Incorporating experimental mortality improves statistical inference of crossover patterning along meiotic chromosomes
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ID: 315626
2026
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Abstract
Abstract Classic recombination experiments designed to test genetic and environmental treatments do not directly measure chromosomal exchange, instead rates and distribution of crossing-over in F1 meiocytes are inferred from genetic markers in F2 adults. In Drosophila melanogaster females this procedure introduces substantial “missing data problems” because 75% of meiotic chromatids segregate to polar body nuclei and another 11% are transmitted to inviable F2 zygotes which cannot be scored for recombination. To address these sources of uncertainty and bias, we extend the Cx(Co)m data-generating process by assuming: 1) programmed double strand breaks occur as a Poisson point process, 2) crossover maturation is a stationary renewal process, 3) chromosome segregation is random one-half thinning of this process, 4) fertilization by X- versus Y-bearing sperm is mendelian, and 5) egg-to-adult survival is binomially distributed with a rate parameter determined by F2 marker alleles. To quantify experimental mortality, we performed egg counts for 6-point X chromosome testcrosses and marker-free X chromosome controls on identical genetic backgrounds under standard laboratory conditions. The 19,927 fly dataset revealed 44% F2 experimental mortality, and likelihood ratio tests support a model where 36 of those 44% are due to sex-specific, marker-associated viability defects. Variability in X chromosome genetic map lengths with experimental mortality can be simulated, and we provide case-control 80% power curves to guide experimental design. We propose that differential mortality should be the de facto null hypothesis when comparing F2 recombinant fractions, and we provide probabilistic models of the data-generating process to improve characterization of F1 meiotic crossover patterning.
| Reference Key |
openalex_W7163333234
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| Authors | Spencer Koury, Melika Ghasemi Shiran, Eva Hammonds, Cassidy Schneider, Laurie Stevison |
| Journal | current genetics |
| Year | 2026 |
| DOI |
10.1093/genetics/iyag142
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| URL | |
| Keywords | Keywords not found |
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