "SNP:435" "SNP:436" "SNP:437" "SNP:438" "SNP:439" "SNP:440" "SNP:441"
[442] "SNP:442" "SNP:443" "SNP:444" "SNP:445" "SNP:446" "SNP:447" "SNP:448"
[449] "SNP:449" "SNP:450" "SNP:451" "SNP:452" "SNP:453" "SNP:454" "SNP:455"
[456] "SNP:456" "SNP:457" "SNP:458" "SNP:459" "SNP:460" "SNP:461" "SNP:462"
[463] "SNP:463" "SNP:464" "SNP:465" "SNP:466" "SNP:467" "SNP:468" "SNP:469"
[470] "SNP:470" "SNP:471" "SNP:472" "SNP:473" "SNP:474" "SNP:475" "SNP:476"
[477] "SNP:477" "SNP:478" "SNP:479" "SNP:480" "SNP:481" "SNP:482" "SNP:483"
[484] "SNP:484" "SNP:485" "SNP:486" "SNP:487" "SNP:488" "SNP:489" "SNP:490"
[491] "SNP:491" "SNP:492" "SNP:493" "SNP:494" "SNP:495" "SNP:496" "SNP:497"
[498] "SNP:498" "SNP:499" "SNP:500"
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