{"paper_id":"282ed8fb-eb22-4f12-9bf7-fab9c69245af","body_text":"Signatures of sex ratio distortion in humans\nJames G. Baldwin-Brown1,*, Sergiusz Wesolowski2, Raquel Mae Zimmerman3, Bennet \nPeterson3, Martin Tristani-Firouzi3, Edgar Havier Hernandez3, Kenneth I. Aston4, Mark \nYandell3, Nitin Phadnis1,*\n1 School of Biological Sciences, University of Utah, 257 South 1400 East, Salt Lake \nCity, UT 84112.\n2Moderna, 200 Technology Square, Cambridge, Massachusetts 02139.\n3Eccles Institute of Human Genetics, School of Medicine, University of Utah, UT 84112.\n4Department of Surgery, School of Medicine, University of Utah, UT 84112.\n*Corresponding author\nSummary\nSegregation distortion, the disproportionate inheritance of selfish genetic \nelements, is an important evolutionary force. While many species carry distorters, it is \nnot clear if humans do. Major limitations for detecting human distortion are the small \nsize of human families and the lack of genetic markers in most subjects. Here, we \npresent evidence of strong distortion in a large human pedigree. We analyzed \npedigrees from the Utah Population Database and identified lineages with a high \nchance of carrying a distorter. In particular, we identified a family that preferentially \nproduced male offspring at a 2:1 ratio. This pattern is consistent with a distorting Y-\nchromosome, a rarity in species with degenerate Y-chromosomes. The detection of \nsuch non-Mendelian inheritance patterns suggests that human genomes may harbor \nsegregation distorters. \nKeywords\nsegregation distortion, meiotic drive, evolution, genetics, human\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nIntroduction\n Segregation distorters are selfish genes that bias Mendelian segregation in their \nfavor. They often do this by manipulating the production or function of gametes to \noutcompete those with the alternative homologous chromosomes (Sandler & Novitski \n1957, Lyttle 1976, Lindholm et al. 2016). Across eukaryotes, signals of segregation \ndistortion have been identified through skewed segregation ratios (Morgan et al. 1925, \nSturtevant & Dobzhansky 1936, Hickey & Craig 1966, Turner & Perkins 1979, Jaenike \n2001, Lyon 2003, Fishman et al. 2008, Matsuda et al. 2011). Sometimes, multiple \ndistinct segregation distorters can exist even within single species (discussed in Kingan \net al. 2010). In mice, the t-haplotype on chromosome 17 causes segregation distortion \nin males by sabotaging the swimming of sperm that carry the wild-type chromosome 17 \n(Winkler and Lindholm 2022, Swanepoel and Mueller 2024). A gamete-killing distorter is \nalso known in mice: divergence in copy number of ampliconic genes (genes with \nmultiple copies) Slx & Sly causes sex ratio distortion (Coquet et al. 2012, Baird et al. \n2023, Arlt et al. 2025, Campbell and Heitzmann 2025). Similar ampliconic genes exist in \nhumans as well (Kruger et al. 2019, Ye et al. 2018, Bhowmick & Takahata 2018, \nLucotte et al. 2018, Vegesna et al. 2019). Humans present no obvious biological \nexception in terms of harboring distorters. Given the wide distribution of distorters \nacross eukaryotes, including in mammalian systems, it is surprising that none have \nbeen definitively identified in humans.\nDistorters in natural populations are usually found serendipitously when \nperforming controlled crosses and measuring the transmission ratios of alleles in large \nnumbers of offspring. Sex chromosome distorters are especially easy to detect because \nthey produce progeny with skewed sex ratios. For example, X-linked distorters often \ndestroy Y-bearing sperm, producing heavily female biased progeny (Jaenike 2001). \nAnecdotally, many people know a family where most children are the same sex. \nUnfortunately, even the largest human families are too small to reliably detect distorters \nwith offspring counting in a single generation. Segregation distorters can also reside on \nautosomes but detection of these necessitates the use of autosomal genetic markers. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSmall family sizes, long lifespans, and ethical constraints make the usual detection \nmethods impractical to apply at scale in humans. \nEarly studies of single loci report some evidence of distorted transmission ratios \nin humans (Evans et al. 1994, Chakraborty et al. 1996, Naumova et al. 1998, Eaves et \nal 1999, Girardet et al. 2000, Naumova et al. 2001). Later studies took a more agnostic \napproach to detecting transmission distortion in humans. These attempts generally used \nmicroarray genotyping data for one or two generations of transmission of alleles in \nfamilies. The first large-scale attempt to specifically look for distortion in humans, based \non the amount of shared genetic material among siblings, argued for large-scale, small-\neffect transmission distortion throughout the human population (Zöllner et\n al. 2004). \nThis study spurred interest in the field and was followed by three landmark studies using \neither 60 individuals from HapMap or 5209 individuals from the Framingham heart panel \n(Frazer et al. 2007, Santos et al. 2009, Deng et al. 2009 Paterson et al. 2009). All three \nstudies used the Transmission Disequilibrium Test (TDT) to identify loci with biased \ntransmission ratios (Spielman et al. 1993). They each found some number of autosomal \nmarkers that show evidence of transmission distortion. Deng et al. found 1,205 outlier \nSNPs and 224 candidate genes, Paterson et al. found 8 outlier SNPs with their most \nconservative thresholding, and Santos et al. found one site that was convincingly \ndistorting.\nA comprehensive reanalysis of these studies, however, showed that most of \nthese biased loci were either not significant at the genome-wide level or best explained \nby genotyping errors (Meyer et al. 2012). Meyer et al. found the eight conservative \nSNPs identified by Paterson et al. and the site identified by Santos et al. to be true \noutliers. Meyer et al. identified two key limitations in accurately detecting transmission \ndistortion in pedigrees. First, even minor errors in genotyping calls produce pervasive \nfalse positive signals of transmission distortion. Second, the small sample sizes of most \ngenotyped pedigrees limit the power to detect anything but the strongest signals of \ndistortion. Thus, these approaches have low power to detect true positives and a high \nlikelihood of generating false positive signals of transmission distortion. A large amount \nof genotyping data from a small number of individuals exacerbates these problems. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nFinally, studies of small numbers of subjects are unlikely to sample distorters that are \nuncommon in the population.\nHere, inspired by Meyer et al.’s insights, we attempt to circumvent the two bottle-\nnecks in detecting transmission distortion in human pedigrees: genotyping errors and \nsmall sample sizes. First, we focus only on sex chromosome transmission using the \nrecorded sex of individuals in pedigrees. To the extent that the recorded sex of individu-\nals accurately reflects their sex chromosome genotypes, our approach should side-step \nthe pervasive false positives arising from genotyping errors. Second, we are able to in-\ncrease the sample size in our study by an order of magnitude compared to previous \nstudies. Because our approach only requires the recorded sex of an individual, we can \nnow access and use information from individuals going back more than 10 generations \nwhere molecular genotype information is unavailable. To the degree that the pedigree \ndata do not suffer systematic biases in recording males vs. females, the sexes of indi-\nviduals should reflect the transmission of sex chromosomes.\nDespite the a priori rarity of Y-chromosome distorters, we focus on understanding \nY-biased inheritance patterns for several reasons. First, the patrilineal inheritance and \nexpression of Y-chromosomes in every generation makes it possible to deterministically \ntrack the inheritance patterns of Y-chromosomes compared to X-chromosomes. \nSecond, because human males are hemizygous, deleterious alleles on either the Y-\nchromosome or the X-chromosome will harm males and produce a bias toward female \noffspring, but not toward male offspring. This property of the sex chromosomes makes it \nhard to distinguish whether female bias is due to deleterious alleles or drivers, whereas \nmale bias cannot be confused in this way. Third, because males determine the sex of \noffspring in humans, any observed anomalies are more likely to be the result of male \ngametogenesis. Because Y-chromosomes exclusively carry genes required for male \nfertility and sex determination, and other sources of bias such as viability differences are \nunlikely to explain the observed patterns. Fourth, our approach treats the SRY locus \nthat determines sex as a dominant, visible marker that faithfully tracks the entire \nrecombinationally inert Y-chromosome, with the exception of the pseudoautosomal \nregion. Difficult to genotype regions that may nevertheless contribute to human \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nsegregation distortion, such as heterochromatin and satellite content, ampliconic genes, \nnon-coding RNAs, etc can be tracked using our method. We report evidence for a \nconsistently male-biased human family. This non-Mendelian inheritance suggests that \nhuman genomes may harbor segregation distorters.\nResults\nTo look for signatures of distortion in humans, we used a Bayesian algorithm to detect \ndistorted families in pedigrees. We used only the recorded sexes of 76,445 individuals \nfrom the Utah Population Database (UPDB), which offers a generational depth dating \nback to the 1700s (Smith & Mineau 2021, Slattery & Kerber 1993) (Utah Population \nDatabase). This is an order of magnitude larger than the previous largest study \n(Paterson et al. 2009). The overall sex ratio of individuals across the dataset is nearly \nMendelian, with 50.2% males. We used a probabilistic programming approach that uses \nBayesian networks to detect systematic sex biases within families. Our program, Warp, \nlets us find families with patterns of larger-than-expected proportions of males or \nfemales (Sup. Fig. 1).\nWarp assigns a likelihood of carrying a transmission distorter to each individual in \nthe pedigree. In the first step of Warp, each individual is assigned a low initial likelihood \nof carrying a transmission distorter (1 in 100 here, but the process is robust to many \nparameters tested – see Methods). These initial likelihoods start out very low because \nthey are based on only a single data point per individual. Starting from individuals at the \nbottom of the tree, Warp looks at the parents of each individual. Using the chain rule \nand Bayes’ theorem, it then updates the children’s likelihoods of carrying a transmission \ndistorter based on their parent’s sexes and likelihoods (Fig. 1, Sup. Fig. 1, Appendix). \nWarp then iterates the same logic going down through the generations until it reaches \nthe bottom of the pedigree. Once the bottom of the pedigree is reached, Warp iterates \nback up through the generations in the pedigree using the same logic. For example, a \nfather of four sons has a higher likelihood of carrying a male-biased distorter compared \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nto a father of two sons and two daughters. This process is iterated up and down through \nthe pedigrees until the likelihoods stabilize.\nWarp identified two putative male-biased families and six putative female-biased \nfamilies in this dataset (Fig. 2a, Sup. Fig. 3, 4). Although we are motivated to identify \nsignatures of sex chromosome distortion, viability differences can also bias sex ratios \nwithin families (Jaenike 2001). For example, the presence of an X-linked recessive \nlethal mutation in a family will produce fewer surviving males than females, generating a \npattern indistinguishable from a female-biased distorter. In addition, tracking the \ntransmission of each identical-by-descent X-chromosome is challenging. In contrast, \nviability differences are less likely to affect inferences about male-biased transmission. \nY-chromosomes carry few if any viability-essential genes. Moreover, a Y-linked \nmutation that reduces viability would produce a female-biased family, and not male-\nbiased. Male-biased families are therefore less likely to produce a spurious signal of \ntransmission distortion. In addition, when searching for male biased distorters, we can \neasily track the transmission of each identical-by-descent Y-chromosome across many \ngenerations of patrilineal transmission in deep pedigrees. Together, because Y-\nchromosomes are hemizygous in males, largely non-recombining, not essential for \nviability (c.f. Turner’s syndrome, Ranke and Saenger 2001), and tracked without \nskipping generations, we focus our further analyses on male-biased families (Fig. 3).\nAlthough we detected some male- or female-biased families, any distribution, if \nsampled enough times, will produce some extreme outliers by chance. To test whether \nthe dense clustering of males in outlier families occurred by chance, we compared the \nlikelihood of observing this bias in our real pedigree to the likelihood of observing a \nsimilar bias in permuted pedigrees. To produce a conservative control dataset that most \nclosely modeled the true data, we performed 1000 permutations of the whole pedigree \ndataset in which we randomly assigned sexes to individuals but kept the tree topology \nthe same. To make the test as conservative as possible, our permuted pedigrees \ninclude extreme outcomes such as two individuals of the same sex having children. \nComparing the likelihood values of individuals in the true pedigree to those of the \npermuted pedigrees showed that the high likelihood values of the most male-biased \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nfamily in the true pedigree could not be explained by chance alone (p = 0.05 by highest-\nlikelihood comparison, p = 0.017 by z-value comparison, and p = 0.001 by rank \ncomparison, see methods). As an aside, we performed the same test on female-biased \nfamilies and found a similar result (p = 0.004 by highest likelihood comparison, p = 0.17 \nby z-value comparison, and p = 0.017 by rank comparison). Together, these results \nprovide the first indication that the sex biased families in our dataset would not occur by \nchance alone.\nTo complement our Bayesian approach, we next used the transmission distortion \ntest (TDT), which is a workhorse test for detecting transmission biases in pedigrees \n(Spielman et al. 1993). The TDT is a chi-squared test that examines whether allelic \ntransmission deviates from a 50:50 expectation.  We identified all unique Y-\nchromosome lineages in the dataset and applied the TDT to each one. It is important to \nnote that some “unique” Y-chromosomes may be identical by descent, but this shared \nancestry may not be captured in the existing pedigree data. We then used false \ndiscovery rate (FDR)-corrected p-values from these results to see how many families \nwere significantly male-biased. Because we do not trust outcomes from small families, \nwe only include families with more than 75 assayable offspring in this analysis (an \narbitrary cutoff). After FDR correction, only one patrilineal lineage out of 26,865 tested \nwas significantly male biased (p = 0.0249, non-FDR-corrected p = 0.00102, Figures 2b, \n4,). This patrilineal lineage identified by TDT is the same family that Warp indicated as \nmost likely to carry a male-biased distorter. Because both Warp and TDT independently \nidentify the same male-biased family, this family is likely to be a true outlier.\nThe progenitor male of this family had two sons, both of whom produced highly \nmale-skewed descendants. The first son had five male offspring out of six. While the \nother son produced only one son, this grandson produced eight male offspring out of \n11. The patrilineal descendants of these sons also produced highly distorted sex ratios \namong their offspring. Together, this patrilineal line spanning seven generations had 33 \nmales that carried the same Y-chromosome and produced at least one child, giving us \nhigh statistical power to detect systematic male bias. This family contained 89 \ninformative transmissions with a total of 60 male offspring and only 29 female offspring. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nPut another way, 89 children contributed to the sex ratio of offspring; of those, 60 were \nmale. This gives a final proportion of 67.4% male offspring in this family (Figure 4). \nWe next used Monte Carlo simulations that compare the observed sex ratio in \nthis outlier family to 10,000 simulated families of equal size where the sexes of \nindividuals were randomly assigned. This approach should be a numerical \napproximation of running the TDT on the family of interest. Here again, we allowed two \nindividuals of the same sex to have children to include extreme outcomes. We \ncompared the sex ratios of these simulated families to the real outlier family and found \nthat the male bias in this outlier family was unlikely to occur by chance alone (We \ncompared the sex ratios of these simulated families to the real outlier family and found \nthat the male bias in this outlier family was unlikely to occur by chance alone \n(p=0.00138), just as the TDT indicated. Taken together, our results are consistent with \nthe presence of a male-biased distorter in humans.\nIn summary, we used a combination of statistical techniques to first identify \ndistorters, then confirm that they could not have arisen by chance. We used Warp to \nfind high-likelihood distorter carriers, then used a permutation test in which we permuted \nthe entire pedigree dataset to determine the chance of finding such a high-likelihood \ndistorter. This produced several likely distorters and showed that they are true outliers \nfrom expectations. We then took the most likely Y-chromosome distorter and tested its \nentire lineage for distortion using the TDT. We then performed a follow-up Monte Carlo \ntest within this lineage that agreed with the TDT. All of these tests together confirmed \nthat this Y-chromosome is a likely sex distorter.\nDiscussion\nSex ratio in humans has fascinated people for centuries (Bernoulli 1713, Fisher \n1958, Graffelman and Hoekstra 2000, Sen 2003). Many researchers have shown that \nthe human sex ratio is very close to 50:50, except in extreme cases such as war or \nother external pressures on sex-specific birth rates (Fisher 1958, Graffelman and \nHoekstra 2000, Sen 2003). Based on our new results, we argue that comprehensive \nstudies are likely to reveal the presence of selfish genes in human populations, \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nincluding those that produce distorted sex ratios in some families. Identifying such \nselfish chromosomes in humans has many important implications. For example, given \nthe dramatic effect on fertility observed in many known distorters in other species (Wu \n1983, Jaenike 2001), distorters may explain the surprisingly high levels of male infertility \nin humans (Sharlip et al. 2002, Agarwal et al. 2015). Distorters may also help to explain \nlong-standing questions related to human natural history. For example, certain regions \nof the Neanderthal genome have introgressed into the human genome, while other \nintrogression ‘deserts’ are free of Neanderthal ancestry (Sankararaman et al. 2014). \nStrong selection preferring the Homo sapiens genome at these loci, such as that \nproduced by distorters, could explain why these deserts remain Neanderthal-free.\nEmpirical studies of sex chromosome drivers in Drosophila, mosquitoes, and \nrodents show that X-chromosomes are more likely to harbor distorters than Y-\nchromosomes (Burt and Trivers 2009, Helleu et al. 2015). There are few known cases \nof Y-chromosome distortion, and they mostly occur in species with non-degenerate Y-\nchromosomes such as mosquitoes (Bachtrog 2013, Sweeny & Barr 1978). The human \nY-chromosome is highly degenerate, with approximately 70 functioning genes (Jobling \n& Tyler-Smith 2017). None of these genes are known to be essential for viability, and \ntypical XX females lacking Y-chromosomes are viable. A signal of Y-biased \ntransmission distortion in humans is thus a surprise. Such Y-distortion, however, is \nknown to occur in mammals. In mice, both experimental manipulations and studies in \nnatural populations have shown X- and Y-chromosome distortion mediated by copy \nnumber variation in ampliconic genes, such as those in the Slx/Sly system (Coquet et \nal. 2012, Baird et al 2023, Arlt et al. 2025, Campbell and Heitzmann 2025). While \nhumans lack Slx/Sly genes, they have other ampliconic genes such as the RBMY and \nPRY gene clusters that are candidates for playing a role in sex ratio distortion (Kruger et \nal. 2019, Ye et al. 2018, Bhowmick & Takahata 2018, Lucotte et al. 2018, Vegesna et \nal. 2019). The PRY gene cluster appears promising because it is involved in sperm \nelimination, a process recently shown to be a consequence of selfish chromosomes in \nthe male germline (Ridges et al. 2026). \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nAlthough Y-chromosome distortion is a parsimonious explanation for the \nobserved male-biased family, the exact molecular stage where biases such as Y-\ndistortion occur in humans remains unknown. There are at least two known \nmechanisms underlying male gametic distortion. First, the most frequently observed \nmechanism for segregation distortion involves killing gametes bearing their competing \nhomologous chromosomes (Jaenike 2001). Second, some selfish chromosomes, such \nas the t-haplotype on chromosome 17 in mouse, can distort transmission ratios by \nreducing the swimming ability of sperm that carry competing homologous chromosomes \n(Winkler and Lindholm 2022). Understanding the molecular and cytological basis of Y-\nchromosome distortion in our focal family would necessitate ethically deanonymizing \naffected individuals and following up with detailed cytological and genomic studies on \nsperm samples from individuals identified from the pedigree. \nAlthough we focus on male biased families to detect signatures of Y-\nchromosome segregation distortion, we also found six putative female-biased families in \nour permutation tests. However, as discussed in the results, it is difficult to track X-\nchromosome inheritance patterns in these pedigrees without genotype information. \nFurther, X-linked deleterious mutations can lead to a depletion of males in families, \ngenerating the same pattern as expected from X-chromosome segregation distortion. \nWithout further information we are therefore cautious in interpreting the observed \nfemale biased families as due to the presence of an X-chromosome segregation \ndistorter. Even more glaring, our genotype-free analysis does not extend to autosomal \ndistorters where most human genomic material resides. This leaves open the possibility \nthat human genomes harbor segregation distorters whose effects may depend on \nwhether the genetic background is permissive for distortion or if it carries suppressor \nalleles. \nOur work adds to several lines of evidence that point toward the presence of \ndistorters in humans. First, previous attempts at large-scale searches for distortion have \nproduced at least a few high-confidence outliers (Meyer et al. 2012). Second, the \npresence of ancient haplotypes on the human X-chromosome and the rapid expansion \nof the FT haplotype of the human Y-chromosome are suggestive of selfish transmission \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\ndynamics in human populations (Skov, et al, 2023; Hallast et al, 2020). Third, in an \nindirect method of detecting skews in sex chromosome transmission, a recent study \nargued for strong clustering of sexes within families, indicating the presence of heritable \nvariation in sex ratios in humans (Wang et al. 2025; though see Zietsch et al. 2020). \nTogether with our observation of apparent Y-biased distortion and the possibility of \nunexplored population-specific or suppressed distorters, segregation distorters appear \nlikely to exist in human populations.\nAcknowledgements\nThanks to Michael Shapiro for his help revising the manuscript. We acknowledge NIH \ngrants R01GM141422 and R35GM156267 to NP for funding this research. Thanks also \nto Molly Przeworski for her advice on the manuscript.\nAuthor contributions\nConceptualization, J.G.B., N.P., K.I., M.Y.; Methodology, J.G.B., N.P., K.I., M.Y., and \nRZ, SW, BP; Software, J.G.B. and RZ, SW, BP; Validation, J.G.B.; Formal analysis, \nJ.G.B. and RZ, SW, BP; Investigation, J.G.B., K.I., and RZ, SW, BP; Resources, N.P., \nK.I., and M.Y.; Data curation, J.G.B.; Writing – original draft, J.G.B.; Writing – review & \nediting, J.G.B., N.P., K.I., M.Y., and RZ, SW, BP; Visualization, J.G.B.; Supervision, \nN.P. and M.Y.; Project administration, J.G.B., N.P., and M.Y.; Funding acquisition N.P. \nand M.Y\nDeclaration of interests\nThe authors declare no competing interests.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nFigure legends\nFigure 1. Warp has high power to infer the likelihood of carrying a distorter in \npedigrees regardless of penetrance, inheritance pattern, and other challenges. \nWarp propagates the likelihood of carrying a distorter from parent to child, and from \nchild to parent, using Bayesian network propagation (a combination of Bayes’ rule and \nthe chain rule). Here, we show an abstract representation of the way Warp propagates \nprobability information up and down a pedigree. Initial likelihoods for carrying the \ndistorter are set using the phenotypes of each individual and the expected allele \nfrequency of the distorter. In the second step, the likelihoods of all offspring of updated \nindividuals are in turn updated based on the parent’s genotype onward down the \npedigree. Finally, parents of updated offspring are also updated. This process is \nrepeated until all likelihoods stop changing. In the case of non-cyclic pedigrees (those \nwith no inbreeding), only one iteration is required.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nFigure 2. One human family shows a strong bias toward male offspring.  A. Results \nfrom Warp testing for male bias plotted as a network of parent-child relationships \n(analogous to a pedigree). Points represent individuals and arrows represent parent-\nchild connections. Bright red connections represent families that contain high-likelihood \nindividuals. Two clusters are highlighted in red, indicating two potential distorters \ndetected here. B. The same, but here testing for X-biased distorters. Six families are \nhighlighted. C. A close look at the most significant family. A single male, top, carries a \nputatively distorting Y-chromosome such that about ⅔ of offspring are male. This partial \npedigree shows the offspring of the focal male. Dark lines represent lines of inheritance \nof the distorting Y-chromosome, while light lines represent other lines of inheritance. \nBlue boxes represent males, while red circles are females. Dotted borders indicate \nindividuals with non-informative offspring that are not shown. Boxed individuals are \noffspring of a distorter-carrying male and are included in the transmission disequilibrium \ntest.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nFigure 3. Y-linked distorters are more easily detected than X-linked distorters \nbecause of the simpler Y-chromosome inheritance pattern. A. A selfish Y-\nchromosome will always be inherited through the patrilineal line, allowing it to distort \nevery generation. This makes finding all carriers in a patrilineal line easy: select for \nmales. Here the bold green Y represents a Y-chromosome carrying a distorter, and the \ngreen line represents the line of descent of the distorter. B. On the other hand, a sex \ndistorting X-chromosome only distorts in males, producing mostly females. It can, thus, \nonly distort every other generation. To follow the distorter through the pedigree, we \nmust also follow offspring that are not distorter carriers, “diluting” the family tree with \nnon-distorting X-chromosomes that reduce the power of statistics like the TDT to detect \ndistortion.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nFigure 4. The TDT re-identifies the outlier male-biased family found be Warp. A. \nThis figure shows the distribution in the UPDB of male sex proportion (y axis) in each Y-\nchromosome lineage compared to lineage size (x axis). Lineages with few individuals \ncan have very distorted male proportions, but no lineage with a large number of \nindividuals is as distorted as the focal family. The blue line is the theoretical 99% \nconfidence interval. The red dot is the focal family discussed in the text. B. The likely-\ndistorting family is large and highly significantly distorted. This figure shows the \ndistribution in the UPDB of p-values derived from the transmission disequilibrium test. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nEach dot represents a unique patrilineal line of descent (and thus a unique Y-\nchromosome). The focal lineage discussed in the paper is red. The X axis is the number \nof children in each patrilineal line that contributed to the TDT test, and the Y axis is the \np-value indicating the chance that the patrilineal line had such a high degree of sex \ndistortion by chance.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSTAR methods section\nResource Availability\nLead contact\nFurther information and requests for resources and reagents should be directed to and \nwill be fulfilled by the lead contacts, James Baldwin-Brown \n(\njgbaldwinbrown@gmail.com) and Nitin Phadnis (nitin.phadnis@utah.edu).\nMaterials availability\nThis study did not generate new unique reagents.\nData and code availability\nAll data generated from this experiment will be made available to the scientific \ncommunity and the public where ethically possible. All analysis scripts are available \nthrough GitHub at https://github.com/jgbaldwinbrown/jgbutils.\nExperimental model and study participant details\nHuman participants\nAll analysis of pedigrees was based on fully anonymized pedigree data made available \nby the Utah Population Database.\nMethod details\nData generation\nAll data used here was derived from the UPDB and was collected previously; we have \nno new data to report (Smith & Mineau 2021).\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nQuantification and statistical Analysis\nBayesian detection of distorting pedigrees\nThe power to detect distortion is determined by pedigree size, distortion depth (as in \nhow many generations the distortion is observed), breadth (how many distorted \nindividuals per generation are observed), and the strength of distortion. In addition to \nthose criteria, the observed distortion must adhere to Mendelian inheritance rules rather \nthan being abundantly, but still randomly scattered throughout the family. To maximize \nthe detection power and plausibility, we built a machine learning model that incorporates \nthese three factors and contrasts the observed distribution of the distortion across family \nbranches against a null model of the possibility of non-heritable causes.\nOur model is based on the Probabilistic Programming paradigm and uses Bayesian \nnetworks (Stephenson 2000). In this model, the family pedigree structure is used as the \nbackbone of the belief propagation algorithm (Kim et al. 1983). The belief propagation \nalgorithm takes as input the observed phenotype (distorted or not) and updates the \nprobability distribution for an individual, where the probability distribution says how likely \nthe individual is to carry an allele that causes the observed phenotype. This is a simple \napplication of Bayes' Theorem, where P(Distorter) is updated to P(Distorter | Sex bias). \nThat said, a single update is rarely enough to draw conclusions about the genetics of \ndistortion, especially in the case of distorters with low penetrance or high prevalence. \nThus, our algorithm extends the inference context to the whole pedigree. In the next \nstep in the algorithm, each individual propagates the updated belief to its parents and \nchildren. This Bayesian Update (message passing) is conducted throughout the entire \npedigree and propagated across generations, meaning that information from family \nfounders can travel to the last offspring and vice versa. Each individual propagates and \nupdates their beliefs until the algorithm converges and propagated beliefs no longer \nchange the individual's distribution – in other words, all individuals in the family agree as \nto whether their observed phenotype is due to heritable distortion or otherwise. In the \nprocess of reaching a consensus, the two models, coded in the conditional probability \ntables of the network, are competing to be the most plausible explanation. The null \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nmodel assumes that the observed phenotypes are not due to heritability. When sex is \nthe phenotype of interest, the model parameters are 50% sex penetrance, no \nheritability. The genetic distortion model assumes 90% penetrance and incorporates the \nprobability of introducing or losing the mutation responsible for the distortion (de novo \nmutation rate 0.000001) and the a priori probability of a mutation occurring in the \npopulation (allele frequency 0.01). We chose this parameter combination by running \nWarp on all permutations of the following parameters, then selecting the parameter \ncombination that produced the individual with the highest probability of carrying a \ndistorter. Although this could cause cherry picking in another context, here we use Warp \nonly to identify the most-likely-distorting families; the permutation tests of Warp, \nexplained below, are the actual test of presence of distortion in the dataset. The \npermutations below are done with the same parameters for the true dataset and the \nshuffled dataset, so the parameters do not affect the significance of the permutation \ntest. The tested penetrances were 50%, 60%, 62%, 64%, 66%, 68%, 70%, 75%, 80%, \n85%, 90%, 95%, 97%, 99%. The tested allele frequencies were 0.001, 0.0001, 0.00001, \n0.000001. The tested de novo mutation rates were 0.01, 0.001, 0.0001, 0.00001, and \n0.000001.\nWe ran Warp on a pedigree reconstructed from 76,445 individuals for the UPDB. In \nsuch a large pedigree, by sheer chance, there are branches with sex bias present, but \nrarely can such biases be explained with heritable patterns. \nOur statistical approach was designed to identify individuals that might carry a distorter, \nand was not designed with hypothesis testing in mind; therefore, we cannot assign a p-\nvalue to the hypothesis that a pedigree carries a distorter based on the core algorithm. \nTo properly assign a p-value to this probability, we used a permutation test to calculate \nthe FDR-corrected probability (p) that a pedigree could, by chance, contain the \nobserved degree of distortion. This code is available at github.com/jgbaldwinbrown/tdt. \nWe permuted the sex of all individuals in the pedigree 1,000 times and then re-ran Warp \non each of these permuted samples. Each run of Warp on permuted data represents \nthe outcome of a new pedigree of the same size, topology, and overall sex ratio, but no \ntrue evidence of distortion. We then compared the likelihood of being a distorter carrier \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nin the most-affected individual in each dataset. The reported p-value is the percentage \nof permuted pedigrees that contain an individual with a higher likelihood of carrying a \ndistorter than the individual in the true dataset with the highest likelihood. We also \ncalculated the p-value in two other ways:\n1. Within each run of Warp, we normalized all individuals’ likelihoods of carrying a \ndistorter into Z values by subtracting the mean and dividing by the standard deviation. \nThen, we compared the Z value of the highest-likelihood individual in the true pedigree \nto the Z value of the highest-likelihood individual in each permuted pedigree. The \npercentage of permuted pedigree Z values higher than the true pedigree Z value is the \np-value. This test resembles the above, but takes into account the fact that, for reasons \nwe may not have considered, the mean and standard deviation of likelihoods in \npermuted pedigrees may differ from the true pedigree. A low p-value by this method \nindicates that the highest-likelihood individual is a larger outlier from all likelihoods in the \ntrue population than in the permuted populations.\n2. We took the putative originator of the distorter in the true dataset and calculated its \nrank order position in the true population. We then compared this rank order position to \nthe rank order position of the exact same individual in the permuted populations. The p-\nvalue here is the percentage of permuted populations in which this individual’s likelihood \nis higher-ranked in the permuted population compared to the true population. This \ncomparison tests if the true likelihood of the most likely distorter is higher than expected \nin a randomized family, but does not take multiple testing into account the way that the \nabove tests do. A low p-value here indicates that the putative originator of the distorter \nis more likely to carry a distorter than an individual with an identical family tree topology, \nbut randomly permuted offspring sexes.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nClustering Warp results into families\nWarp reports the likelihood of carrying a distorter on a per-individual basis. We know, \nhowever, that many of the individuals that are likely to carry a distorter are closely \nrelated, as clustering of phenotype within families is one of the key ways that Warp \nclassifies individuals as high-likelihood in the first place. To count the unique distorters \nin the population, we created a method for taking the individual-likelihood results of \nWarp and grouping high-likelihood individuals together into closely related families. We \ndid this by simple graph traversal. An individual was considered high-likelihood if their \nlikelihood of carrying a distorter was above 40%. From each high-likelihood individual, \nwe walked outward on the graph of relationships, where each individual was a node and \neach parent-child relationship was an edge. Any high-likelihood individuals within two \nedges of each other (i.e., a grandparent-child relationship or a sibling relationship) were \nconsidered part of the same family. Families could extend further if they consisted of \nmultiple such two-step relationships connected into chains or webs. This algorithm is \navailable in the relative_clusters command in \ngithub.com/jgbaldwinbrown/tdt.\nTransmission disequilibrium test\nIn addition to Warp, we also used the transmission disequilibrium test (Spielman et al. \n1993) (TDT) to test pedigrees for distortion. We included trios in the TDT in a manner \nconsistent with the expected pattern of inheritance of a Y-linked distorter. A valid trio \nconsists of a potential distorter-carrying father, a mother of any genotype, and a child. \nBecause we are searching for Y-linked distorters, any male offspring in such a trio carry \nthe same putatively distorting Y-chromosome, while female offspring do not. We \nidentified all such trios descended from the (presumed) distorter-carrying progenitor of \nthe pedigree. Then, in keeping with the TDT, we performed a chi-squared test of the \nexpectation of equal numbers of male and female progeny with one degree of freedom. \nA low p-value here indicates rejection of the null hypothesis and supports the presence \nof a distorter. Confidence intervals for the TDT were calculated in R using qbinom() with \np = 0.995 and p = 0.005, n = family size, and prob = 0.5.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nKey resources table\nREAGENT or RESOURCE SOURCE IDENTIFIER\nDeposited data\nSoftware and algorithms\nAssorted scripts This paper https://github.com/\njgbaldwinbrown/\njgbutils\nTransmission disequilibrium test This paper https://github.com/\njgbaldwinbrown/tdt\nReferences\nAgarwal A, Mulgund A, Hamada A, Chyatte MR. 2015. A unique view on male infertility \naround the globe. Reprod Biol Endocrinol. 13(1):37. https://doi.org/10.1186/s12958-015-\n0032-1\nArlt MF, Kruger AN, Swanepoel CM, Mueller JL. 2025. Reenacting a mouse genetic \nevolutionary arms race in yeast reveals that SLXL1/SLX compete with SLY1/2 for \nbinding to Spindlins. Proceedings of the National Academy of Sciences. \n122(7):e2421446122. \nhttps://doi.org/10.1073/pnas.2421446122\nBachtrog D. 2013. Y-chromosome evolution: emerging insights into processes of Y-\nchromosome degeneration. Nat Rev Genet. 14(2):113–124. \nhttps://doi.org/10.1038/nrg3366\nBaird SJE et al. 2023. A gene copy number arms race in action: X,Y-chromosome \ntransmission distortion across a species barrier. Evol. 77(6):1330–1340. \nhttps://doi.org/10.1093/evolut/qpad051\nBernoulli J. 1713. Ars coniectandi. Impensis Thurnisiorum, Fratrum.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nBhowmick BK, Satta Y, Takahata N. 2007. The origin and evolution of human \nampliconic gene families and ampliconic structure. Genome Res. 17(4):441–450. \nhttps://doi.org/10.1101/gr.5734907\nBurt A, Trivers R. 2009. Genes in Conflict: The Biology of Selfish Genetic Elements. \nHarvard University Press. [accessed 2024 Nov 11]. \nhttps://www.degruyter.com/document/doi/10.4159/9780674029118/html. \nhttps://doi.org/10.4159/9780674029118\nCampbell P, Heitzmann LD. 2025. Sex ratio drive in mice: A binding competition \nbetween sex-linked genes. Proceedings of the National Academy of Sciences. \n122(10):e2501248122. \nhttps://doi.org/10.1073/pnas.2501248122\nChakraborty R et al. 1996. Segregation distortion of the CTG repeats at the myotonic \ndystrophy locus. Am J Hum Genet. 59(1):109–118\nCocquet J et al. 2012. A Genetic Basis for a Postmeiotic X Versus Y Chromosome \nIntragenomic Conflict in the Mouse. PLOS Genetics. 8(9):e1002900. \nhttps://doi.org/10.1371/journal.pgen.1002900\nDeng L et al. 2009. Constructing an initial map of transmission distortion based on high \ndensity HapMap SNPs across the human autosomes. Journal of Genetics and \nGenomics. 36(12):703–709. https://doi.org/10.1016/S1673-8527(08)60163-0\nEaves IA et al. 1999. Transmission ratio distortion at the INS-IGF2 VNTR. Nat Genet. \n22(4):324–325. https://doi.org/10.1038/11890\nEvans K et al. 1994. Genetic linkage of cone–rod retinal dystrophy to chromosome 19q \nand evidence for segregation distortion. Nat Genet. 6(2):210–213. \nhttps://doi.org/10.1038/ng0294-210\nFisher RA. 1958. The genetical theory of natural selection: a complete variorum edition. \nOxford University Press. [accessed 2025 Aug 28]. https://books.google.com/books?\nhl=en&lr=&id=sT4lIDk5no4C&oi=fnd&pg=PR6&dq=r+a+fisher+genetical+theory+of+nat\nural+selection&ots=oGMe2D2X5d&sig=vvcXdqPJfldmFkPx7HV6JROgP3A\nFishman L, Aagaard J, Tuthill JC. 2008. Toward the evolutionary genomics of \ngametophytic divergence: patterns of transmission ratio distortion in monkeyflower \n(Mimulus) hybrids reveal a complex genetic basis for conspecific pollen precedence. \nEvolution. 62(12):2958–2970. https://doi.org/10.1111/j.1558-5646.2008.00475.x\nFrazer. 2007. A second generation human haplotype map of over 3.1 million SNPs. \nNature. 449(7164):851–861. https://doi.org/10.1038/nature06258\nGirardet A et al. 2000. Meiotic Segregation Analysis of RB1 Alleles in Retinoblastoma \nPedigrees by Use of Single-Sperm Typing. The American Journal of Human Genetics. \n66(1):167–175. https://doi.org/10.1086/302715\nGraffelman J, Hoekstra RF. 2000. A Statistical Analysis of the Effect of Warfare on the \nHuman Secondary Sex Ratio. Human Biology. 72(3):433–445\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nHallast P et al. 2021. A Southeast Asian origin for present-day non-African human Y \nchromosomes. Hum Genet. 140(2):299–307. https://doi.org/10.1007/s00439-020-\n02204-9\nHelleu Q, Gérard PR, Montchamp-Moreau C. 2015. Sex Chromosome Drive. Cold \nSpring Harb Perspect Biol. 7(2):a017616. https://doi.org/10.1101/cshperspect.a017616\nHickey WA, Craig GB. 1966. Genetic Distortion of Sex Ratio in a Mosquito, AEDES \nAEGYPTI. Genetics. 53(6):1177–1196\nJaenike J. 2001. Sex Chromosome Meiotic Drive. Annual Review of Ecology, Evolution, \nand Systematics. 32(Volume 32, 2001):25–49. \nhttps://doi.org/10.1146/annurev.ecolsys.32.081501.113958\nJobling MA, Tyler-Smith C. 2017. Human Y-chromosome variation in the genome-\nsequencing era. Nat Rev Genet. 18(8):485–497. https://doi.org/10.1038/nrg.2017.36\nKim J, Pearl J. 1983. A computational model for causal and diagnostic reasoning in \ninference systems. In: International Joint Conference on Artificial Intelligence. Citeseer; \np 0–0 [accessed 2024 Aug 29]. https://citeseerx.ist.psu.edu/document?\nrepid=rep1&type=pdf&doi=bab9e16cdc4088cf62f89e76c12a5b31af0a1802\nKingan SB, Garrigan D, Hartl DL. 2010. Recurrent Selection on the Winters sex-ratio \nGenes in Drosophila simulans. Genetics. 184(1):253–265. \nhttps://doi.org/10.1534/genetics.109.109587\nKruger AN et al. 2019. A Neofunctionalized X-Linked Ampliconic Gene Family Is \nEssential for Male Fertility and Equal Sex Ratio in Mice. Current Biology. 29(21):3699-\n3706.e5. https://doi.org/10.1016/j.cub.2019.08.057\nLindholm AK et al. 2016. The Ecology and Evolutionary Dynamics of Meiotic Drive. \nTrends in Ecology & Evolution. 31(4):315–326. \nhttps://doi.org/10.1016/j.tree.2016.02.001\nLucotte EA et al. 2018. Dynamic Copy Number Evolution of X- and Y-Linked Ampliconic \nGenes in Human Populations. Genetics. 209(3):907–920. \nhttps://doi.org/10.1534/genetics.118.300826\nLyon MF. 2003. Transmission ratio distortion in mice. Annu Rev Genet. 37:393–408. \nhttps://doi.org/10.1146/annurev.genet.37.110801.143030\nLyttle TW. 1977. Experimental Population Genetics of Meiotic Drive Systems I. Pseudo-\nY Chromosomal Drive as a Means of Eliminating Cage Populations of DROSOPHILA \nMELANOGASTER. Genetics. 86(2):413–445\nMatsuda E et al. 2011. A homolog of male sex-determining factor SRY cooperates with \na transposon-derived CENP-B protein to control sex-specific directed recombination. \nPNAS. 201109988. https://doi.org/10.1073/pnas.1109988108\nMeyer WK et al. 2012. Evaluating the Evidence for Transmission Distortion in Human \nPedigrees. Genetics. 191(1):215–232. https://doi.org/10.1534/genetics.112.139576\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nMorgan TH, Bridges CB, Sturtevant AH. 1925. The genetics of Drosophila \nmelanogaster. Biblphia genet. 2:1–262\nNaumova AK et al. 1998. Parental Origin–Dependent, Male Offspring–Specific \nTransmission-Ratio Distortion at Loci on the Human X Chromosome. The American \nJournal of Human Genetics. 62(6):1493–1499. https://doi.org/10.1086/301860\nNaumova AK, Greenwood CM, Morgan K. 2001. Imprinting and deviation from \nMendelian transmission ratios. Genome. 44(3):311–320. https://doi.org/10.1139/g01-\n013\nPaterson AD et al. 2009. Transmission-ratio distortion in the Framingham Heart Study. \nBMC Proceedings. 3(7):S51. https://doi.org/10.1186/1753-6561-3-S7-S51\nRidges JT et al. 2026. Selfish chromosomes exploit a germline checkpoint to eliminate \ncompeting gametes. Nat Commun. 17(1):1532. \nhttps://doi.org/10.1038/s41467-025-\n68254-7\nSandler L, Novitski E. 1957. Meiotic Drive as an Evolutionary Force. The American \nNaturalist. 91(857):105–110. https://doi.org/10.1086/281969\nSankararaman S et al. 2014. The genomic landscape of Neanderthal ancestry in \npresent-day humans. Nature. 507(7492):354–357. https://doi.org/10.1038/nature12961\nSantos PSC et al. 2009. Assessment of transmission distortion on chromosome 6p in \nhealthy individuals using tagSNPs. Eur J Hum Genet. 17(9):1182–1189. \nhttps://doi.org/10.1038/ejhg.2009.16\nSen A. 2003. Missing women—revisited. BMJ. 327(7427):1297–1298\nSharlip ID et al. 2002. Best practice policies for male infertility. Fertility and sterility. \n77(5):873–882\nSkov L et al. 2023. Extraordinary selection on the human X chromosome associated \nwith archaic admixture. Cell Genomics. 3(3) [accessed 2025 Nov 13]. \nhttps://www.cell.com/cell-genomics/abstract/S2666-979X(23)00034-4. \nhttps://doi.org/10.1016/j.xgen.2023.100274\nSlattery ML, Kerber RA. 1993. A Comprehensive Evaluation of Family History and \nBreast Cancer Risk: The Utah Population Database. JAMA. 270(13):1563–1568. \nhttps://doi.org/10.1001/jama.1993.03510130069033\nSmith KR, Mineau GP. 2021. The Utah Population Database. The Legacy of Four \nDecades of Demographic Research. Historical Life Course Studies. 11:48–73. \nhttps://doi.org/10.51964/hlcs10916\nSpielman RS, McGinnis RE, Ewens WJ. 1993. Transmission test for linkage \ndisequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). \nAm J Hum Genet. 52(3):506–516\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nStephenson TA. 2000. An Introduction to Bayesian Network Theory and Usage. \n[accessed 2024 Aug 29]. https://infoscience.epfl.ch/handle/20.500.14299/227920\nSturtevant AH, Dobzhansky Th. 1936. Geographical Distribution and Cytology of ‘Sex \nRatio’ in Drosophila Pseudoobscura and Related Species. Genetics. 21(4):473–490\nSwanepoel CM, Mueller JL. 2024. Out with the old, in with the new: Meiotic driving of \nsex chromosome evolution. Seminars in Cell & Developmental Biology. 163:14–21. \nhttps://doi.org/10.1016/j.semcdb.2024.04.004\nSweeny TL, Barr AR. 1978. Sex Ratio Distortion Caused by Meiotic Drive in a Mosquito, \nCulex pipiens L. Genetics. 88(3):427–446\nTurner BC, Perkins DD. 1979. Spore Killer, a Chromosomal Factor in Neurospora That \nKills Meiotic Products Not Containing It. Genetics. 93(3):587–606\nVegesna R, Tomaszkiewicz M, Medvedev P, Makova KD. 2019. Dosage regulation, and \nvariation in gene expression and copy number of human Y chromosome ampliconic \ngenes. PLOS Genetics. 15(9):e1008369. https://doi.org/10.1371/journal.pgen.1008369\nWang S et al. 2025. Is sex at birth a biological coin toss? Insights from a longitudinal \nand GWAS analysis. Science Advances. 11(29):eadu7402. \nhttps://doi.org/10.1126/sciadv.adu7402\nWinkler L, Lindholm AK. 2022. A meiotic driver alters sperm form and function in house \nmice: a possible example of spite. Chromosome Res. 30(2):151–164. \nhttps://doi.org/10.1007/s10577-022-09695-4\nYe D et al. 2018. High Levels of Copy Number Variation of Ampliconic Genes across \nMajor Human Y Haplogroups. Genome Biol Evol. 10(5):1333–1350. \nhttps://doi.org/10.1093/gbe/evy086\nZietsch BP et al. 2020. No genetic contribution to variation in human offspring sex ratio: \na total population study of 4.7 million births. Proceedings of the Royal Society B: \nBiological Sciences. 287(1921):20192849. https://doi.org/10.1098/rspb.2019.2849\nZöllner S et al. 2004. Evidence for Extensive Transmission Distortion in the Human \nGenome. The American Journal of Human Genetics. 74(1):62–72. \nhttps://doi.org/10.1086/381131\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSupplementary Materials\nExample Bayesian calculations used by WARP\nAssume the allele D is dominant, makes all carriers affected (100% penetrant), and has \nan allele frequency of 0.01. Here, M and F refer to affected (i.e., male) and unaffected \n(i.e., female).\nBayes’s theorem applied to probabilities of parental genotypes:\nP ( M )= P ( dd )\n2 +P ( Dd ) + P ( DD )= p\n2\n2 +2 pq+q\n2\n= 0.9801\n2 +0.0198+0.0001=0.50995\nP ( dd ∨ M )= P ( M ∨ dd ) P ( dd )\nP ( M ) = 0.5 ⋅ 0.9801\n0.50995 =0.9609765.. .\nP ( Dd ∨ M )= P ( M ∨ Dd ) P ( Dd )\nP ( M ) = 1⋅ 0.0198\n0.50995 =0.38827 .. .\nP ( DD ∨ M )= P ( M ∨ DD ) P ( DD )\nP ( M ) = 1⋅ 0.0001\n0.50995 =0.000196097 .. .\nP ( dd ∨ F )= P ( F ∨ dd ) P ( dd )\nP ( F ) = 0.5 ⋅ 0.9801\n1− 0.50995 =1\nThus, the probabilities of genotypes of offspring are as below. Bayes’ rule can be further \napplied to update these probabilities if the sex of the offspring is known. Here, the \nfather’s phenotype will be marked fF or fM, and the mother’s will be marked mF or mM. \nAdditionally, the event of the two parents having opposite affected statuses, equivalent \nto \nfF ∩ mM ∪ fM ∩ mF, will be notated as H.\nP ( dd ∨ fF ∩ mF )=1\nP ( DD ∨ H )=0\nP ( Dd ∨ H )=P ( DD ∨ fM ) + P ( Dd ∨ fM )\n2 =0.000196+ 0.0388\n2 =0.01959\nP ( dd ∨ H )=1 − P ( Dd ∨ H )=1 − 0.1959=0.98041\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nP ( dd ∨ fM ∩ mM )=P ( dd ∨ M )\n2\n+ P ( Dd ∨ M )\n2\n4 + 2 P ( dd ∨ M ) P ( Dd ∨ M )\n2\n...=0.9609\n2\n+ 0.0388\n2\n4 + 2⋅ 0.9609 ⋅ 0.0388\n2 =0.9609 ...\nP ( Dd ∨ fM ∩ mM )= 2 P ( Dd ∨ M ) P ( dd ∨ M )\n2 + P ( Dd )\n2\n2 + 2 P ( Dd ) P ( DD )\n2 + 2 P ( DD ) P ( dd )\n1 =0.03842 ...\nP ( DD ∨ fM ∩ mM )= P ( Dd )\n2\n4 + 2 P ( Dd ) P ( DD )\n2 + P ( DD )\n2\n=0.00038400.. .\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSupplementary figures\nSupplementary Figure 1. WARP has high power to infer the likelihood of carrying a \ndistorter in pedigrees regardless of penetrance, inheritance pattern, and other \nchallenges. WARP propagates information about the likelihood of carrying a distorter \nfrom parent to child, and from child to parent, using Bayesian network propagation (a \ncombination of Bayes’ rule and the chain rule). Here, we depict the probability of two \nparents transmitting a dominant distorter, D, to a child, assuming that the Dd and DD \ngenotypes cause the affected status 100% of the time, the dd genotype causes the \naffected status 50% of the time, and the population allele frequency for D is 0.01. The \nparental genotype likelihoods are calculated using Bayes’ rule, and the child’s expected \ngenotypes are calculated using the chain rule. See Supplementary Materials for detail. \nIn our study, these assumptions would be used when looking for a Y-chromosome \ndistorter with a 100% distortion ratio and an allele frequency of 0.01.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSupplementary Figure 2. Likelihoods for highly Y-distorted individuals lie outside \nthe distribution of simulated individuals. This Q-Q plot shows the distribution of \nBayesian likelihoods of carrying a Y-biased distorter in the true data (Y axis) and in \nsimulated data (X axis, see Methods for details). The black line is the line of 1:1 \ncorrespondence, and the blue line represents the 95th percentile (95% of all data is left \nof this line). There is a notable deviation away from the 1:1 line in a small number of \nindividuals, most of which are in the putative Y-distorting family (family cluster 2, red).\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint \n\nSupplementary figure 3. Likelihoods for highly X-distorted individuals lie outside \nthe distribution of simulated individuals. This Q-Q plot shows the distribution of \nBayesian likelihoods of carrying an X-biased distorter in the true data (Y axis) and in \nsimulated data (X axis, see Methods for details). The black line is the line of 1:1 \ncorrespondence, and the blue line represents the 95th percentile (95% of all data is left \nof this line). There is a notable deviation away from the 1:1 line in a small number of \nindividuals.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 23, 2026. ; https://doi.org/10.64898/2026.02.04.702084doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}