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SUMMARY:Fitness inferences from massive genomic data.
DTSTART:20260608T133000Z
DTEND:20260608T153000Z
DTSTAMP:20260608T131400Z
UID:indico-event-9707@indico.fysik.su.se
DESCRIPTION:Speakers: Hongli Zeng (Nanjing University of Posts and Telecom
 munications)\n\nInferring fitness from genomic sequences is a central chal
 lenge in evolutionary populations and infectious disease surveillance. Thi
 s talk presents related studies on reconstructing fitness landscapes from 
 whole-genome\, time-stratified population data. Using SARS-CoV-2 as a case
  study\, we compare transient Quasi-Linkage Equilibrium (tQLE)\, which inc
 orporates epistasis under a near-linkage-equilibrium assumption\, with Max
 imum Path Likelihood (MPL)\, which assumes additive fitness but accommodat
 es arbitrary allele correlations. We further examine\, through theory and 
 simulations\, the parameter regimes in which genotype fitness order can be
  reliably inferred under selection\, mutation\, and recombination. We exte
 nd the QLE theory to interacting populations connected by migration and sh
 ow that low migration rates preserve the QLE phase\, enabling accurate inf
 erence of additive and epistatic fitness parameters. The studies clarify b
 oth the potential and the limitations of fitness inference from large-scal
 e genomic time-series data.\nzoom: https://stockholmuniversity.zoom.us/j/
 622224375\n\nhttps://indico.fysik.su.se/event/9707/
URL:https://indico.fysik.su.se/event/9707/
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