Periodic Reporting for period 1 - FeverTime (SELECTION VERSUS DRIFT IN THE RISE OF DELETERIOUS MUTATIONS: THE CASE OF FAMILIAL MEDITERRANEAN FEVER)
Reporting period: 2023-01-01 to 2024-12-31
Familial Mediterranean Fever (FMF) is the most prevalent monogenic autoinflammatory disorder worldwide, caused by mutations in the MEFV gene. These mutations are particularly common in Mediterranean populations and their diasporas, with carrier rates reportedly reaching up to 20%. This raises a compelling question that remains unresolved: what evolutionary forces have led to the emergence and persistence of such a substantial genetic burden across this broad region? The phenomenon of balancing selection is famously exemplified by the relationship between malaria resistance and hemoglobin disorders, a landmark example of how selective pressure can shape the human genome. By analogy, it has been proposed that MEFV mutations might confer a survival advantage against historical infectious diseases such as tuberculosis, smallpox, cholera, or plague. Despite the appeal of this idea, definitive evidence to support it is lacking and a comprehensive analysis of the interplay between selection and genetic drift in driving the regional prevalence of MEFV mutations is needed.
FeverTime sought to leverage ancient genomics to establish a new benchmark example of how evolutionary mechanisms shape the genetic architecture of human disease, outlining the following objectives:
Reconstructing the demographic history of the Armenian Highland. Changes in the frequency of deleterious alleles can be influenced by demographic processes, making it crucial to accurately model the demographic history of the Armenian population to make correct inferences about the role of evolutionary forces in the local mutation load. To achieve the most thorough demographic reconstructions over time, time-series data from this geographically and genetically continuous region, spanning many millennia, were generated.
Detecting the role of selection and random drift in the drive of local MEFV mutation burden. FeverTime investigated the possibility that selection pressure on MEFV variants has changed through time, considering major cultural and technology shifts as well as detailed climate reconstructions as possible explicit drivers.
Estimating the age of selected MEFV alleles. FeverTime evaluated the age of MEFV alleles and examined whether the local increase in the frequency of disease-causing alleles was driven by a founder effect or recurrent mutations.
FeverTime sought to leverage ancient genomics to establish a new benchmark example of how evolutionary mechanisms shape the genetic architecture of human disease, outlining the following objectives:
Reconstructing the demographic history of the Armenian Highland. Changes in the frequency of deleterious alleles can be influenced by demographic processes, making it crucial to accurately model the demographic history of the Armenian population to make correct inferences about the role of evolutionary forces in the local mutation load. To achieve the most thorough demographic reconstructions over time, time-series data from this geographically and genetically continuous region, spanning many millennia, were generated.
Detecting the role of selection and random drift in the drive of local MEFV mutation burden. FeverTime investigated the possibility that selection pressure on MEFV variants has changed through time, considering major cultural and technology shifts as well as detailed climate reconstructions as possible explicit drivers.
Estimating the age of selected MEFV alleles. FeverTime evaluated the age of MEFV alleles and examined whether the local increase in the frequency of disease-causing alleles was driven by a founder effect or recurrent mutations.
I screened ancient skeletons from 23 archaeological sites spanning the Paleolithic, Neolithic, Eneolithic, Bronze Age, Iron Age, Antiquity, and the Middle Ages. DNA extraction was performed using a silica column-based protocol, targeting the densest portions of the petrous bone or teeth to maximize endogenous DNA yield. Libraries were treated with the USER enzyme to mitigate DNA-damage-induced transitions and subsequently screened to assess ancient human DNA content using an Illumina MiSeq platform. We then sequenced 52 of these samples to 1X genome-wide coverage, with at least five samples represented per time period. The sequencing was performed using the NovaSeq 6000 platform. We developed in-solution RNA baits to capture and sequence the MEFV gene at high coverage (located on chromosome 16). This was done for the majority of the screened samples. We processed the data using standard procedures for ancient genomics and rigorous quality control measures. Data authenticity was evaluated by analyzing the average sequence length, identifying patterns of molecular damage, and estimating contamination rates in the mitochondrial and X chromosomes. We also conducted radiocarbon dating to place them precisely along the time scale. In addition to the ancient data generated in this project, I obtained population-level MEFV gene haplotype frequencies from 68 unrelated modern Armenians. I also integrated our data with a recently published study that featured 36 whole-genome sequences (30x coverage) from modern Armenian individuals. In our recent published study (Hovhannisyan et al. 2025), we observed an unexpected finding: nearly half of the individuals in our dataset (16 out of 36) carried pathogenic or likely pathogenic variants in the MEFV gene - a frequency significantly exceeding the 20% reported in previous studies. This result underscores the limitations of conventional diagnostic approaches, such as strip assays, which appear to lack the sensitivity required to accurately identify pathogenic or likely pathogenic mutations in the Armenian population. For the population genetic analyses, we compared Armenians with reference datasets from the broader geographic region, using an array of conventional population genetics models, such as Principal Component Analyses, ADMIXTURE, fineStructure, and various f-statistics. To detect selection, we used a Bayesian statistical framework which accounts for demography.
The work has been a success. During the grant period, two publications acknowledging the Marie Curie Fellowship were produced (with a third currently under review). Our recently published paper is the first to shed light on the demographic history of the Armenian Highlands and presents initial findings on MEFV gene variation in the modern Armenian population (published in the American Journal of Human Genetics). The dataset we released is the first substantial whole-genome dataset for the modern Armenian population, which can be used for further research, including population and medical genetics studies. As proponents of open science, we have already shared all raw data from our published paper in open repositories such as ENA and Figshare. Our study has been covered by multiple international and Armenian media outlets, and we were interviewed on radio and television to reach a broader audience. To reach out to a general audience, we also shared our publications and conference attendance on Twitter, BlueSky and LinkedIn accounts. The Twitter post about the publication in November got 30,000 views and more than 100 reposts.
Currently, the paper featuring the novel ancient dataset, along with the captured dataset of ancient samples and novel high-coverage modern samples, is in preparation and will be submitted to a high-impact journal. We plan to maintain the open science standards by sharing the raw data and code for our major paper on MEFV gene variation as well.
Currently, the paper featuring the novel ancient dataset, along with the captured dataset of ancient samples and novel high-coverage modern samples, is in preparation and will be submitted to a high-impact journal. We plan to maintain the open science standards by sharing the raw data and code for our major paper on MEFV gene variation as well.