Determining the origin of different variants associated with familial mediterranean fever by machine-learning

Orit Adato, Ronen Brenner, Avi Levy, Yael Shinar, Asaf Shemer*, Shalem Dvir, Ilan Ben-Zvi, Avi Livneh, Ron Unger, Shaye Kivity

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A growing number of familial Mediterranean fever (FMF) patients in Israel do not have a single country of origin for all four grandparents. We aimed to predict the Mediterranean fever gene (MEFV) variant most likely to be found for an individual FMF patient, by a machine learning approach. This study was conducted at the Sheba Medical Center, a referral center for FMF in Israel. All Jewish referrals included in this study carried an FMF associated variant in MEFV as shown by genetic testing performed between 2001 and 2017. We introduced the term ‘origin score’ to capture the dose and different combinations of the grandparents’ origin. A machine learning approach was used to analyze the data. In a total of 1781 referrals included in this study, the p.Met694Val variant was the most common, and the variants p.Glu148Gln and p.Val726Ala second and third most common, respectively. Of 26 countries of origin analyzed, those that increased the likelihood of a referral to carry specific variants were identified in North Africa for p.Met694Val, Europe for p.Val726Ala, and west Asia for p.Glu148Gln. Fourteen of the studied countries did not show a highly probable variant. Based on our results, it is possible to describe an association between modern day origins of the three most common MEFV variant types and a geographical region. A strong geographic association could arise from positive selection of a specific MEFV variant conferring resistance to endemic infectious agents.

Original languageEnglish
Article number15206
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

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