table_GWAS_Results()
The function table_GWAS_Results() creates a table of
GWAS results.
Specifying a folder is all that is required to produce a
GWAS results table.
myResults <- table_GWAS_Results(
# Specify a folder with GWAS results
folder = "GWAS_Results/" )Customization
myResults <- table_GWAS_Results(
# Specify a folder with GWAS results
folder = "GWAS_Results/",
# Set threshold level
threshold = 6.7,
# Set suggestive threshold level
sug.threshold = 5 )
# Save
write.csv(myResults, "GWAS_Results_Table.csv", row.names = F)
read.csv("GWAS_Results_Table.csv")[1:20,]## SNP Chr Pos P.value MAF effect
## 1 Lcu.1GRN.Chr1p365986872 1 365986872 1.754402e-175 0.47096774 NA
## 2 Lcu.1GRN.Chr1p365986872 1 365986872 1.164052e-154 0.47096774 NA
## 3 Lcu.1GRN.Chr1p365986872 1 365986872 3.281941e-128 0.47096774 NA
## 4 Lcu.1GRN.Chr1p361840399 1 361840399 2.178943e-84 0.04193548 NA
## 5 Lcu.1GRN.Chr1p361840399 1 361840399 6.963600e-47 0.04193548 NA
## 6 Lcu.1GRN.Chr1p361856257 1 361856257 3.377658e-40 0.04516129 NA
## 7 Lcu.1GRN.Chr1p365986872 1 365986872 2.845683e-37 0.47096774 NA
## 8 Lcu.1GRN.Chr1p365318023 1 365318023 1.769760e-35 0.47096774 NA
## 9 Lcu.1GRN.Chr1p365318027 1 365318027 2.803987e-35 0.47258065 NA
## 10 Lcu.1GRN.Chr5p1658484 5 1658484 1.050791e-34 0.12345679 NA
## 11 Lcu.1GRN.Chr1p368962767 1 368962767 2.535833e-34 0.43225806 NA
## 12 Lcu.1GRN.Chr2p44546658 2 44546658 9.063107e-34 0.06790123 NA
## 13 Lcu.1GRN.Chr6p3269280 6 3269280 1.213338e-33 0.28105590 NA
## 14 Lcu.1GRN.Chr1p437374598 1 437374598 2.783736e-31 0.47670807 NA
## 15 Lcu.1GRN.Chr1p365786633 1 365786633 6.742839e-31 0.43225806 NA
## 16 Lcu.1GRN.Chr1p365987296 1 365987296 2.173474e-30 0.42741935 NA
## 17 Lcu.1GRN.Chr1p365785855 1 365785855 2.180688e-30 0.43064516 NA
## 18 Lcu.1GRN.Chr1p361407757 1 361407757 3.259584e-30 0.15000000 NA
## 19 Lcu.1GRN.Chr1p367671439 1 367671439 3.283698e-30 0.42741935 NA
## 20 Lcu.1GRN.Chr1p365986872 1 365986872 6.530328e-30 0.47096774 NA
## H.B.P.Value Model Type Trait negLog10_P negLog10_HBP
## 1 5.901229e-170 BLINK NYC Cotyledon_RedvsYellow 174.75587 169.22906
## 2 3.915488e-149 MLMM NYC Cotyledon_RedvsYellow 153.93403 148.40721
## 3 1.103937e-122 FarmCPU NYC Cotyledon_RedvsYellow 127.48387 121.95706
## 4 3.664623e-79 BLINK NYC Cotyledon_RedvsYellow 83.66175 78.43597
## 5 1.171163e-41 FarmCPU NYC Cotyledon_RedvsYellow 46.15717 40.93138
## 6 5.680663e-35 MLMM NYC Cotyledon_RedvsYellow 39.47138 34.24560
## 7 9.571939e-32 GLM NYC Cotyledon_RedvsYellow 36.54581 31.01900
## 8 2.976445e-30 GLM NYC Cotyledon_RedvsYellow 34.75209 29.52630
## 9 3.143895e-30 GLM NYC Cotyledon_RedvsYellow 34.55222 29.50253
## 10 3.534516e-29 BLINK NYC DTF_Nepal_2017 33.97848 28.45167
## 11 2.132426e-29 GLM NYC Cotyledon_RedvsYellow 33.59588 28.67113
## 12 3.048530e-28 FarmCPU NYC DTF_Nepal_2017 33.04272 27.51591
## 13 4.081267e-28 FarmCPU NYC DTF_Sask_2017_b 32.91602 27.38920
## 14 4.681785e-26 FarmCPU NYC DTF_Sask_2017_b 30.55537 25.32959
## 15 4.536137e-26 GLM NYC Cotyledon_RedvsYellow 30.17116 25.34331
## 16 1.047873e-25 GLM NYC Cotyledon_RedvsYellow 29.66285 24.97969
## 17 1.047873e-25 GLM NYC Cotyledon_RedvsYellow 29.66141 24.97969
## 18 3.654722e-25 FarmCPU NYC Cotyledon_RedvsYellow 29.48684 24.43715
## 19 1.380660e-25 GLM NYC Cotyledon_RedvsYellow 29.48364 24.85991
## 20 2.196587e-24 MLM NYC Cotyledon_RedvsYellow 29.18507 23.65825
## Threshold Effect
## 1 Significant -0.46837845
## 2 Significant -0.49056078
## 3 Significant -0.49543836
## 4 Significant -0.46035890
## 5 Significant -0.47155436
## 6 Significant -1.49473301
## 7 Significant -0.45583432
## 8 Significant -0.44709570
## 9 Significant -0.44512589
## 10 Significant 12.56191501
## 11 Significant -0.43780205
## 12 Significant -1.31597036
## 13 Significant -2.25531798
## 14 Significant -0.19434407
## 15 Significant -0.41587018
## 16 Significant -0.41397885
## 17 Significant -0.41358916
## 18 Significant -0.02021127
## 19 Significant -0.40080355
## 20 Significant -0.43389870table_GWAS_Results_Summary()
The function table_GWAS_Results_Summary() creates a
summary table of GWAS results.
Specifying a folder is all that is required to produce a
GWAS summary results table.
mySummary <- table_GWAS_Results_Summary(
# Load table_GWAS_Results() object
xx = myResults )Customization
mySummary <- table_GWAS_Results_Summary(
# Load table_GWAS_Results() object
xx = myResults,
# Should markers be binned
binMarkers = T,
# Set the size of the bin
binSize = 5000000 )
# Save
write.csv(mySummary, "GWAS_Results_Summary_Table.csv", row.names = F)
read.csv("GWAS_Results_Summary_Table.csv")[1:20,]## SNP Chr Pos Hits MAF max_negLog10_P
## 1 Lcu.1GRN.Chr1p365986872 1 365986872 697 0.47096774 174.75587
## 2 Lcu.1GRN.Chr1p361840399 1 361840399 179 0.04193548 83.66175
## 3 Lcu.1GRN.Chr5p1658484 5 1658484 88 0.12345679 33.97848
## 4 Lcu.1GRN.Chr1p368962767 1 368962767 109 0.43225806 33.59588
## 5 Lcu.1GRN.Chr2p44546658 2 44546658 229 0.06790123 33.04272
## 6 Lcu.1GRN.Chr6p3269280 6 3269280 54 0.28105590 32.91602
## 7 Lcu.1GRN.Chr1p437374598 1 437374598 107 0.47670807 30.55537
## 8 Lcu.1GRN.Chr1p446411579 1 446411579 45 0.27950311 28.18039
## 9 Lcu.1GRN.Chr1p357509147 1 357509147 86 0.36129032 27.09940
## 10 Lcu.1GRN.Chr1p351845237 1 351845237 41 0.34677419 27.01084
## 11 Lcu.1GRN.Chr1p371692181 1 371692181 87 0.33870968 24.49782
## 12 Lcu.1GRN.Chr1p381165558 1 381165558 42 0.37258065 23.48246
## 13 Lcu.1GRN.Chr4p416272481 4 416272481 28 0.27795031 23.48129
## 14 Lcu.1GRN.Chr1p27007485 1 27007485 15 0.07741935 23.11123
## 15 Lcu.1GRN.Chr4p423043571 4 423043571 22 0.28881988 22.58392
## 16 Lcu.1GRN.Chr3p239208186 3 239208186 56 0.46118012 22.55951
## 17 Lcu.1GRN.Chr4p441072457 4 441072457 107 0.39751553 22.40887
## 18 Lcu.1GRN.Chr4p431243285 4 431243285 3 0.26863354 22.34247
## 19 Lcu.1GRN.Chr2p414512712 2 414512712 23 0.22670807 22.24036
## 20 Lcu.1GRN.Chr4p418804416 4 418804416 15 0.28726708 22.22975
## min_negLog10_P Models
## 1 5.002856 BLINK;MLMM;FarmCPU;GLM;MLM
## 2 5.021118 BLINK;FarmCPU;MLMM;GLM;MLM
## 3 5.068647 BLINK;FarmCPU;GLM;MLMM;MLM
## 4 5.053575 GLM;MLM
## 5 5.056161 FarmCPU;GLM;BLINK;MLMM;MLM
## 6 5.028903 FarmCPU;GLM;BLINK;MLMM;MLM
## 7 5.028706 FarmCPU;GLM;BLINK;MLMM;MLM
## 8 6.528411 FarmCPU;GLM
## 9 5.081874 GLM;MLM
## 10 5.150784 GLM;MLM
## 11 5.001179 GLM;MLM;FarmCPU;BLINK
## 12 5.218290 GLM;MLM
## 13 6.283576 GLM
## 14 6.132473 MLMM;GLM;FarmCPU;BLINK
## 15 5.147086 GLM;FarmCPU
## 16 13.972620 GLM;BLINK
## 17 6.544255 GLM
## 18 18.687908 GLM
## 19 18.627747 GLM
## 20 6.211708 GLM
## Traits max_negLog10_HBP
## 1 Cotyledon_RedvsYellow 169.22906
## 2 Cotyledon_RedvsYellow 78.43597
## 3 DTF_Nepal_2017;DTF_Sask_2017_b;DTF_Sask_2017 28.45167
## 4 Cotyledon_RedvsYellow 28.67113
## 5 DTF_Nepal_2017;DTF_Sask_2017 27.51591
## 6 DTF_Sask_2017_b;DTF_Nepal_2017;DTF_Sask_2017 27.38920
## 7 DTF_Sask_2017_b;DTF_Sask_2017;DTF_Nepal_2017 25.32959
## 8 DTF_Sask_2017_b;DTF_Nepal_2017;DTF_Sask_2017 23.13070
## 9 Cotyledon_RedvsYellow 22.95280
## 10 Cotyledon_RedvsYellow 22.88197
## 11 Cotyledon_RedvsYellow;DTF_Nepal_2017 20.66120
## 12 Cotyledon_RedvsYellow 19.80690
## 13 DTF_Sask_2017_b;DTF_Sask_2017 18.54795
## 14 Cotyledon_RedvsYellow 18.06154
## 15 DTF_Sask_2017_b;DTF_Sask_2017 18.12774
## 16 DTF_Sask_2017_b 18.12774
## 17 DTF_Sask_2017_b;DTF_Sask_2017 18.12774
## 18 DTF_Sask_2017_b 18.12774
## 19 DTF_Sask_2017_b 18.12774
## 20 DTF_Sask_2017_b;DTF_Sask_2017 18.12774
## min_negLog10_HBP min_P max_P min_HBP max_HBP
## 1 2.2949280 1.754402e-175 9.934454e-06 5.901229e-170 5.070747e-03
## 2 0.9188869 2.178943e-84 9.525370e-06 3.664623e-79 1.205350e-01
## 3 1.2595187 1.050791e-34 8.537931e-06 3.534516e-29 5.501503e-02
## 4 2.3403431 2.535833e-34 8.839436e-06 2.132426e-29 4.567273e-03
## 5 1.2595187 9.063107e-34 8.786970e-06 3.048530e-28 5.501503e-02
## 6 0.6316665 1.213338e-33 9.356142e-06 4.081267e-28 2.335250e-01
## 7 0.6316665 2.783736e-31 9.360395e-06 4.681785e-26 2.335250e-01
## 8 3.8754994 6.600970e-29 2.962025e-07 7.401162e-24 1.331989e-04
## 9 2.3679743 7.954236e-28 8.281817e-06 1.114809e-23 4.285738e-03
## 10 2.4274280 9.753502e-28 7.066686e-06 1.312302e-23 3.737421e-03
## 11 1.7421589 3.178199e-25 9.972885e-06 2.181717e-21 1.810677e-02
## 12 2.4894361 3.292641e-24 6.049371e-06 1.559910e-20 3.240141e-03
## 13 3.7152165 3.301480e-24 5.205045e-07 2.831713e-19 1.926564e-04
## 14 1.7196030 7.740496e-24 7.371009e-07 8.678825e-19 1.907203e-02
## 15 0.7813179 2.606656e-23 7.127122e-06 7.451844e-19 1.654559e-01
## 16 8.9229276 2.757344e-23 1.065075e-14 7.451844e-19 1.194187e-09
## 17 3.8884305 3.900628e-23 2.855913e-07 7.451844e-19 1.292914e-04
## 18 16.1385144 4.544939e-23 2.051598e-19 7.451844e-19 7.269183e-17
## 19 16.0965685 5.749635e-23 2.356423e-19 7.451844e-19 8.006293e-17
## 20 3.6778994 5.891782e-23 6.141754e-07 7.451844e-19 2.099426e-04