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Record W4389922495 · doi:10.36939/ir.202312181420

Evolutionary history and diversity of human-specific FAM72A paralogs

2023· dissertation· en· W4389922495 on OpenAlexaff
Ilya Kisselev

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsGene duplicationBiologyFunctional divergenceEvolutionary biologyAdaptation (eye)Most recent common ancestorGeneNegative selectionGeneticsGenomeHaplotypePopulationLineage (genetic)Molecular evolutionPhylogenetic treeNucleotide diversityGene familyAllele

Abstract

fetched live from OpenAlex

Gene duplication is a key driver of genetic diversity and adaptation, allowing genomes to develop complexity and redundant sequences that evolve along different trajectories. In human evolution, gene duplication played an important role: since divergence from the common ancestor with chimpanzees, humans have gained approximately 75 lineage-specific genes, influencing brain development, dietary adaptation, and immune regulation. The FAM72 gene family, with four paralogs (FAM72A-D) that arose after human-chimpanzee divergence, illustrates this process. The evolutionary history and function of the FAM72 paralogs remain poorly described. The ancestral FAM72A protein drives early stages of somatic hypermutation in B cells by antagonizing UNG2. However, FAM72C-D paralogs have Trp125Arg amino acid substitution that prevents them from interacting with UNG2. This study hypothesizes that after the initial duplication from FAM72A to FAM72B, FAM72B duplicated to FAM72C and FAM72D. I hypothesize that opposing selective forces operate on FAM72A-B and FAM72C-D paralogs. Another hypothesis is that population-specific exposure to local environments during human evolution has driven the selection of population-specific adaptive haplotypes of FAM72A paralogs. The study used the 1000 Genomes dataset, testing selection through neutrality metrics and haplotype-based scores, and investigated functional divergence by comparing conserved amino acid sites and gene-wide LD patterns across human populations. Bayesian divergence time estimation between FAM72 paralogs was performed using the most common haplotypes in humans and chimpanzees. The hypothesized sequence of duplication events was supported by the phylogenetic analysis. The neutrality metrics identified FAM72C as recovering from a selective sweep, with other paralogs not showing signals of positive selection. Integrated haplotype scores of FAM72D suggested a recent selective sweep in African populations, and FAM72A-B showed high conservation. Linkage disequilibrium analysis highlighted functional regions, with FAM72A and FAM72B sharing active LD-enriched promoters, while FAM72C contained an active enhancer linked to immune cell function. Finally, multiple signatures of balancing selection were observed in an intronic region of FAM72C. The results suggest neutral or relaxed selection for FAM72A-B, but purifying selection following a selective sweep for FAM72C-D. The divergence of paralog pairs is evident in regulatory and functional shifts, notably with FAM72C’s unique immune cell associations. No clear signs of population-specific adaptation were identified, but FAM72B shows distinct haplotypes between East Asian and South Asian populations, hinting at either population bottlenecks or adaptive evolution. The findings show how gene duplication within the FAM72 gene family has contributed to genetic diversity and potential adaptability, with some members potentially shaping the evolutionary trajectory of immune function in human populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.237
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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