Discrepancies in GWAS Results with Updated Version

[VidhiPriyanka13]

Hello GAPIT Forum,

After updating to the latest version, I'm getting different GWAS results from the same data file. Also, there are discrepancies between analyses run on different machines. Any insights on the necessary steps for the updated version and why results differ would be appreciated.

Thank you
P

[yuru0503]

I also found out it had different results between GAPIT3.4 and GAPIT3.3. Based on my understanding of my GWAS research, I think that the outputs from the GAPIT3.3 version are the correct ones.

[Hadi-Hoda-AghBaba]

Hi there, are you using the same model?

[VidhiPriyanka13]

Yes same model Get Outlook for iOS<https://aka.ms/o0ukef>

…

________________________________ From: Hadi-Hoda-Aghbaba ***@***.***> Sent: Sunday, April 28, 2024 7:04:32 PM To: jiabowang/GAPIT ***@***.***> Cc: Priyanka Gupta ***@***.***>; Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) Hi there, are you using the same model? — Reply to this email directly, view it on GitHub<#120 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BIACSKZNF6HWFIJCKBAY5WLY7V6ABAVCNFSM6AAAAABGS2Z566VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAOBRGY4DSNJTG4>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[Hadi-Hoda-AghBaba]

Could you please send both codes here.
Cheers

[Hadi-Hoda-AghBaba]

Also the Manhattan plots as well. tnx

[VidhiPriyanka13]

Same Script in my server i used as follows myY <- read.table("/home/prgup1/icsw/fast-gbs_v2/results/gwas/TKW1.txt", head = TRUE) myG <- read.table("/home/prgup1/icsw/fast-gbs_v2/results/gwas/Genotyping_file_21Chr_153K_withS_withoutsemicoloumn2_142G.hmp.txt", head = FALSE) myGAPIT <- GAPIT(Y=myY,G=myG,PCA.total=3, model=c("FarmCPU"), Multiple_analysis=TRUE, Model.selection = TRUE)[https://res-h3.public.cdn.office.net/assets/mail/file-icon/png/zip_16x16.png]wheat-trial.zip<https://ulavaldti-my.sharepoint.com/:u:/g/personal/prgup1_ulaval_ca/EetikM47OVhIm_EQ11siwlkB1iznkTz_mP-g5QHs7n6i8g> [cid:fc22e810-fdc1-4c4c-b817-0c1deb1bed27] [cid:247a11dd-ad10-42fa-859f-10168d34ad53]

…

________________________________ From: Hadi-Hoda-AghBaba ***@***.***> Sent: Sunday, April 28, 2024 11:12 PM To: jiabowang/GAPIT ***@***.***> Cc: Priyanka Gupta ***@***.***>; Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) Also the Manhattan plots as well. tnx — Reply to this email directly, view it on GitHub<#120 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BIACSKYVTUNDMSKRIDNCSPTY7W3BJAVCNFSM6AAAAABGS2Z566VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAOBRHAYTSOBQGI>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[Hadi-Hoda-AghBaba]

Hi,

this link doesn;t work. Pls send a screen shot of your Manhattan plots.

Try this code to see if it solves the problem:

myGAPIT <- GAPIT
(Y=myY,
G=myG,
NJtree.group=5,
NJtree.type=c("fan"),
kinship.algorithm = "VanRaden",
model=c("FarmCPU"),
Multiple_analysis=TRUE,
Model.selection = TRUE)

[VidhiPriyanka13]

Hi can I share with you the complete dataset- so that you can check and i will also share the error I am receiving? Is there any possibility to have a virtual call with you please

…

________________________________ From: Hadi-Hoda-AghBaba ***@***.***> Sent: Tuesday, April 30, 2024 3:07 AM To: jiabowang/GAPIT ***@***.***> Cc: Priyanka Gupta ***@***.***>; Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) Hi, this link doesn;t work. Pls send a screen shot of your Manhattan plots. Try this code to see if it solves the problem: myGAPIT <- GAPIT (Y=myY, G=myG, NJtree.group=5, NJtree.type=c("fan"), kinship.algorithm = "VanRaden", model=c("FarmCPU"), Multiple_analysis=TRUE, Model.selection = TRUE) — Reply to this email directly, view it on GitHub<#120 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BIACSK3RA6J6GU53LCXK3X3Y747L3AVCNFSM6AAAAABGS2Z566VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAOBUGUZTGMBXGQ>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[Hadi-Hoda-AghBaba]

Hi there,
yes good idea. Send me through the data.
Let's look at the data and we'll go from there :)
Cheers

[jiabowang]

Recently, we are revising some functions. So that makes a mistake.
Now, through comparison results and revision, the results should be correct.

[Lightoscope]

I don't know if it's related, but I'm getting different results when GAPIT calculates kinship and when that output file, 'GAPIT.Genotype.Kin_Zhang.csv' in this case, is used as KI in an otherwise identical run.

[VidhiPriyanka13]

Dear GAPIT forum, I need your help troubleshooting an issue I'm facing with GWAS analysis using different models. FarmCPU, Blink, and MLMM are providing different results for the same traits. If you agree, I can share my genotyping and phenotyping files with you for further analysis. Thank you for your assistance. Best regards, P

…

________________________________ From: Ryan Disney ***@***.***> Sent: Sunday, May 19, 2024 8:13 PM To: jiabowang/GAPIT ***@***.***> Cc: Priyanka Gupta ***@***.***>; Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) I don't know if it's related, but I'm getting different results when GAPIT calculates kinship and when that output file, 'GAPIT.Genotype.Kin_Zhang.csv' in this case, is used as KI in an otherwise identical run. — Reply to this email directly, view it on GitHub<#120 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BIACSK2IHD5IK23Q45E2LXTZDE5Z7AVCNFSM6AAAAABGS2Z566VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMJZGQ3DMNRTGA>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[VidhiPriyanka13]

[https://res.public.onecdn.static.microsoft/assets/mail/file-icon/png/txt_16x16.png]genotying_fileHeteroMissing_80K_SNPs_corrected_NN.hmp.txt<https://ulavaldti-my.sharepoint.com/:t:/g/personal/prgup1_ulaval_ca/EdbqN1dgKVRAop9PBkz0Q8EB-P1NRMfr-QXLkjfaeBNRjw> Here is my file. Would it be possible to have a virtual call to fix the issue? I have been struggling with it for the last couple of months. Please somebody in your team can assist me. Thanks, P

…

________________________________ From: Priyanka Gupta ***@***.***> Sent: Wednesday, July 3, 2024 11:39 AM To: jiabowang/GAPIT ***@***.***>; jiabowang/GAPIT ***@***.***> Cc: Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) Dear GAPIT forum, I need your help troubleshooting an issue I'm facing with GWAS analysis using different models. FarmCPU, Blink, and MLMM are providing different results for the same traits. If you agree, I can share my genotyping and phenotyping files with you for further analysis. Thank you for your assistance. Best regards, P

________________________________ From: Ryan Disney ***@***.***> Sent: Sunday, May 19, 2024 8:13 PM To: jiabowang/GAPIT ***@***.***> Cc: Priyanka Gupta ***@***.***>; Author ***@***.***> Subject: Re: [jiabowang/GAPIT] Discrepancies in GWAS Results with Updated Version (Issue #120) I don't know if it's related, but I'm getting different results when GAPIT calculates kinship and when that output file, 'GAPIT.Genotype.Kin_Zhang.csv' in this case, is used as KI in an otherwise identical run. — Reply to this email directly, view it on GitHub<#120 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BIACSK2IHD5IK23Q45E2LXTZDE5Z7AVCNFSM6AAAAABGS2Z566VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMJZGQ3DMNRTGA>. You are receiving this because you authored the thread.Message ID: ***@***.***> Taxa TKWUL23 TKWUL22 ICSW_P1_p1_A10_w72.sort 36 29 ICSW_P1_p1_A1_W1.sort 43 29 ICSW_P1_p1_A2_w8.sort 45 38 ICSW_P1_p1_A3_w16.sort 38 31 ICSW_P1_p1_A4_w24.sort 43 37 ICSW_P1_p1_A5_w32.sort 28 26 ICSW_P1_p1_A6_w40.sort 36 30 ICSW_P1_p1_A7_w48.sort 40 33 ICSW_P1_p1_A8_w56.sort 39 36 ICSW_P1_p1_A9_w64.sort 42 34 ICSW_P1_p1_B1_W2.sort 43 38 ICSW_P1_p1_B2_w9.sort 49 40 ICSW_P1_p1_B3_w17.sort 44 31 ICSW_P1_p1_B4_w25.sort 42 33 ICSW_P1_p1_B5_w33.sort 45 38 ICSW_P1_p1_B6_w41.sort 42 34 ICSW_P1_p1_B7_w49.sort 37 30 ICSW_P1_p1_B8_w57.sort 33 31 ICSW_P1_p1_B9_w65.sort 39 32 ICSW_P1_p1_C1_W3.sort 37 32 ICSW_P1_p1_C10_w74.sort 37 28 ICSW_P1_p1_C2_w10.sort 46 37 ICSW_P1_p1_C3_w18.sort 43 33 ICSW_P1_p1_C4_w26.sort 44 41 ICSW_P1_p1_C5_w34.sort 40 35 ICSW_P1_p1_C6_w42.sort 36 30 ICSW_P1_p1_C7_w50.sort 39 33 ICSW_P1_p1_C8_w58.sort 37 31 ICSW_P1_p1_C9_w66.sort 37 31 ICSW_P1_p1_D1_W4.sort 43 37 ICSW_P1_p1_D10_w75.sort 29 23 ICSW_P1_p1_D2_w11.sort 46 38 ICSW_P1_p1_D3_w19.sort 42 37 ICSW_P1_p1_D4_w27.sort 46 36 ICSW_P1_p1_D5_w35.sort 37 30 ICSW_P1_p1_D6_w43.sort 38 29 ICSW_P1_p1_D7_w51.sort 41 31 ICSW_P1_p1_D8_w59.sort 34 33 ICSW_P1_p1_D9_w67.sort 32 28 ICSW_P1_p1_E1_w5.sort 40 34 ICSW_P1_p1_E2_w12.sort 43 32 ICSW_P1_p1_E3_w20.sort 42 38 ICSW_P1_p1_E4_w28.sort 38 34 ICSW_P1_p1_E5_w36.sort 36 35 ICSW_P1_p1_E6_w44.sort 41 34 ICSW_P1_p1_E7_w52.sort 37 28 ICSW_P1_p1_E8_w60.sort 36 33 ICSW_P1_p1_E9_w68.sort 30 23 ICSW_P1_p1_F1_w6.sort 47 35 ICSW_P1_p1_F2_w13.sort 42 34 ICSW_P1_p1_F3_w21.sort 39 33 ICSW_P1_p1_F4_w29.sort 36 35 ICSW_P1_p1_F5_w37.sort 41 32 ICSW_P1_p1_F6_w45.sort 41 35 ICSW_P1_p1_F7_w53.sort 39 30 ICSW_P1_p1_F8_w61.sort 33 29 ICSW_P1_p1_F9_w69.sort 32 30 ICSW_P1_p1_G1_w7.sort 36 33 ICSW_P1_p1_G2_w14.sort 45 40 ICSW_P1_p1_G3_w22.sort 43 34 ICSW_P1_p1_G5_w38.sort 43 36 ICSW_P1_p1_G6_w46.sort 37 29 ICSW_P1_p1_G7_w54.sort 35 28 ICSW_P1_p1_G8_w62.sort 40 33 ICSW_P1_p1_H2_w15.sort 40 30 ICSW_P1_p1_H3_w23.sort 41 27 ICSW_P1_p1_H4_w31.sort 29 28 ICSW_P1_p1_H5_w39.sort 34 28 ICSW_P1_p1_H6_w47.sort 41 32 ICSW_P1_p1_H7_w55.sort 36 28 ICSW_P1_p1_H8_w63.sort 37 33 ICSW_P1_p1_H9_w71.sort 40 33 ICSW_P2_p2_A10_w170.sort 36 32 ICSW_P2_p2_A3_w92.sort 39 28 ICSW_P2_p2_A4_w100.sort 42 35 ICSW_P2_p2_A5_w108.sort 36 30 ICSW_P2_p2_A6_w116.sort 44 39 ICSW_P2_p2_A7_w146.sort 37 26 ICSW_P2_p2_A8_w154.sort 32 30 ICSW_P2_p2_A9_w162.sort 39 35 ICSW_P2_p2_B10_w171.sort 41 35 ICSW_P2_p2_B2_w85.sort 38 33 ICSW_P2_p2_B3_w93.sort 33 33 ICSW_P2_p2_B4_w101.sort 39 31 ICSW_P2_p2_B5_w109.sort 37 32 ICSW_P2_p2_B6_w139.sort 37 32 ICSW_P2_p2_B7_w147.sort 37 34 ICSW_P2_p2_B8_w155.sort 41 34 ICSW_P2_p2_B9_w163.sort 42 37 ICSW_P2_p2_C1_w78.sort 33 27 ICSW_P2_p2_C10_w172.sort 37 34 ICSW_P2_p2_C2_w86.sort 46 34 ICSW_P2_p2_C3_w94.sort 35 30 ICSW_P2_p2_C4_w102.sort 36 26 ICSW_P2_p2_C5_w110.sort 39 34 ICSW_P2_p2_C6_w140.sort 33 29 ICSW_P2_p2_C7_w148.sort 43 34 ICSW_P2_p2_C8_w156.sort 34 30 ICSW_P2_p2_C9_w164.sort 35 31 ICSW_P2_p2_D1_w79.sort 32 29 ICSW_P2_p2_D10_w173.sort 40 35 ICSW_P2_p2_D2_w87.sort 39 32 ICSW_P2_p2_D3_w95.sort 41 33 ICSW_P2_p2_D4_w103.sort 38 32 ICSW_P2_p2_D5_w111.sort 35 30 ICSW_P2_p2_D6_w141.sort 37 31 ICSW_P2_p2_D7_w149.sort 41 36 ICSW_P2_p2_D9_w165.sort 35 28 ICSW_P2_p2_E2_w88.sort 31 24 ICSW_P2_p2_E3_w96.sort 38 33 ICSW_P2_p2_E4_w104.sort 33 32 ICSW_P2_p2_E5_w112.sort 40 36 ICSW_P2_p2_E6_w142.sort 34 31 ICSW_P2_p2_E7_w150.sort 41 37 ICSW_P2_p2_E8_w158.sort 37 32 ICSW_P2_p2_E9_w166.sort 32 31 ICSW_P2_p2_F1_w81.sort 30 28 ICSW_P2_p2_F2_w89.sort 40 31 ICSW_P2_p2_F3_w97.sort 33 34 ICSW_P2_p2_F4_w105.sort 39 31 ICSW_P2_p2_F5_w113.sort 36 31 ICSW_P2_p2_F6_w143.sort 32 28 ICSW_P2_p2_F7_w151.sort 34 31 ICSW_P2_p2_F9_w167.sort 36 31 ICSW_P2_p2_G1_w82.sort 29 24 ICSW_P2_p2_G2_w90.sort 38 27 ICSW_P2_p2_G3_w98.sort 39 27 ICSW_P2_p2_G4_w106.sort 42 35 ICSW_P2_p2_G5_w114.sort 46 33 ICSW_P2_p2_G6_w144.sort 38 36 ICSW_P2_p2_G7_w152.sort 37 31 ICSW_P2_p2_G8_w160.sort 36 28 ICSW_P2_p2_G9_w168.sort 40 36 ICSW_P2_p2_H1_w83.sort 35 30 ICSW_P2_p2_H2_w91.sort 32 29 ICSW_P2_p2_H3_w99.sort 34 32 ICSW_P2_p2_H4_w107.sort 39 34 ICSW_P2_p2_H5_w115.sort 39 31 ICSW_P2_p2_H6_w145.sort 31 28 ICSW_P2_p2_H7_w153.sort 47 38 ICSW_P2_p2_H8_w161.sort 46 35 ICSW_P2_p2_H9_w169.sort 37 34