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Since the first half of the twentieth century, evolutionary theory has been dominated by the idea that mutations occur randomly with respect to their consequences1. Here we test this assumption with large surveys of de novo mutations in the plant Arabidopsis thaliana. In contrast to expectations, we find that mutations occur less often in functionally constrained regions of the genome—mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. With independent genomic mutation datasets, including from the largest Arabidopsis mutation accumulation experiment conducted to date, we demonstrate that epigenomic and physical features explain over 90% of variance in the genome-wide pattern of mutation bias surrounding genes. Observed mutation frequencies around genes in turn accurately predict patterns of genetic polymorphisms in natural Arabidopsis accessions (r = 0.96). That mutation bias is the primary force behind patterns of sequence evolution around genes in natural accessions is supported by analyses of allele frequencies. Finally, we find that genes subject to stronger purifying selection have a lower mutation rate. We conclude that epigenome-associated mutation bias2 reduces the occurrence of deleterious mutations in Arabidopsis, challenging the prevailing paradigm that mutation is a directionless force in evolution.

Summary The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p

Fields such as behavioural and evolutionary ecology are built on the assumption that natural selection leads to organisms that behave as if they are trying to maximise their fitness. However, there is considerable evidence for selfish genetic elements that change the behaviour of individuals to increase their own transmission. How can we reconcile this contradiction? Here we show that: (1) when selfish genetic elements have a greater impact at the individual level, they are more likely to be suppressed, and suppression spreads more quickly; (2) selection on selfish genetic elements leads them towards a greater impact at the individual level, making them more likely to be suppressed; (3) the majority interest within the genome generally prevails over ‘cabals’ of a few genes, irrespective of genome size, mutation rate and the sophistication of trait distorters. Overall, our results suggest that even when there is the potential for considerable genetic conflict, this will often have negligible impact at the individual level., The ‘parliament of genes’ hypothesis suggests that selfish genetic elements will be counteracted by suppressors that maintain equal transmission of the rest of the genome. Here, the authors find support for this hypothesis using mathematical models to explore a range of different scenarios.

Hox genes are crucial players in the generation and pattering of the vertebrate trunk and posterior body during embryogenesis. Their initial expression takes place shortly after the establishment of the primitive streak, in the posterior-most part of the mouse embryo and is a determinant step for setting up the definitive Hox expression boundaries along the antero-posterior body axis. The developmental signals and epigenetic mechanisms underlying this early activation remained unsolved until recently. The development of novel embryo-derived model systems, combined with methods that examine chromatin status and chromosome conformation, led to deeper understanding of the process of Hox activation in the early embryo. Here we summarize how the early Hox cis-regulatory landscape becomes active upon receiving the appropriate developmental signal, and we discuss the importance of the local topological segmentation of the HoxA cluster during early Hox activation.

During eukaryotic transcription, RNA polymerase (RNAP) translocates along DNA molecules covered with nucleosomes and other DNA binding proteins. Though the interactions between a single nucleosome and RNAP are by now fairly well understood, this understanding has not been synthesized into a description of transcription on crowded genes, where multiple RNAP transcribe through nucleosomes while preserving the nucleosome coverage. We here take a deductive modeling approach to establish the consequences of RNAP–nucleosome interactions for transcription in crowded environments. We show that under physiologically crowded conditions, the interactions of RNAP with nucleosomes induce a strong kinetic attraction between RNAP molecules, causing them to self-organize into stable and moving pelotons. The peloton formation quantitatively explains the observed nucleosome and RNAP depletion close to the initiation site on heavily transcribed genes. Pelotons further translate into short-timescale transcriptional bursts at termination, resulting in burst characteristics consistent with instances of bursty transcription observed in vivo. To facilitate experimental testing of our proposed mechanism, we present several analytic relations that make testable quantitative predictions.

Quantitative Trait Locus (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as theSolanum pennelliiIntrogression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar andS. pennelli. This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix ofBC2andBC3lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existingS. pennelliiIL population, with which it shares parents. Using the BILs we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous Hidden Markov Model for genotyping the lines, and by using state of the art sparse regression techniques for QTL mapping.

Whole-exome sequencing has enabled new approaches for discovering genes associated with monogenic disorders. One such approach is gene-based burden testing, in which the aggregate frequency of “qualifying variants” is compared between case and control subjects for each gene. Despite substantial successes of this approach, the genetic causes for many monogenic disorders remain unknown or only partially known. It is possible that particular genetic architectures lower rates of discovery, but the influence of these factors on power has not been rigorously evaluated. Here, we leverage large-scale exome-sequencing data to create an empirically based simulation framework to evaluate the impact of key parameters (background variation rates, locus heterogeneity, mode of inheritance, penetrance) on power in gene-based burden tests in the context of monogenic disorders. Our results demonstrate that across genes, there is a wide range in sample sizes needed to achieve power due to differences in the background rate of rare variants in each gene. Increasing locus heterogeneity results in rapid increases in sample sizes needed to achieve adequate power, particularly when individual genes contribute to less than 5% of cases under a dominant model. Interestingly, incomplete penetrance as low as 10% had little effect on power due to the low prevalence of monogenic disorders. Our results suggest that moderate incomplete penetrance is not an obstacle in this gene-based burden testing approach but that dominant disorders with high locus heterogeneity will require large sample sizes. Our simulations also provide guidance on sample size needs and inform study design under various genetic architectures.

Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome segregation. Measuring homolog nondisjunction frequencies is laborious, and involves dissecting thousands of tetrads to detect missegregation of individually marked chromosomes. Here we describe a computational method (TetFit) to estimate the relative contributions of meiosis I nondisjunction and random-spore death to spore inviability in wild type and mutant strains. These values are based on finding the best-fit distribution of 4, 3, 2, 1, and 0 viable-spore tetrads to an observed distribution. Using TetFit, we found that meiosis I nondisjunction is an intrinsic component of spore inviability in wild-type strains. We show proof-of-principle that the calculated average meiosis I nondisjunction frequency determined by TetFit closely matches empirically determined values in mutant strains. Using these published data sets, TetFit uncovered two classes of mutants: Class A mutants skew toward increased nondisjunction death, and include those with known defects in establishing pairing, recombination, and/or synapsis of homologous chromosomes. Class B mutants skew toward random spore death, and include those with defects in sister-chromatid cohesion and centromere function. Epistasis analysis using TetFit is facilitated by the low numbers of tetrads (as few as 200) required to compare the contributions to spore death in different mutant backgrounds. TetFit analysis does not require any special strain construction, and can be applied to previously observed tetrad distributions.

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels., Dominance is difficult to measure in natural populations as it is confounded with fitness. Here, Huber et al. developed a new approach to co-estimate dominance and selection coefficients, and found that the observed relationship is best fit by a new model of dominance based on gene expression level.

Carcinogenesis is an evolutionary process whereby cells accumulate multiple mutations. Besides the ‘driver mutations’ that cause the disease, cells also accumulate a number of other mutations with seemingly no direct role in this evolutionary process. They are called passenger mutations. While it has been argued that passenger mutations render tumours more fragile due to reduced fitness, the role of passenger mutations remains understudied. Using evolutionary computational models, we demonstrate that in the context of tumour suppressor gene inactivation (and hence fitness valley crossing), the presence of passenger mutations can accelerate the rate of evolution by reducing overall population fitness and increasing the relative fitness of intermediate mutants in the fitness valley crossing pathway. Hence, the baseline rate of tumour suppressor gene inactivation might be faster than previously thought. Conceptually, parallels are found in the field of turbulence and pattern formation, where instabilities can be driven by perturbations that are damped (disadvantageous), but provide a richer set of pathways such that a system can achieve some desired goal more readily. This highlights, through a number of novel parallels, the relevance of physical sciences in oncology.

Polyploidy is increasingly seen as a driver of both evolutionary innovation and ecological success. One source of polyploid organisms’ successes may be their origins in the merging and mixing of genomes from two different species (e.g., allopolyploidy). Using POInT (the Polyploid Orthology Inference Tool), we model the resolution of three allopolyploidy events, one from the bakers’ yeast (Saccharomyces cerevisiae), one from the thale cress (Arabidopsis thaliana) and one from grasses including Sorghum bicolor. Analyzing a total of 21 genomes, we assign to every gene a probability for having come from each parental subgenome (i.e., derived from the diploid progenitor species), yielding orthologous segments across all genomes. Our model detects statistically robust evidence for the existence of biased fractionation in all three lineages, whereby genes from one of the two subgenomes were more likely to be lost than those from the other subgenome. We further find that a driver of this pattern of biased losses is the co-retention of genes from the same parental genome that share functional interactions. The pattern of biased fractionation after the Arabidopsis and grass allopolyploid events was surprisingly constant in time, with the same parental genome favored throughout the lineages’ history. In strong contrast, the yeast allopolyploid event shows evidence of biased fractionation only immediately after the event, with balanced gene losses more recently. The rapid loss of functionally associated genes from a single subgenome is difficult to reconcile with the action of genetic drift and suggests that selection may favor the removal of specific duplicates. Coupled to the evidence for continuing, functionally-associated biased fractionation after the A. thaliana At-α event, we suggest that, after allopolyploidy, there are functional conflicts between interacting genes encoded in different subgenomes that are ultimately resolved through preferential duplicate loss., Author summary Genome duplications/polyploidies can transiently double an organism’s gene content. However, this doubled condition is unstable and descendants of polyploid founders rapidly lose many of their duplicate genes. Here, we describe a phylogenomic pipeline that allows us to trace this history of gene loss across a set of modern genomes that all descend from the same three polyploidy events (two in flowering plants and one in yeasts). Polyploidy often occurs by the hybridization of related but not identical lineages, and previous studies in single polyploid genomes have identified a tendency for one lineage to lose fewer genes after polyploidy than the other (known as biased fractionation). However, single genome studies can be misled into inferring biased fractionation by incorrectly assuming that short regions of shared gene order are derived from the same parental genome. By phylogenetically modeling the resolution of these three polyploidy events across 21 genomes (10 plants and 11 yeasts), we confirm the existence of biased fractionation in plants and provide new evidence for it in yeasts (where it occurred only for a short interval post-polyploidy). We also show that genes from alternative parental genomes tend to encode products that do not physically interact, suggesting that selection to maintain function in co-adapted complexes helped to drive this bias in loss patterns.

Background System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models. Results The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure. Conclusions The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans. Electronic supplementary material The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users.

Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles - a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of dominance and selection coefficients - quantities that remain poorly characterized for current genomic data sets.

Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding., Author summary A key long-term goal of biology is understanding the genetic basis of phenotypic variation. Although most new mutations are likely disadvantageous, their prevalence and importance in explaining patterns of phenotypic variation is controversial and not well understood. In this study we combine whole genome-sequencing and field evaluation of a maize mapping population to investigate the contribution of deleterious mutations to phenotype. We show that a priori prediction of deleterious alleles correlates well with effect sizes for grain yield and that variants predicted to be more damaging are on average more recessive. We develop a simple model allowing for variation in the heterozygous effects of deleterious mutations and demonstrate its improved ability to predict both phenotypes and hybrid vigor. Our results help reconcile alternative explanations for hybrid vigor and highlight the use of leveraging evolutionary history to facilitate breeding for crop improvement.

Evolution can favor organisms that are more adaptable, provided that genetic variation in adaptability exists. Here, we quantify this variation among 230 offspring of a cross between diverged yeast strains. We measure the adaptability of each offspring genotype, defined as its average rate of adaptation in a specific environmental condition, and analyze the heritability, predictability, and genetic basis of this trait. We find that initial genotype strongly affects adaptability and can alter the genetic basis of future evolution. Initial genotype also affects the pleiotropic consequences of adaptation for fitness in a different environment. This genetic variation in adaptability and pleiotropy is largely determined by initial fitness, according to a rule of declining adaptability with increasing initial fitness, but several individual QTLs also have a significant idiosyncratic role. Our results demonstrate that both adaptability and pleiotropy are complex traits, with extensive heritable differences arising from naturally occurring variation.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X’s gene content, gene expression, and evolution., Laurence Hurst, Lukasz Huminiecki, and the FANTOM5 consortium propose a new explanation for the peculiar expression properties of genes on the human X chromosome, based on the premise that very high expression levels cannot be achieved on a haploid-expressed chromosome., Author Summary Genes located on the human X chromosome are not a random mix of genes: they tend to be expressed in relatively few tissues or are specific for a particular set of tissues, e.g., brain regions. Prior attempts to explain this skewed gene content have hypothesized that the X chromosome might be peculiar because it has to balance mutations that are advantageous to one sex but deleterious to the other, or because it has to shut down during the process of sperm manufacture in males. Here we suggest and test a third possible explanation: that genes on the X chromosome are limited in their transcription levels and thus tend to be genes that are lowly or specifically expressed. We consider the suggestion that since these genes can only be expressed from one chromosome, as males only have one X, the ability to express a gene at very high rates is limited owing to potential transcriptional traffic jams. As predicted, we find that human X-located genes have maximal expression rates far below that of genes residing on autosomes. When we look at genes that have moved onto or off the X chromosome during recent evolution, we find the maximal expression is higher when not on the X chromosome. We also find that X-located genes that are relatively highly expressed are not able to increase their expression level further. Our model explains both the enrichment for tissue specificity and the paucity of certain tissues with X-located genes. Genes underrepresented on the X are either expressed in many tissues—such genes tend to have high maximal expression—or are from tissues that require a lot of transcription (e.g., fast secreting tissues like the liver). Just as many of the findings cannot be explained by the two earlier models, neither can the traffic jam model explain all the peculiar features of the genes found on the X chromosome. Indeed, we find evidence of a reproduction-related bias in X-located genes, even after allowing for the traffic jam problem.

Changes in gene copy number are among the most frequent mutational events in all genomes and were among the mutations for which a physical basis was first known. Yet mechanisms of gene duplication remain uncertain because formation rates are difficult to measure and mechanisms may vary with position in a genome. Duplications are compared here to deletions, which seem formally similar but can arise at very different rates by distinct mechanisms. Methods of assessing duplication rates and dependencies are described with several proposed formation mechanisms. Emphasis is placed on duplications formed in extensively studied experimental situations. Duplications studied in microbes are compared with those observed in metazoan cells, specifically those in genomes of cancer cells. Duplications, and especially their derived amplifications, are suggested to form by multistep processes often under positive selection for increased copy number.

When messenger RNA splicing occurs co-transcriptionally, the potential for kinetic control based on transcription dynamics is widely recognized. Indeed, perturbation studies have reported that when transcription kinetics are perturbed genetically or pharmacologically splice patterns may change. However, whether kinetic control is contributing to the control of splicing within the normal range of physiological conditions remains unknown. We examined if the kinetic determinants for co-transcriptional splicing (CTS) might be reflected in the structure and expression patterns of the genome and epigenome. To identify and then quantitatively relate multiple, simultaneous CTS determinants, we constructed a scalable mathematical model of the kinetic interplay of RNA synthesis and CTS and parameterized it with diverse next generation sequencing (NGS) data. We thus found a variety of CTS determinants encoded in vertebrate genomes and epigenomes, and that these combine variously for different groups of genes such as housekeeping versus regulated genes. Together, our findings indicate that the kinetic basis of splicing is functionally and physiologically relevant, and may meaningfully inform the analysis of genomic and epigenomic data to provide insights that are missed when relying on statistical approaches alone.

Next-generation sequencing of human tumours has refined our understanding of the mutational processes operative in cancer initiation and progression, yet major questions remain regarding the factors that induce driver mutations and the processes that shape mutation selection during tumorigenesis. Here we performed whole-exome sequencing on adenomas from three mouse models of non-small-cell lung cancer, which were induced either by exposure to carcinogens (methyl-nitrosourea (MNU) and urethane) or by genetic activation of Kras (Kras(LA2)). Although the MNU-induced tumours carried exactly the same initiating mutation in Kras as seen in the Kras(LA2) model (G12D), MNU tumours had an average of 192 non-synonymous, somatic single-nucleotide variants, compared with only six in tumours from the Kras(LA2) model. By contrast, the Kras(LA2) tumours exhibited a significantly higher level of aneuploidy and copy number alterations compared with the carcinogen-induced tumours, suggesting that carcinogen-induced and genetically engineered models lead to tumour development through different routes. The wild-type allele of Kras has been shown to act as a tumour suppressor in mouse models of non-small-cell lung cancer. We demonstrate that urethane-induced tumours from wild-type mice carry mostly (94%) Kras Q61R mutations, whereas those from Kras heterozygous animals carry mostly (92%) Kras Q61L mutations, indicating a major role for germline Kras status in mutation selection during initiation. The exome-wide mutation spectra in carcinogen-induced tumours overwhelmingly display signatures of the initiating carcinogen, while adenocarcinomas acquire additional C > T mutations at CpG sites. These data provide a basis for understanding results from human tumour genome sequencing, which has identified two broad categories of tumours based on the relative frequency of single-nucleotide variations and copy number alterations, and underline the importance of carcinogen models for understanding the complex mutation spectra seen in human cancers.

Sexual conflict likely plays a crucial role in the origin and maintenance of homosexuality in our species. Although environmental factors are known to affect human homosexual (HS) preference, sibling concordances and population patterns related to HS indicate that genetic components are also influencing this trait in humans. We argue that multilocus, partially X-linked genetic factors undergoing sexually antagonistic selection that promote maternal female fecundity at the cost of occasional male offspring homosexuality are the best candidates capable of explaining the frequency, familial clustering, and pedigree asymmetries observed in HS male proband families. This establishes male HS as a paradigmatic example of sexual conflict in human biology. HS in females, on the other hand, is currently a more elusive phenomenon from both the empirical and theoretical standpoints because of its fluidity and marked environmental influence. Genetic and epigenetic mechanisms, the latter involving sexually antagonistic components, have been hypothesized for the propagation and maintenance of female HS in the population. However, further data are needed to truly clarify the evolutionary dynamics of this trait.