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Chr17 50M-51M

Methodology

To narrow down the DecodeME1 GWAS-1 signal, I fine-mapped the GWAS-1 hit on chromosome 17 using SUSIE2. The parameters of my SUSIE runs were the same as for the chromosome 1 locus.

Results

Each of the 4 SUSIE runs produced a single credible set with the same 63 variants, as illustrated by the UpSet plot below:

chr17_upset

The distribution of probability mass across these variants was slightly different across the runs, as illustrated by the following UpSet plot, which is restricted to variants of \(PIP>0.01\).

chr_17_pip001

The stackplot below, for the \(L=10\) case, illustrates the typical PIP pattern across variants.

chr17_stackplot

The credible set at this locus is focused upstream of the CA10 gene. This would seem to make a CA10 a good candidate for further investigation.

The detailed variants results are below:

Variant List (L=10)
cs CHR POS EA NEA alpha mu PIP
L1 17 50260366 T C 0.055044 0.023495 0.055161
L1 17 50282834 T C 0.0412133 0.0232911 0.0413269
L1 17 50287448 A C 0.03704 0.0232154 0.0371532
L1 17 50279497 T C 0.0357889 0.023191 0.035902
L1 17 50276929 G C 0.0353752 0.0231828 0.0354882
L1 17 50294376 T C 0.0347934 0.023171 0.0349065
L1 17 50289205 T G 0.0338718 0.0231519 0.0339847
L1 17 50290356 G A 0.0326279 0.0231252 0.0327407
L1 17 50285434 A G 0.0322195 0.0231162 0.0323321
L1 17 50295022 A G 0.032076 0.0231131 0.0321887
L1 17 50272817 TA T 0.0318892 0.0231089 0.0320022
L1 17 50271517 A G 0.0315094 0.0231003 0.031622
L1 17 50270268 T C 0.0296969 0.023058 0.0298095
L1 17 50294496 G A 0.0295546 0.0230546 0.0296669
L1 17 50271048 A G 0.029051 0.0230423 0.0291633
L1 17 50271674 A G 0.0277654 0.0230099 0.0278775
L1 17 50274393 A G 0.0272457 0.0229964 0.0273578
L1 17 50261933 CA C 0.0224699 0.0228578 0.0225895
L1 17 50298441 A C 0.0164279 0.0226309 0.0165402
L1 17 50285721 CT C 0.0154185 0.0225847 0.0155312
L1 17 50297907 G A 0.0143061 0.02253 0.0144173
L1 17 50288321 T G 0.0133761 0.0224807 0.0134877
L1 17 50294365 G C 0.0129315 0.0224559 0.013043
L1 17 50291040 T C 0.0113472 0.0223598 0.0114599
L1 17 50293499 C T 0.011319 0.022358 0.0114317
L1 17 50290955 T C 0.0108259 0.0223251 0.0109383
L1 17 50299063 G A 0.0106136 0.0223105 0.0107243
L1 17 50296779 C A 0.0100429 0.0222696 0.010153
L1 17 50317099 G T 0.00958189 0.0222348 0.00969233
L1 17 50299079 A G 0.00905355 0.0221928 0.00916326
L1 17 50316842 G A 0.00902542 0.0221904 0.00913553
L1 17 50316120 G C 0.00869143 0.0221624 0.00880133
L1 17 50314751 A G 0.00863265 0.0221574 0.00874245
L1 17 50314078 T C 0.00860537 0.022155 0.00871515
L1 17 50316095 C T 0.00857642 0.0221525 0.00868625
L1 17 50315044 T C 0.00834915 0.0221325 0.0084588
L1 17 50301060 G A 0.00734718 0.0220371 0.00745657
L1 17 50314350 T C 0.00724198 0.0220263 0.00735097
L1 17 50303770 G A 0.00693928 0.0219944 0.00704804
L1 17 50298413 C T 0.00676585 0.0219754 0.00687585
L1 17 50313611 G C 0.00673919 0.0219724 0.00684788
L1 17 50309650 G A 0.00665405 0.0219629 0.00676269
L1 17 50299568 CT C 0.00665184 0.0219626 0.00676031
L1 17 50300195 G C 0.00659815 0.0219566 0.00670669
L1 17 50312430 G A 0.00653389 0.0219492 0.00664245
L1 17 50299809 C T 0.00640457 0.0219342 0.00651299
L1 17 50300935 A G 0.00639552 0.0219331 0.00650393
L1 17 50312701 T C 0.00636609 0.0219297 0.00647456
L1 17 50301112 G A 0.00625822 0.0219168 0.0063666
L1 17 50309626 T G 0.00624581 0.0219153 0.00635419
L1 17 50313435 C A 0.006139 0.0219024 0.00624731
L1 17 50311547 G C 0.00610706 0.0218984 0.00621535
L1 17 50309871 A C 0.00604867 0.0218912 0.00615692
L1 17 50300731 A G 0.00586534 0.021868 0.00597344
L1 17 50301115 T C 0.00566136 0.0218413 0.00576936
L1 17 50308625 G A 0.00536093 0.0218001 0.00546875
L1 17 50308600 G A 0.0053587 0.0217998 0.00546652
L1 17 50291181 A C 0.00402888 0.0215831 0.00413852
L1 17 50302285 G A 0.00369619 0.0215172 0.00380575
L1 17 50296459 T G 0.00315961 0.0213967 0.00326817
L1 17 50302857 T C 0.00315112 0.0213946 0.00325992
L1 17 50303110 G C 0.0030557 0.0213709 0.00316436
L1 17 50303521 T C 0.0030314 0.0213648 0.00314003

  1. Genetics Delivery Team, Thibaud Boutin, Andrew D Bretherick, Joshua J Dibble, Esther Ewaoluwagbemiga, Emma Northwood, Gemma L Samms, Veronique Vitart, Project, Cohort Delivery Team, Øyvind Almelid, and others. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome. medRxiv, pages 2025–08, 2025. URL: https://www.medrxiv.org/content/10.1101/2025.08.06.25333109v1. ↩

  2. Gao Wang, Abhishek Sarkar, Peter Carbonetto, and Matthew Stephens. A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(5):1273–1300, 2020. URL: https://academic.oup.com/jrsssb/article/82/5/1273/7056114. ↩