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A reply to Harden's response to Coop and Przeworski (2022).

A book review of "The genetic lottery: why DNA matters for social equality." (Princeton University Press, 2021) by Kathryn Paige Harden.

In humans, most germline mutations are inherited from the father. This observation has been widely interpreted as reflecting the replication errors that accrue during spermatogenesis. If so, the male bias in mutation should be substantially lower in a closely related species with similar rates of spermatogonial stem cell divisions but a shorter mean age of reproduction. To test this hypothesis, we resequenced two 3-4 generation nuclear families (totaling 29 individuals) of olive baboons (Papio anubis), who reproduce at approximately 10 years of age on average, and analyzed the data in parallel with three 3-generation human pedigrees (26 individuals). We estimated a mutation rate per generation in baboons of 0.57×10-8 per base pair, approximately half that of humans. Strikingly, however, the degree of male bias in germline mutations is approximately 4:1, similar to that of humans-indeed, a similar male bias is seen across mammals that reproduce months, years, or decades after birth. These results mirror the finding in humans that the male mutation bias is stable with parental ages and cast further doubt on the assumption that germline mutations track cell divisions. Our mutation rate estimates for baboons raise a further puzzle, suggesting a divergence time between apes and Old World monkeys of 65 million years, too old to be consistent with the fossil record; reconciling them now requires not only a slowdown of the mutation rate per generation in humans but also in baboons.

Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or average treatment effects; ATEs) of alleles, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. This feature will matter if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in linkage disequilibrium patterns. At a single locus, family-based GWAS can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores (PGSs), however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate of the LATE for any subset or weighted average of families. In practice, the potential biases of a family-based GWAS are likely smaller than those that can arise from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, their causal interpretation is less straightforward than has been widely appreciated. [ABSTRACT FROM AUTHOR]

The rates at which mutations accumulate across human cell types vary. To identify causes of this variation, mutations are often decomposed into a combination of the single-base substitution (SBS) "signatures" observed in germline, soma, and tumors, with the idea that each signature corresponds to one or a small number of underlying mutagenic processes. Two such signatures turn out to be ubiquitous across cell types: SBS signature 1, which consists primarily of transitions at methylated CpG sites thought to be caused by spontaneous deamination, and the more diffuse SBS signature 5, which is of unknown etiology. In cancers, the number of mutations attributed to these 2 signatures accumulates linearly with age of diagnosis, and thus the signatures have been termed "clock-like." To better understand this clock-like behavior, we develop a mathematical model that includes DNA replication errors, unrepaired damage, and damage repaired incorrectly. We show that mutational signatures can exhibit clock-like behavior because cell divisions occur at a constant rate and/or because damage rates remain constant over time, and that these distinct sources can be teased apart by comparing cell lineages that divide at different rates. With this goal in mind, we analyze the rate of accumulation of mutations in multiple cell types, including soma as well as male and female germline. We find no detectable increase in SBS signature 1 mutations in neurons and only a very weak increase in mutations assigned to the female germline, but a significant increase with time in rapidly dividing cells, suggesting that SBS signature 1 is driven by rounds of DNA replication occurring at a relatively fixed rate. In contrast, SBS signature 5 increases with time in all cell types, including postmitotic ones, indicating that it accumulates independently of cell divisions; this observation points to errors in DNA repair as the key underlying mechanism. Thus, the 2 "clock-like" signatures observed across cell types likely have distinct origins, one set by rates of cell division, the other by damage rates. The rate at which DNA mutations accumulate varies substantially across cell types and tissues in the human body, but the origin of these differences is not well understood. This study develops a model to interpret "clock-like" mutation accumulation in humans, showing that the rate of accumulation depends on underlying processes of DNA damage, repair and replication. [ABSTRACT FROM AUTHOR]

Our understanding of the chronology of human evolution relies on the "molecular clock" provided by the steady accumulation of substitutions on an evolutionary lineage. Recent analyses of human pedigrees have called this understanding into question by revealing unexpectedly low germline mutation rates, which imply that substitutions accrue more slowly than previously believed. Translating mutation rates estimated from pedigrees into substitution rates is not as straightforward as it may seem, however. We dissect the steps involved, emphasizing that dating evolutionary events requires not "a mutation rate" but a precise characterization of how mutations accumulate in development in males and females-knowledge that remains elusive.

Events in primate evolution are often dated by assuming a constant rate of substitution per unit time, but the validity of this assumption remains unclear. Among mammals, it is well known that there exists substantial variation in yearly substitution rates. Such variation is to be expected from differences in life history traits, suggesting it should also be found among primates. Motivated by these considerations, we analyze whole genomes from 10 primate species, including Old World Monkeys (OWMs), New World Monkeys (NWMs), and apes, focusing on putatively neutral autosomal sites and controlling for possible effects of biased gene conversion and methylation at CpG sites. We find that substitution rates are up to 64% higher in lineages leading from the hominoid-NWM ancestor to NWMs than to apes. Within apes, rates are ∼2% higher in chimpanzees and ∼7% higher in the gorilla than in humans. Substitution types subject to biased gene conversion show no more variation among species than those not subject to it. Not all mutation types behave similarly, however; in particular, transitions at CpG sites exhibit a more clocklike behavior than do other types, presumably because of their nonreplicative origin. Thus, not only the total rate, but also the mutational spectrum, varies among primates. This finding suggests that events in primate evolution are most reliably dated using CpG transitions. Taking this approach, we estimate the human and chimpanzee divergence time is 12.1 million years,​ and the human and gorilla divergence time is 15.1 million years​.

The study of human evolution has been revolutionized by inferences from ancient DNA analyses. Key to these studies is the reliable estimation of the age of ancient specimens. High-resolution age estimates can often be obtained using radiocarbon dating, and, while precise and powerful, this method has some biases, making it of interest to directly use genetic data to infer a date for samples that have been sequenced. Here, we report a genetic method that uses the recombination clock. The idea is that an ancient genome has evolved less than the genomes of present-day individuals and thus has experienced fewer recombination events since the common ancestor. To implement this idea, we take advantage of the insight that all non-Africans have a common heritage of Neanderthal gene flow into their ancestors. Thus, we can estimate the date since Neanderthal admixture for present-day and ancient samples simultaneously and use the difference as a direct estimate of the ancient specimen's age. We apply our method to date five Upper Paleolithic Eurasian genomes with radiocarbon dates between 12,000 and 45,000 y ago and show an excellent correlation of the genetic and (14)C dates. By considering the slope of the correlation between the genetic dates, which are in units of generations, and the (14)C dates, which are in units of years, we infer that the mean generation interval in humans over this period has been 26-30 y. Extensions of this methodology that use older shared events may be applicable for dating beyond the radiocarbon frontier.

Instances in which natural selection maintains genetic variation in a population over millions of years are thought to be extremely rare. We conducted a genome-wide scan for long-lived balancing selection by looking for combinations of SNPs shared between humans and chimpanzees. In addition to the major histocompatibility complex, we identified 125 regions in which the same haplotypes are segregating in the two species, all but two of which are noncoding. In six cases, there is evidence for an ancestral polymorphism that persisted to the present in humans and chimpanzees. Regions with shared haplotypes are significantly enriched for membrane glycoproteins, and a similar trend is seen among shared coding polymorphisms. These findings indicate that ancient balancing selection has shaped human variation and point to genes involved in host-pathogen interactions as common targets.

Through an analysis of polymorphism within and divergence between species, we can hope to learn about the distribution of selective effects of mutations in the genome, changes in the fitness landscape that occur over time, and the location of sites involved in key adaptations that distinguish modern-day species. We introduce a novel method for the analysis of variation in selection pressures within and between species, spatially along the genome and temporally between lineages. We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation, selection, and drift. Our approach has the advantage of performing direct inference on coding sequences, inferring ancestral states probabilistically, utilizing allele frequency information, and generalizing to multiple species. We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome. To demonstrate the utility of the method, we infer selective pressures acting in Drosophila melanogaster and D. simulans from polymorphism and divergence data for 100 X-linked coding regions.