john hawks weblog

paleoanthropology, genetics and evolution

mutation

  • Space radiation

    Fri, 2013-01-04 14:44 -- John Hawks

    Maggie Koerth-Baker, on "How space radiation hurts astronauts". I did not know about this part:

    Cucinotta calls this pre-flight calibration. Scientists take a blood sample from an astronaut before the launch. While the astronaut is in space, the scientists divide that blood sample up and expose it to various levels of gamma rays — the kind of damaging radiation we're used to dealing with on Earth. Then, when the astronaut comes back, they compare those gamma ray-affected samples to what has actually happened to the astronaut while in space. "You see about a two-to-three fold difference across the population of astronauts," Cucinotta told me.

    The sample size of astronauts is small enough that I was surprised to see significant effects for one condition: cataracts. The article notes that the Mercury and Gemini astronauts had less spaceflight time than Mir and Skylab cosmonauts and astronauts, which is obvious, but I wonder how they control for the extensive flight time of astronauts who were former test pilots and the consequent history of radiation exposure before going to space.

    Tags: 
    space
    health
    radiation
    mutation

  • Recent evolution of coding variants

    Wed, 2012-12-05 01:00 -- John Hawks

    How did I get myself quoted in a story as the skeptic about recent human evolution? ("Human Evolution Enters an Exciting New Phase"). After all, I've been a huge advocate of the idea that recent human evolution was a lot faster and more interesting than anthropologists used to think ("Why human evolution accelerated").

    The story, by Brandom Keim, is a good account of a new paper in Nature by Wenqing Fu and colleagues, "Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants" [1]. It's a pretty cool study, which has identified protein-coding alleles in large samples of European-American and African-American individuals.

    Fu and colleagues compared all the coding variants they found in large samples of European-Americans and African-Americans, and discovered that the European-ancestry people have a higher fraction of rare coding variants. They propose that the rate of new coding variants entering and persisting within the population actually accelerated in the ancestral European population. Why would this happen? In their view, demography is the most likely explanation. As European populations expanded during the Neolithic and later time periods, the rate by which new mutations are lost by genetic drift began to decline. These new mutations have pooled up within the European population, giving them a glut of new changes to protein-coding sequences. Many of these mutations may be deleterious, just not bad enough for natural selection to have weeded them out in the growing ancient population.

    I think in large part this explanation is correct. In some ways it is incomplete.

    The effect of population history on our evolution was the theme of our 2007 paper on positive selection in recent humans [2]. We relied on exactly the same mathematical relations used in this new paper: More people means more different mutations entering the population. In our case, the increase in the total number of mutations meant that we could expect more potential adaptive mutations to be selected within a growing population. In this case, the increase in the total number of mutations means more mutations remain to be picked up by resequencing rare neutral or deleterious variations in present samples.

    One of the senior authors of the study, Joshua Akey, commented:

    Most of the mutations that we found arose in the last 200 generations or so. There hasn’t been much time for random change or deterministic change through natural selection. We have a repository of all this new variation for humanity to use as a substrate. In a way, we’re more evolvable now than at any time in our history.

    (this is quoted by Punnett Square, not sure about the original source)

    That's a cool concept. These rare protein-coding variations may be mostly unimportant to fitness today, and many are slightly deleterious. Still they provide a store of variability that increases the potential range of responses to future adaptive challenges. Or, they give us room to examine the effects of small differences, which will help us to understand better how genes work. For the past few thousand years, a small proportion of those have come under positive selection, the part that we have been studying in my lab since 2007.

    The current study has some drawbacks. For one, it isn't evident from the results how these new coding mutations are distributed among individuals. Under population growth alone, we should expect that the number of these new coding variants carried by any one individual should be approximately the same as any other individual, regardless of the population size. Where a big population differs from a small population is in the variety of mutations carried by different individuals, with the average number per individual being equal. That may be true in this study, but it isn't possible to tell from the results presented.

    To the extent that some of these mutations are deleterious, their distribution matters. In Europeans, there may be a greater number of deleterious mutations that are on average more rare; all things being equal, this pattern should make it harder to find statistical evidence for association of these rare variants with complex disorders. By contrast, in Africans, the higher average frequencies of such variants should make them easier to tie to phenotypic variation. All this can be concluded from frequencies alone, without a need to relate frequency to age.

    Probably the biggest shortcoming of the paper is in its estimation of ages for these rare mutational variants. Estimating the ages of mutations in human populations has been a real problem for those of us working with genotyping or sequencing data from small samples. When we depend on the linkage between a rare allele and nearby genetic loci, we run into a sampling problem: Estimating the proportion of recombinants in a population fundamentally has a lot of error when you are working with a sample of 10 copies of the rare allele.

    Estimating dates by LD is bad enough, but this paper doesn't even go that far. Instead, it estimates the ages of alleles from their frequency.

    Frequency estimation of age is OK if the genome sequences have come from a Wright-Fisher population (that is, a random-mating, constant size population). More common alleles tend to be older, new alleles tend to be very rare. This isn't a very accurate means of dating any particular mutation, because the relationship of age and frequency under genetic drift has a tremendous variance. But when pooling large sets of alleles into frequency classes, the age-by-frequency approach gives a rough idea of whether mutations have accelerated or stayed at a constant rate over time.

    But there's one obvious thing missing from the model that may have a large effect on the frequencies of rare coding variants: Introgression from Neandertals! If we want to know why Europeans have a large store of rare coding variants relative to Africans, their ancient mixture of a small fraction of a very divergent human population is one obvious reason. None of the Neandertal alleles in Europeans today are new, they are all old. But a method that estimates their ages by allele frequency alone will always conclude that these rare Neandertal alleles are very young.

    In the current paper, the relation of frequency and age is derived from simulations that are based on a model of human population history. Like all recent papers that apply a model of human population history, this one is both overcomplicated (lots of parameters to which we have no good estimates) and oversimplified (too few events to accommodate known historical phenomena). Here's the population model used to derive allele ages in the paper:

    Population model from Fu et al. 2012

    Population model from Figure S5 in the supplementary information from Fu et al. 2012

    The parameters for population divergence times and ancient population sizes are estimated from genetic data, so any systematic error will propagate through to the estimation of allele ages. The exclusion of Neandertal introgression in the model really does bias the allele age estimates badly, as Neandertal genes today are mostly rare, and mostly very old. This year's shift in our assumptions about mutation rates (to a much slower rate than previously assumed) will also affect the estimates of the demographic parameters in the model. An older coalescence time for most genes means a larger ancestral effective size for these populations, and much older allele ages when frequency is the estimator.

    Our lab is working very hard on allele ages, and I hope to be able to share some of that work soon.

    This study is not alone in demonstrating the real importance of rare coding variation in human populations. This line of research has substantial value, as it helps to show why so much of the additive genetic variation underlying variation in human phenotypes has not yet been assigned to genes. We know that many traits are heritable by comparing genetic relatives with each other. Finding the genetic loci that explain similarity among relatives is relatively easy when the genes involved are common, because the same gene variants will be shared across many families. But pooling many families doesn't help us find very rare mutations, as these are likely carried only by a few pedigrees even in a very large sample. By showing the large store of rare coding variation, these studies help to establish that much of the genetic variation underlying disease may be there for us to discover, if we change our discovery approach.

    References

    1. Fu W, O'Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, Gabriel S, Altshuler D, Shendure J, Nickerson DA, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2012.
    2. Hawks J, Wang ET, Cochran G, Harpending HC, Moyzis RK. Recent acceleration of human adaptive evolution. Proceedings of the National Academy of Sciences, U. S. A. [Internet]. 2007;104:20753–20758. Available from: http://dx.doi.org/10.1073/pnas.0707650104
    Tags: 
    recent evolution
    Europe
    Africa
    Holocene
    acceleration
    fitness effects
    mutation
    Synopsis: 
    Probing the pattern of noncoding rare variation in whole exome data.

  • A longer timescale for human evolution

    Fri, 2012-08-10 16:36 -- John Hawks
    Research authors: 
    John Hawks
    Publication information: 

    In press in Proceedings of the National Academy of Sciences, USA

    Work status: 

    This manuscript has just been added to the open research queue. Until this status is updated, readers can assume that the manuscript is incomplete and essential parts are being added by one or more authors. It may be an extremely early draft upload awaiting editing and addition of citations, so reader beware.

    Abstract: 

    none

    Tags: 
    mutation
    mutation rate
    chimpanzees

    During the last few years, the best estimates of the human single nucleotide mutation rate have been cut in half. Until recently, estimates of mutation rate have relied on counting substitutions between primate species and assuming that fossil relatives of living species can accurately pin dates onto phylogenetic branches. This procedure allows very precise estimates, but introduces systematic bias toward higher substitution rates and longer branch lengths because a new lineage can leave a fossil record only after its origin, never beforehand [1]. Now, widespread resequencing, initially of de novo Mendelian genetic disorders [2],[3] and later of whole genomes in parent-offspring trios [4], has allowed direct comparisons of parent and offspring genomes. The most commonly-used but now outdated estimate of the mutation rate was 2.4 times 10^-8 changes per nucleotide per generation [5]. Current estimates of the same value based on resequencing data are much lower, around 1.1-1.28 times 10^-8 [4], [3].

    Langergraber et al. (this issue) [6] seal one of the remaining holes in this emerging understanding, by providing the most accurate estimates of generation length yet possible for wild chimpanzees and gorillas. They determined the parentage of chimpanzees and gorillas in wild study populations, which in concert with field data on births allows an accurate measure of the mean generation length. Chimpanzees average more than 24 years per generation; gorillas more than 19, substantially longer than indicated by earlier life history assessments [7]. Long generations, with few genetic mutations in each, mean that the clock of genetic substitutions has ticked very slowly during the evolution of humans and apes.

    Breathing easier

    Some paleoanthropologists will welcome the new, slower mutation rate. For twenty years, they have been unearthing Late Miocene fossils that purport to represent the lineage leading to recent hominins. Candidates including the 7-million-year-old Sahelanthropus tchadensis, 6 million-year-old Orrorin tugenensis, and 5.5-million-year-old Ardipithecus kadabba vie for a place in our ancestry. Genetic comparisons once pegged the human-chimpanzee common ancestor as recently as 4 million years ago, pruning these fossil limbs out of our family tree [8]. As Langergraber et al. report, a slower rate places the human-chimpanzee common ancestor more than 7 and possibly as early as 13 million years ago, reopening the case for these and other fossils.

    A longer timescale has many other consequences. The 10.5-million-year-old Chororapithecus abyssinicus may really be an early member of the gorilla lineage, as its dental anatomy suggests [9]. For the orangutan lineage, the prospect of a much deeper genetic estimate of divergence illuminates the relation between phylogenetics and population genetics. Genetic divergence between two species is a function not only of the time that the species became isolated, but also of the genetic variation within their ancient common ancestral population. Whole-genome analysis of apes and humans has uncovered abundant evidence of complex population structure in the common ancestors of living species [10]. Hobolth et al. [11] assessed incomplete lineage sorting of orangutan similarity in human and chimpanzee genomes, showing that the ancestral population of the orangutans and African apes must have been large and diverse. A fast mutation rate and this complex ancient structure made the origin of the orangutan branch uncomfortably recent, only 9-13 million years ago, barely old enough to accommodate the earliest known orangutan-like fossil evidence, the 12.5-million-year-old Sivapithecus indicus. A slower mutation rate appears to be a better fit to both fossil evidence and the genetic structure of this ancient population.

    Beyond branches

    The genomes of the African apes and humans have opened a new way of studying population history. In addition to the cladistic relations among species plodding along phyletic branches, we can now test hypotheses about the diversity and structure of dynamic populations. We depend on accurate estimates of mutation and recombination to examine introgression, partial population replacement, continuing gene flow and changes in population size. A slower mutation rate demands that we revisit the population histories of humans and our close relatives. The histories of the present subspecies of chimpanzees may go back to nearly a million years ago. As Langergraber et al. show, the genetic differences between western and eastern gorillas may be 1.5 million years or older. A longer timescale shows that the present subspecies of primates have survived multiple episodes of climate change in tropical Africa, events should also have shaped human evolution in complementary ways. More interesting, the depth of gene genealogies in these primates may reflect ancient episodes of partial population replacement and introgression.

    Along these lines, genomes from Neandertals and from Denisova Cave [12],[13] demonstrate the complexity of human population history. Ten years ago, many scientists argued that the population history of living humans converged to a recent strong bottleneck in a single African population. Today we work to refine a richer and more complex model with multiple episodes of dispersal, genetic differentiation and introgression. So much is left for us to discover as we are far from achieving the full potential of billions of base pairs of new data.

    A mere two years ago, genomic evidence from Neandertals suggested that they had originated within the last 270,000-440,000 years [12]. This troublesome date excludes specimens that have appeared to be strong candidates for Neandertal ancestors, including the large sample of skeletal remains from Sima de los Huesos, Atapuerca, Spain, possibly more than 530,000 years old. Now the maximum value for Neandertal-human common ancestry from 2010 seems instead closer to a minimum date. Langergraber et al. suggest a range from 420,000-780,000 years, bringing much of the Middle Pleistocene record of Europe into the scope of Neandertal ancestry.

    Moving out

    Across this same timescale, the archaic ancestors of today's Africans had already developed an intricate population structure. Genomic investigation of African hunter-gatherers has opened new windows onto this deep genetic history of differentiation and introgression [14], [15], bringing the origin of modern African diversity into the population structure of the early Middle Pleistocene. A simple hypothesis of modern human origins in a bottlenecked population cannot account for this diverse genetic history.

    The mitochondrial DNA timescale now poses a hanging question. Mitochondrial mutations occur much more often than nuclear DNA mutations, with greater heterogeneity among sites [16]. Still, our estimate of mtDNA substitution rates depends on our estimates of branch lengths of the primate phylogeny. Up to now, mitochondrial comparisons have been the strongest evidence in favor of a short timescale for the dispersal and differentiation of non-African peoples, within the last 70,000 years [17]. Some recent attempts to examine the relationships of non-African populations using nuclear genome data have led to timescales in excess of 100,000 years [18], others favor more recent estimates [19]. Despite the recency of this work, most authors have continued to use outdated fast molecular clock and short generation time estimates. As we move forward, such results will need to be corrected or adjusted to enable comparisons with current work.

    A common language

    It may seem surprising that such a basic parameter as the mutation rate could have been inaccurately estimated for so long. An accurate per-genome estimate of mutation rate depends on large amounts of sequence data, observed for a large number of parent-offspring pairs. Whole genome sequencing has become very widespread during the last two years, but low-coverage genomes have a high rate of false positive changes, which have delayed acceptance of the lower rate estimates. Stronger evidence about mutation rate comes from the even broader sample of parent-offspring trios from surveillance of de novo Mendelian diseases [3]. These values will be subject to continuing refinement, as geneticists add more and more primate and human genomes and closer examination of their biology.

    Sampling DNA from other primates effectively collates thousands of generations of time into a single comparison, allowing the substitution rate to be estimated from relatively short DNA sequences. For mutations not under selection, the substitution rate estimates the mutation rate very precisely.

    But precision is not accuracy. Radiometric ages are often very precise, and paleontologists can constrain the provenience of some Miocene primate fossils to ranges less than a hundred thousand years. Accuracy about the time of speciation would require evidence the fossil record can never provide. We cannot say how many orangutan ancestors may have lived before the 12.5-million-year-old Sivapithecus indicus; we can only hope to discover more of them. Fossils have limited value even as minimum estimators of speciation time. Steiper and Young [20] estimated a relatively slow rate of mutations in primates, by assuming that a series of fossils represent minimum ages for various phylogenetic branches of primates. Their slow rate estimate depended upon placing the 7-million-year-old Sahelanthropus tchadensis as a member of the hominin lineage, an assumption that has been challenged on morphological grounds [21]. This challenge could not necessitate a higher mutation rate, but could delay acceptance of a slower rate. A slow mutation rate does not settle the phylogenetic position of Sahelanthropus or other fossil specimens, it merely refocuses study upon anatomical and ecological evidence.

    Mutation rates estimated from pedigree and phylogenetic data may still prove to be significantly different, as they are for mitochondrial DNA [16]. The nuclear mutation rate varies among sites and regions (e.g., CpG nucleotides) [22], and discovery of functional elements will bring to light some amount of previously unrecognized purifying selection. The average mutation rate across the genome is only a starting point. Still, as genomes have begun to reveal the kind of complexity long evidenced by the fossil record, we can begin to seek a new anthropological synthesis that ties together genomes, morphology, and life history data.

    References

    1. Steiper ME, Young NM. Timing primate evolution: Lessons from the discordance between molecular and paleontological estimates. Evol. Anthropol. [Internet]. 2008;17:179–188. Available from: http://dx.doi.org/10.1002/evan.20177
    2. Kondrashov AS. Direct estimates of human per nucleotide mutation rates at 20 loci causing mendelian diseases. Hum. Mutat. [Internet]. 2003;21:12–27. Available from: http://dx.doi.org/10.1002/humu.10147
    3. Lynch M. Rate, molecular spectrum, and consequences of human mutation. Proceedings of the National Academy of Sciences [Internet]. 2010;107:961–968. Available from: http://dx.doi.org/10.1073/pnas.0912629107
    4. Roach JC, Glusman G, Smit AFA, Huff CD, Hubley R, Shannon PT, Rowen L, Pant KP, Goodman N, Bamshad M, et al. Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing. Science [Internet]. 2010;328:636–639. Available from: http://dx.doi.org/10.1126/science.1186802
    5. Nachman MW, Crowell SL. Estimate of the Mutation Rate per Nucleotide in Humans. Genetics [Internet]. 2000;156:297–304. Available from: http://www.genetics.org/cgi/content/abstract/156/1/297
    6. Langergraber KE, Prüfer K, Rowney C, Boesch C, Crockford C, Fawcett K, Inoue E, Inoue-Muruyama M, Mitani JC, Muller MN, et al. Generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and human evolution. Proceedings of the National Academy of Sciences of the United States of America. 2012.
    7. Teleki G, Hunt EE, Pfiffering JH. Demographic observations (1963–1973) on the chimpanzees of {Gombe} {National} {Park}, {Tanzania}. Journal of Human Evolution. 1976;5:559–598.
    8. Wildman DE, Uddin M, Liu G, Grossman LI, Goodman M. Implications of Natural Selection in Shaping 99.4% Nonsynonymous DNA Identity Between Humans and Chimpanzees: Enlarging Genus Homo. Proceedings of the National Academy of Sciences, U. S. A. [Internet]. 2003;100:7181–7188. Available from: http://dx.doi.org/10.1073/pnas.1232172100
    9. Suwa G, Kono RT, Katoh S, Asfaw B, Beyene Y. A New Species of Great Ape from the Late Miocene Epoch in Ethiopia. Nature [Internet]. 2007;448:921–924. Available from: http://dx.doi.org/10.1038/nature06113
    10. Siepel A. Phylogenomics of primates and their ancestral populations. Genome research. 2009;19(11):1929-41.
    11. Hobolth A, Dutheil JY, Hawks J, Schierup MH, Mailund T. Incomplete lineage sorting patterns among human, chimpanzee, and orangutan suggest recent orangutan speciation and widespread selection. Genome research. 2011;21(3):349-56.
    12. Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz MH, et al. A Draft Sequence of the Neandertal Genome. Science [Internet]. 2010;328:710–722. Available from: http://dx.doi.org/10.1126/science.1188021
    13. Reich D, Green RE, Kircher M, Krause J, Patterson N, Durand EY, Viola B, Briggs AW, Stenzel U, Johnson PLF, et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature [Internet]. 2010;468:1053–1060. Available from: http://dx.doi.org/10.1038/nature09710
    14. Lachance J, Vernot B, Elbers  C, Ferwerda B, Froment A, Bodo J-M, Lema G, Fu W, Nyambo  B, Rebbeck  R, et al. Evolutionary History and Adaptation from High-Coverage Whole-Genome Sequences of Diverse African Hunter-Gatherers. Cell. 2012.
    15. Hammer MF, Woerner AE, Mendez FL, Watkins JC, Wall JD. Genetic evidence for archaic admixture in Africa. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(37):15123-15128.
    16. Soares P, Ermini L, Thomson N, Mormina M, Rito T, Röhl A, Salas A, Oppenheimer S, Macaulay V, Richards MB. Correcting for purifying selection: an improved human mitochondrial molecular clock. American journal of human genetics [Internet]. 2009;84:740–759. Available from: http://dx.doi.org/10.1016/j.ajhg.2009.05.001
    17. Endicott P, Ho SYW, Metspalu M, Stringer C. Evaluating the mitochondrial timescale of human evolution. Trends in Ecology & Evolution [Internet]. 2009;24:515–521. Available from: http://dx.doi.org/10.1016/j.tree.2009.04.006
    18. Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data. PLoS Genet [Internet]. 2009;5:e1000695+. Available from: http://dx.doi.org/10.1371/journal.pgen.1000695
    19. Lukic S, Hey J. Demographic Inference Using Spectral Methods on SNP Data, With an Analysis of the Human out-of-Africa Expansion. Genetics. 2012.
    20. Steiper ME, Young NM. Primate molecular divergence dates. Molecular Phylogenetics and Evolution [Internet]. 2006;41:384–394. Available from: http://dx.doi.org/10.1016/j.ympev.2006.05.021
    21. Wolpoff MH, Hawks J, Senut B, Pickford M, Ahern J. An Ape or the Ape: Is the Toumaï Cranium TM 266 a Hominid?. PaleoAnthropology. 2006;2006:36–50.
    22. Subramanian S, Kumar S. Neutral Substitutions Occur at a Faster Rate in Exons Than in Noncoding DNA in Primate Genomes. Genome Research [Internet]. 2003;13:838–844. Available from: http://dx.doi.org/10.1101/gr.1152803

  • Recombination in chimpanzees

    Mon, 2012-04-23 22:35 -- John Hawks

    Adam Auton and colleagues [1] sequenced a panel of chimpanzees to examine recombination in that species, thereby constructing a chimp-specific genetic map. This paper doesn't give any new information about chimpanzee population history or structure, but does have some conclusions about the evolution of recombination in the human and chimpanzee lineages:

    Our results also reveal the different processes that operate at fine and broad scales. At broad scales, we find substantial correlation in recombination rate between the species, which is disrupted by chromosomal rearrangement. However, even among conserved regions, less than 40% of the variance in chimpanzee recombination rate at 1 Mb can be explained by the human rate. Determining the factors that shape stasis and change in broad-scale recombination rates presents a key challenge in the study of recombination. A population sequencing approach, such as the one taken here, should enable further informative studies of recombination across a wide range of species.

    They also find that local substitution biases and polymorphism biases emerge in concert with recombination rates. Sites near structural polymorphisms in humans, such as chromosomal inversions and the fusion of human chromosome 2, buck the genome-wide trend of correlation between human and chimpanzee recombination rates.

    This process is important to understand if we want to use today's genetics to examine the ancestral population of humans and chimpanzees. It will be interesting to consider how the changes in recombination may affect genetic models of speciation.

    References

    1. Auton A, Fledel-Alon A, Pfeifer S, Venn O, Ségurel L, Street T, Leffler EM, Bowden R, Aneas I, Broxholme J, et al. A fine-scale chimpanzee genetic map from population sequencing. Science (New York, N.Y.). 2012;336(6078):193-8.
    Tags: 
    chimpanzees
    recombination
    linkage
    mutation

  • When genes break: validating loss-of-function variants

    Fri, 2012-02-17 12:20 -- John Hawks

    Daniel MacArthur and colleagues have an important paper in Science, titled "A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes" [1]. They took 1000 Genomes Project pilot data and systematically looked at every allelic variant in the sample that appeared to cause the loss of function of a protein-coding gene. Mutations that de-activate genes in this way are not rare, but they are often eliminated from the population rapidly by purifying natural selection, because the normal function of a protein is necessary to survival or reproduction. However, not every protein is so important, and MacArthur and colleagues confirmed that more than 1200 alleles in this sample genuinely occur in one or more of the 1000 Genomes Project individuals.

    Some of these are common but most occur in fewer than 2% of individuals in the sample, as expected if purifying selection were affecting many of them.

    MacArthur is one of the authors of the Genomes Unzipped group blog, and has written a great summary and introduction to his research paper: "All genomes are dysfunctional: broken genes in healthy individuals". It's free and well-written, so it will probably work better for many readers than the original paper.

    Science is running a commentary to accompany the research article, by Lluis Quintana-Murci [2]. This paragraph encompasses a lot of the numerical facts about these loss-of-function variations, and discusses the idea that some of them were positively selected -- that is advantageous in recent human populations.

    MacArthur et al. estimated that, depending on ethnic background, each individual's genome carries 26 to 37 variants that introduce a stop codon (which signals the termination of translation of nucleic acids into protein), with up to 6 present in the homozygous state. When considering other types of LoF variants, including those that disrupt splice-sites, large deletions, or insertions or deletions of nucleotides that change the DNA reading frame, the total number per individual is extended to 103 to 121, with ∼20 present in homozygosity. A large proportion of LoF variants were enriched in low-frequency alleles, suggesting that the removal of deleterious alleles has prevented them from increasing to high frequencies. Furthermore, some have already been associated with severe human diseases, supporting the less-is-less hypothesis. Other LoF variants, which can reach higher population frequencies, fall into poorly evolutionarily conserved genes or belong to multigene families displaying high paralogous sequence identity. This suggests that the functions of the corresponding genes are highly redundant, explaining their greater tolerance for LoF variants and supporting a less-is-nothing scenario. Also, although no substantial enrichment in positive selection signals was observed among LoF variants at the genomewide level, 20 of them fell into regions displaying signatures of positive selection, as predicted by the less-is-more hypothesis, suggesting that they may have conferred a selective advantage in human evolution.

    Common loss-of-function variants that are evolutionarily recent are very interesting to us as we work to understand the changes that accompanied modern human origins and the later invention and spread of agriculture. I am really excited that these analyses were carried out using the 1000 Genomes samples because that means we can use the sequence data to estimate the ages of these functional losses. We can do quite a lot better than to say that they "fall into regions displaying signatures of positive selection": In fact, we can determine whether these variants themselves were selected, or hitchhiked to high frequency along with some other variant that was selected.

    Many of loss-of-function variants are in genes that may not matter much to selection. Olfactory receptor genes, for example, comprise a very large family with recurrent duplications and pseudogenizations during primate evolution. We have scores of olfactory receptor pseudogenes, many of which are polymorphic in living human populations. Some may continue to make a noticeable difference to the phenotype, such as the asparagus-urine-smelling polymorphism. But many are probably invisible to us. Still, a few of these do look like they've been positively selected in recent human populations.

    Sometimes less really is more.

    References

    1. MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, Jostins L, Habegger L, Pickrell JK, Montgomery SB, et al. A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes. Science. 2012;335(6070):823 - 828.
    2. Quintana-Murci L. Gene Losses in the Human Genome. Science. 2012;335(6070):806 - 807.
    Tags: 
    pseudogenes
    mutation
    1000 Genomes Project
    fitness effects
    positive selection
    recent selection
    Synopsis: 
    A "punishing" resequencing project validates mutations in the 1000 Genomes Project individuals that deactivate protein-coding genes.

  • What is the human mutation rate?

    Thu, 2010-11-04 01:33 -- John Hawks

    Last spring I wrote about a study that used whole-genome comparisons between parents and offspring to estimate the rate of per-genome mutation in humans ("A low human mutation rate may throw everything out of whack").

    The study was by Jared Roach and colleagues [1], and as you might guess from my post title, the result was surprising. Previous work had suggested a human mutation rate around 2.5 x 10-8 per site per generation. The new study found less than half the expected number of mutations between these parents and offspring, an estimated rate of only 1.1 x 10-8 per site.

    If this lower rate of mutation were to hold up, it would affect much of our understanding of the chronology of human evolution. Fossils and archaeological sites would not change in date, but some hypotheses about their relationships would be challenged. For example, the higher rate of 2.5 x 10-8 per site suggests a chimpanzee-human population divergence around 4 million years ago. A new rate of 1.1 x 10-8 would not have a linear effect on this divergence time -- the genes don't have genealogical roots at the same instant as the population divergence. But the human-chimpanzee divergence time would be radically higher than in many recent estimates.

    The same might be true for other primate divergences, and for genealogical relations within human populations today. Basically any times that are estimated from genetic differences may be affected by our knowledge of the per-generation rate of mutations.

    What does this mean? Open below the fold to read more.

    What mutations are we counting?

    Human genomes differ from each other in many ways. There are single base-pair changes in sequences, insertions and deletions, repeat polymorphisms, and larger-scale rearrangements such as inversions and gene duplications. Recent work suggests that some of these larger-scale effects may be very important to phenotypic variation among people. So why should we be talking about only the first of these kinds of variation?

    Single nucleotide mutations have been the focus of most attention about mutation rates because they are relatively easy and quantify. In high-quality sequence data, a single nucleotide change is relatively unambiguous. Reversals are fairly unlikely, although at a small fraction of "hotspot" sites, recurrent mutations can make a big difference.

    It is somewhat misleading to refer to "a" rate of single nucleotide mutations, because some kinds of sites (e.g., CpG nucleotides) have had a much higher probability of mutations than others. This affects the apparent rate of mutations in noncoding versus synonymous sites [2]. Also, the germline in males has been estimated to be as much as 6 times more likely to suffer mutations than the germline in females (discussed by Crow [3]). The idea of a genome-wide rate assumes that when we bin all the single nucleotide mutations together, across large amounts of sequence, we do arrive at a relatively stable rate that can be applied to similarly broad extents of sequence data. Or at least that we can identify sequence regions with compatible rates (e.g., noncoding DNA or synonymous sites).

    At the moment, technical issues make it hard to find and quantify many other kinds of variation. The current generation of sequencing devices tend to generate short reads, which make it difficult to assess the presence of insertions or deletions of more than a few base pairs. Duplications and other rearrangements require special treatment such as higher coverage or longer sequence reads. By contrast, a single nucleotide mutation will typically align in the proper location and be quite evident in a read. In principle, we can just run down the genome and count them.

    Still, finding novel mutations is not without its problems. Recent sequencing projects have yielded a very high rate of false positives. The rate of false negatives is really not yet known. We have a good reason to suspect that the false negative rate will be high. In a low-coverage genome, many short segments of the genome will have very low read numbers, making it likely that the sequence reads represent only one of the two copies of the genome present at that location. Any novel mutations in that area have a 50-50 chance of being missed by our sequencing efforts. This false negative risk can be reduced by adding higher sequence coverage, but we're not yet at the point where we have a lot of genomes sequenced at the 10x or higher coverage that we would really want.

    So while sequencing a parent and offspring genome is the most direct way to estimate the per-generation mutation rate, it is not yet ideal.

    Where did the high rate come from?

    That means we need to look very closely at other sources of data, to see if they may provide some independent confirmation of a lower per-generation mutation rate. In the process, we should ask, why did the higher rate, around 2.5 x 10-8 per generation, become so widely accepted?

    The source cited by Roach and colleagues for the higher rate, 2.5 x 10-8 per site, is a paper by Michael Nachman and Susan Crowell [4]. Nachman and Crowell examined processed pseudogenes in humans and chimpanzees, under the assumption that mutations in these pseudogenes would be neutral to selection in the human and chimpanzee lineages.

    The average mutation rate was calculated from the average autosomal rate of evolution assuming a generation time of 20 years (Table 3). Recent estimates of the time since humans and chimpanzees diverged (T) include 4.5 mya (TAKAHATA and SATTA 1997 ), 5.5 mya (KUMAR and HEDGES 1998 ), and 6.0 mya (GOODMAN et al. 1998 ). ARNASON et al. 1998 estimated the Homo-Pan divergence at 10–13 mya; however, their estimate is based on a calibration using distant, nonprimate species and is at odds with most other recent estimates. Mutation rates were calculated for a range of different human-chimpanzee divergence times and for two different ancestral population sizes. Mutation rate estimates vary from 1.3 x 10-8 (assuming T = 6 mya and Ne = 105) to 2.7 x 10-8 (assuming T = 4.5 mya and Ne = 104). If the average generation time is assumed to be 25 years (e.g., EYRE-WALKER and KEIGHTLEY 1999 ), then mutation rates are estimated to be between 1.6 x 10-8 and 3.4 x 10-8.

    Wait a minute. There's no independent estimate of mutation rate here at all!

    What they did was to assume values for the human-chimpanzee divergence and ancestral (chuman) effective size, and then provide an estimate of mutation rate consistent with those assumptions. That's perfectly reasonable as a way of quantifying the genetic divergence that they observed. If our goal is to predict the per-generation mutation rate from interspecific divergence, that's more or less the kind of estimate that we want.

    But many, many other studies have instead used a citation to the Nachman and Crowell rate as a justification for their own estimates of the human-chimpanzee divergence time! That's not perfectly reasonable, in fact, it's perfectly circular. It's turtles all the way down!

    Worse, those citations tend to cite the midpoint of Nachman and Crowell's range of estimates (2.5 x 10-8) as if it were a true value measured with little error. Reading the original reference, you can plainly see that Nachman and Crowell reported estimates that varied over a factor of three, corresponding to a wide range of chuman population histories. From their discussion:

    Mutation rates estimated for a range of divergence times and ancestral population sizes fall between 1.3 x 10-8 and 2.7 x 10-8 assuming a generation time of 20 years (Table 3) or between 1.6 x 10-8 and 3.4 x 10-8 assuming a generation time of 25 years. We suggest that 2.5 x 10-8 is a reasonable estimate of the average mutation rate per nucleotide site (but caution that the actual rate may be between 1.3 x 10-8 and 3.4 x 10-8).

    That 2.5 x 10-8 is simply the midpoint of their range of estimates with the 25-year generation time.

    What would be more reasonable? For hominins and chimpanzees, we probably want to apply a shorter generation length, a larger ancestral effective size, and a higher time of divergence. All of these would have yielded a lower rate for the Nachman and Crowell data. But we don't want to just assume these values, we should try to test whether they are valid based on other data.

    Other mutation rates from phylogenetic comparisions

    Nachman and Crowell have not been alone in their ultimate reliance on fossil evidence as an assumption underlying the per-generation mutation rate. But several other studies came to a slower mutation rate. Mostly, these studies have assumed that the human-chimpanzee divergence happened significantly earlier than 5 million years ago. Necessarily, then, the human per-generation mutation rate would have to be lower, as long as the sequence divergence remained the same.

    These estimates are ultimately rooted in the date of one or more fossils, among which the generation time certainly varied. The resulting per-site mutation rates are often reported as per-year instead of per-generation. For example, Yi and colleagues [5] yielded a rate of 0.99 x 10-9 per year for the human-chimpanzee comparison, which would multiply to 1.98 x 10-8 per 20-year generation. They propose this as a maximal rate, assuming that Sahelanthropus at a minimum date of 6 million years ago is a hominin. With an older divergence date, they propose a correspondingly lower rate (e.g., 0.79 x 10-9 per year, not accounting for ancestral population polymorphism).

    Similarly, Steiper and Young [6] considered a long (1.9 Mb) alignment of sequence from 19 primate species. In their model to estimate relative rates on different branches of the primate phylogeny, they incorporated the assumption that Sahelanthropus is on the hominin clade. A divergence date of 6 million years gave rise to a human per-site mutation rate of 0.65 x 10-9 per year (1.3 x 10-8 per 20-year generation). A divergence date of 7 million years lowered the mutation rate to 0.57 x 10-9 per year.

    Low mutation rates do not always result from these studies. Several have arrived at either a high human mutation rate or a recent human-chimpanzee divergence time. Sometimes a recent human-chimpanzee divergence emerges simply by assuming the rate given by Nachman and Crowell. Yang [7] provides an example of this -- a paper that very thoroughly explores the relationship of divergence time and ancestral effective population size, but ultimately roots the estimates on a single value for mutation rate. This rate we have already seen was itself based on an assumption about divergence time.

    Kumar and colleagues [8] came to a much lower estimate for the human-chimpanzee divergence time, based on an Old World monkey-hominoid divergence at 23.8 million years ago. This estimate did not consider the effect of ancestral polymorphism on the mean genetic divergence time, and so should -- in the language of computer software -- be deprecated.

    I should reiterate that none of these estimates are suitable for testing the times of phylogenetic divergences, because they all assume that the date of some particular fossil (or set of fossils, by fitting a model) is the minimum divergence time for a clade.

    So much of the literature in this area is ultimately circular, I'm pulling out my sparse hair reading through it. By the time we get back to the mid-1990's, the sequence data are even sparser than my hair by today's standards -- only a few hundred base pairs, or a sampling of restriction sites. But the divergence time estimates have propagated forward from that time to today, recycled through the assumptions of papers in the intervening time. It's like the genetic equivalent of money laundering!

    Evidence from parent-offspring sequence differences

    There is another way besides phylogenetic comparison: Simply look at living people and see how many new mutations they have.

    But this is tricky because we are rarely in a position to know which mutations are new. Most variations that we see between two people have persisted in the population for hundreds of generations or more. It takes a special kind of mutation to make its newness evident.

    Up until the advent of large-scale sequencing, the most important source of information about the mutation rate came from the rates of spontaneous Mendelian diseases. When a person has a dominant genetic disorder not carried by either of his parents, you know that the mutation must be new. Disease rates have long been tracked as standard public health data.

    However, the per-genome or per-locus rate of Mendelian disorders can estimate the per-site rate of mutations only by adding well-resolved information about the target size of functional genes. For example, if we know the average gene length and the proportion of different amino acids in functional proteins we can make some estimate of the ratio of synonymous to nonsynonymous sites. But we would still lack information about the fraction of nonsynonymous mutations that cause deleterious effects on protein function. For this reason, it was possible for very early workers (e.g., Haldane) to come within the ballpark of per-locus mutation rates even before the genetic code was available. Yet such estimates are not strictly useful for understanding per-site rates of mutation.

    By 2000, widespread sequencing had begun to identify disease-causing mutations at the sequence level. When exons are known, it is possible to determine the "target size" -- the number of sites at which loss-of-function mutations may occur. These two values provide the numerator and denominator for an estimate of the per-site mutation rate.

    Kondrashov [9] applied this method to estimate the per-site mutation rate across 20 human genes. He surveyed the literature for genes where more than 100 patients had been sequenced completely for the causative locus, finding the causal mutations. Using this value and the disease incidence allowed an estimate of the per-site rate of mutation for different categories of transitions and transversions. There was some variation among loci, with an average rate of per-site mutation equal to 1.8 x 10-8 per generation.

    Kondrashov observed a few hotspots in these genes, with substitution or deletion rates as much as a hundred times the average site. He also observed that the per-gene rate of mutation varies according to the number of CpG sites. The rate of short deletions was on the order of 5 x 10-10, insertions were even less frequent.

    The rate estimate by Kondrashov is within the range considered by Nachman and Crowell, but only 3/4 of the value 2.4 x 10-8 widely cited as the long-term estimate. If this rate were applied to Nachman and Crowell's pseudogene data, it would predict a human-chimpanzee divergence time around 6 million years.

    This year, Lynch [10] performed a more extensive comparison using similar methods as Kondrashov. Including more genes, and considering a broader range of mutational effects (including missense as well as nonsense coding mutations), Lynch found an even lower estimate of mutation rate per generation -- only 1.28 x 10-8 per site.

    These estimates are not precisely the same as comparing parent-offspring pairs, but they are exceedingly powerful because the data on disease rates encompass very large populations of people.

    We should keep in mind the result of Subramanian and Kumar [2], which showed that exons have a higher effective rate of substitution than do noncoding regions. That result implies that the genome-wide rate of change should be lower than estimated by Lynch, because his estimate encompasses only coding mutations. Also, any effect of purifying selection on these mutations will tend to decrease the long-term rate of substitutions per site to a lower value than the rate of mutations. The rate estimated by Lynch should then be an overestimate of the substitution rate that would be applicable to hominoid phylogenetic relationships.

    A slower rate

    These estimates of the per-generation mutation rate are all low compared to the commonly-cited 2.5 x 10-8. They are not quite as low as the rate estimated by Roach and colleagues [1], but the Lynch estimate is very close: 1.28 x 10-8 compared to 1.1 x 10-8 per site.

    The lower estimate from Roach and colleagues is a direct comparison of parent and offspring. In my earlier discussion of that rate, I suggested that false negatives in the sequence comparisons might have lowered the apparent rate of mutations. I still think we can't rule out that possibility. But the rate is not alone, and so it is less surprising than it may have seemed.

    My post last week on the 1000 Genomes Project results ("Now for anthropological genomics") mentioned that the 1000 Genomes comparisions have arrived at essentially the same rate as Roach and colleagues. Comparison of one family trio led to a rate of 1.0 x 10-8 per site per generation; the other family trio gave rise to an estimate of 1.2 x 10-8 per site per generation. These bracket the estimate given by Roach and colleagues.

    My basic observation about the human-chimpanzee divergence time is still sound:

    If this mutation rate is accurate, then the average human-chimpanzee gene divergence has to be up around 11 million years ago. That can be accommodated with a 7-million-year-old species divergence only if we assume a very large ancestral population -- on the order of 50,000 or higher. Or, the ancestral effective size could be lower -- but that would make the species divergence substantially older -- 9 million years or more.

    As we go further back in time, this lower human mutation rate may be less and less relevant, because different primate lineages may have higher (or lower) rates. When some of the kinks have been worked out of whole-genome sequencing, it would be tremendously useful to sequence parent-offspring pairs in other primate species. With those data, rate heterogeneity could be tested directly.

    For events within the hominins, the parent-offspring rate of mutations ought to be better than a rate estimated from phylogenetic distance. Phylogenetic distances are estimated with even more error than mutations, increasingly so as our methods for comparing genomes improve. But some fraction of new mutations will ultimately be lost to purifying selection. That implies, again, that the longer term rate of substitutions will be lower than the rate of mutations measured from parent-offspring comparisons.

    A rate of 1.1 x 10-8 would have no effect on the number of genetic differences observed between people, because these differences are just counted, not estimated by genealogical relationships that are known. It is the unknown genealogical relationships, which are estimated from genetic differences, that may change substantially.

    Let's consider an example. Harris and Hey [11] sequenced 4200 bp of the gene PDHA1, an X-linked gene whose product is part of a mitochondrial enzyme complex. At the time of their study (1999), their result was one of the oldest coalescence times estimated for non-African populations based on sequence data; they estimated the root of the PDHA1 genealogy was 1.8 million years old. This estimate was based on the assumption that human and chimpanzee copies, which differed by an average of 40.42 substitutions, had diverged at 5 million years ago. That would imply that the average genetic difference between humans across the deepest root of the genealogy, 15.05 mutational differences, corresponds to 1.86 million years of time. If we instead assert a per-generation rate of 1.1 x 10-8 per site, these data would generate an estimate of 163,000 generations for the root of the genealogy, roughly 3.3 million years.

    In other words, a coalescence that appeared to have happened in early Homo now looks rooted at the age of A. afarensis. The chimpanzee-human genetic root would be around 8.7 million years for these data.

    These estimates would likely be biased too low, because the X chromosome has a lower rate of mutation than the autosomes by some extent. That issue was addressed by Lynch [10], due to the fact that X chromosomes are in males (with their higher rate of mutations) only 1/3 of the time compared to 1/2 the time for autosomes. Any purifying selection would also bias the estimate too low. If these 4200 bp have a higher-than-average CpG content, that is one factor that might require a higher per-generation rate.

    Is any of this a problem? I don't think we know yet. A lower rate must readjust the apparent correspondence of some molecular time estimates with the archaeological record. But to be honest, most of the apparent correspondences of such dates have been illusory, because genealogical relationships among genes have such large expected variance under any realistic human population model. It is really the availability of whole-genome comparisons that has a chance of improving these population models.

    References

    1. Roach JC, Glusman G, Smit AFA, Huff CD, Hubley R, Shannon PT, Rowen L, Pant KP, Goodman N, Bamshad M, et al. Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing. Science [Internet]. 2010;328:636–639. Available from: http://dx.doi.org/10.1126/science.1186802
    2. Subramanian S, Kumar S. Neutral Substitutions Occur at a Faster Rate in Exons Than in Noncoding DNA in Primate Genomes. Genome Research [Internet]. 2003;13:838–844. Available from: http://dx.doi.org/10.1101/gr.1152803
    3. Crow JF. The origins, patterns and implications of human spontaneous mutation. Nature Reviews Genetics [Internet]. 2000;1:40–47. Available from: http://dx.doi.org/10.1038/35049558
    4. Nachman MW, Crowell SL. Estimate of the Mutation Rate per Nucleotide in Humans. Genetics [Internet]. 2000;156:297–304. Available from: http://www.genetics.org/cgi/content/abstract/156/1/297
    5. Yi S, Ellsworth DL, wen-Hsiung Li. Slow Molecular Clocks in {Old World} Monkeys, Apes, and Humans. Molecular Biology and Evolution. 2002;19:2191–2198.
    6. Steiper ME, Young NM. Primate molecular divergence dates. Molecular Phylogenetics and Evolution [Internet]. 2006;41:384–394. Available from: http://dx.doi.org/10.1016/j.ympev.2006.05.021
    7. Yang Z. Likelihood and Bayes Estimation of Ancestral Population Sizes in Hominoids Using Data From Multiple Loci. Genetics [Internet]. 2002;162:1811–1823. Available from: http://www.genetics.org/cgi/content/abstract/162/4/1811
    8. Kumar S, Filipski A, Swarna V, Walker A, Hedges BS. Placing Confidence Limits on the Molecular Age of the Human-Chimpanzee Divergence. Proceedings of the National Academy of Sciences, U. S. A. [Internet]. 2005;102:18842–18847. Available from: http://dx.doi.org/10.1073/pnas.0509585102
    9. Kondrashov AS. Direct estimates of human per nucleotide mutation rates at 20 loci causing mendelian diseases. Hum. Mutat. [Internet]. 2003;21:12–27. Available from: http://dx.doi.org/10.1002/humu.10147
    10. Lynch M. Rate, molecular spectrum, and consequences of human mutation. Proceedings of the National Academy of Sciences [Internet]. 2010;107:961–968. Available from: http://dx.doi.org/10.1073/pnas.0912629107
    11. Harris EE, Hey J. X chromosome evidence for ancient human histories. Proceedings of the National Academy of Sciences, U. S. A. 1999;96:3320–3324.
    Tags: 
    mutation rate
    mutation
    1000 Genomes Project
    chimpanzees
    genetic divergence
    population genetics
    chumans
    Synopsis: 
    The 1000 Genomes Project is finding that the mutation rate is half the value usually assumed.

  • A low human mutation rate may throw everything out of whack

    Thu, 2010-03-18 16:30 -- John Hawks

    Last week, a paper looking for the genetic causes of Miller syndrome reported the whole genomes of four members of a single family: two siblings with the disorder and their two parents without. The idea was that they would simply compare the affected and unaffected genomes. They would then find candidate loci that might account for Miller syndrome in the affected siblings. By exploiting some other sources of information, they found what they were looking for. Daniel MacArthur covered the story in his post, "Disease hunting with whole genome sequences: the good news, and the bad news".

    I got interested in another aspect of the story. With whole-genome sequences of parents and offspring, it becomes possible to directly determine the rate of mutations in each generation. The paper by Roach and colleagues did just that -- they counted 28 in the 2.3 billion bases of sequence they included in their comparison. That makes a per-site mutation rate of 1.1 x 10-8 per generation.

    Which is a pretty interesting number. You see, it's less than half what it ought to be:

    [O]ur estimated human mutation rate is lower than previous estimates, the most widely cited of which is 2.5 x 10-8 per generation (10) based on three parameters: a human-chimpanzee nucleotide divergence per site (Kt) of 0.013, a species divergence time of five million years ago, and an ancestral effective population size of 10,000. More recent estimates indicate a nucleotide divergence of 0.012 (9), species divergence time between six and seven million years ago (11–15), and ancestral effective population size between 40,000 and 148,000 (16–19). With these parameter ranges and a generation length of 15 to 25 years, the mutation rate estimate is between 7.6 x 10-9 and 2.2 x 10-8 per generation, which is consistent with our intergenerational estimate of 1.1 x 10-8. Our estimate is within one standard deviation (SD) of an earlier estimate of 1.7 x 10-8 (SD: 9 x 10-9) based on 20 disease-causing loci (20). The rate we report is for autosomes, and should be several-fold lower than that of the Y chromosome, as in the male germline more cell divisions occur per generation. Though our rate differs approximately as expected from the recently reported estimate of 3.0 x 10-8 (95% CI: 8.9 x 10-9 – 7.0 x 10-8) for the Y chromosome, the error rates make this difference not significant (21).

    You can see the obvious implication: If this mutation rate is accurate, then the average human-chimpanzee gene divergence has to be up around 11 million years ago. That can be accommodated with a 7-million-year-old species divergence only if we assume a very large ancestral population -- on the order of 50,000 or higher. Or, the ancestral effective size could be lower -- but that would make the species divergence substantially older -- 9 million years or more.

    There is a second implication. Most studies of human genetic variation have assumed that 5-million-year-old human-chimpanzee divergence and the high associated rate of mutations. If the true rate is less than half that, then the coalescence times of human genes are more than double most estimates. That would include our estimates of human-Neandertal genetic differences.

    Well, that's a fine pickle.

    I'm not quite ready to believe the very low rate estimate. The analysis in this paper uncovered tens of thousands of false positives, and had to filter through those to arrive at 28 true mutations. The filtering involved resequencing all the positives to determine which were true and which were false, but maybe there's room in there for a substantial number of false negatives, too.

    If this low estimate were true of the human-chimpanzee divergence, it would imply vastly higher ages for other primate divergences, or a much lower rate on the human lineage specifically. So that allows another check on the process.

    But generally, I'll be looking at whole-genome family comparisons with great interest, because they will give us a much more precise understanding of the rate of mutations and recombinations across the genome.

    References:

    Roach JC and 14 others. 2010. Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing. Science (early online) doi:10.1126/science.1186802

    Tags: 
    mutation rate
    mutation
    1000 Genomes Project
    genomics
    sequencing
    chumans
    genetic divergence
    coalescent
    Synopsis: 
    Whole genome sequencing of a family finds a very low number of mutations, suggesting evolution doesn't have the timescale we thought.
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Neandertals

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Malapa

Just outside Johannesburg, the Malapa site is producing some of the most exciting finds in human evolution. This site is the headquarters of the Malapa Soft Tissue Project.