Graham Coop

PRDM9 is a Major Determinant of Meiotic Recombination Hotspots in humans and mice

Homing in on Hotspots The clustering of recombination in the genome, around locations known as hotspots, is associated with specific DNA motifs. Now, using a variety of techniques, three studies implicate a chromatin-modifying protein, the histone-methyltransferase PRDM9, as a major factor involved in human hotspots (see the Perspective by Cheung et al.). Parvanov et al. (p. 835, published online 31 December) mapped the locus in mice, and analyzed allelic variation in mice and humans, whereas Myers et al. (p. 876, published online 31 December) used a comparative analysis between human and chimpanzees to show that the recombination process leads to a self-destructive drive in which the very motifs that recruit hotspots are eliminated from our genome. Baudat et al. (p. 836, published online 31 December) took this analysis a step further to identify human allelic variants within Prdm9 that differed in the frequency at which they used hotspots. Furthermore, differential binding of this protein to different human alleles suggests that this protein interacts with specific DNA sequences. Thus, PDRM9 functions in the determination of recombination loci within the genome and may be a significant factor in the genomic differences between closely related species. A chromatin-modifying enzyme is implicated in the determination of recombination loci within the genome. Meiotic recombination events cluster into narrow segments of the genome, defined as hotspots. Here, we demonstrate that a major player for hotspot specification is the Prdm9 gene. First, two mouse strains that differ in hotspot usage are polymorphic for the zinc finger DNA binding array of PRDM9. Second, the human consensus PRDM9 allele is predicted to recognize the 13-mer motif enriched at human hotspots; this DNA binding specificity is verified by in vitro studies. Third, allelic variants of PRDM9 zinc fingers are significantly associated with variability in genome-wide hotspot usage among humans. Our results provide a molecular basis for the distribution of meiotic recombination in mammals, in which the binding of PRDM9 to specific DNA sequences targets the initiation of recombination at specific locations in the genome.

Signals of recent positive selection in a worldwide sample of human populations

Genome-wide scans for recent positive selection in humans have yielded insight into the mechanisms underlying the extensive phenotypic diversity in our species, but have focused on a limited number of populations. Here, we present an analysis of recent selection in a global sample of 53 populations, using genotype data from the Human Genome Diversity-CEPH Panel. We refine the geographic distributions of known selective sweeps, and find extensive overlap between these distributions for populations in the same continental region but limited overlap between populations outside these groupings. We present several examples of previously unrecognized candidate targets of selection, including signals at a number of genes in the NRG-ERBB4 developmental pathway in non-African populations. Analysis of recently identified genes involved in complex diseases suggests that there has been selection on loci involved in susceptibility to type II diabetes. Finally, we search for local adaptation between geographically close populations, and highlight several examples.

Sequencing and Analysis of Neanderthal Genomic DNA

Our knowledge of Neanderthals is based on a limited number of remains and artifacts from which we must make inferences about their biology, behavior, and relationship to ourselves. Here, we describe the characterization of these extinct hominids from a new perspective, based on the development of a Neanderthal metagenomic library and its high-throughput sequencing and analysis. Several lines of evidence indicate that the 65,250 base pairs of hominid sequence so far identified in the library are of Neanderthal origin, the strongest being the ascertainment of sequence identities between Neanderthal and chimpanzee at sites where the human genomic sequence is different. These results enabled us to calculate the human-Neanderthal divergence time based on multiple randomly distributed autosomal loci. Our analyses suggest that on average the Neanderthal genomic sequence we obtained and the reference human genome sequence share a most recent common ancestor ∼706,000 years ago, and that the human and Neanderthal ancestral populations split ∼370,000 years ago, before the emergence of anatomically modern humans. Our finding that the Neanderthal and human genomes are at least 99.5% identical led us to develop and successfully implement a targeted method for recovering specific ancient DNA sequences from metagenomic libraries. This initial analysis of the Neanderthal genome advances our understanding of the evolutionary relationship of Homo sapiens and Homo neanderthalensis and signifies the dawn of Neanderthal genomics.

A worldwide survey of haplotype variation and linkage disequilibrium in the human genome

Recent genomic surveys have produced high-resolution haplotype information, but only in a small number of human populations. We report haplotype structure across 12 Mb of DNA sequence in 927 individuals representing 52 populations. The geographic distribution of haplotypes reflects human history, with a loss of haplotype diversity as distance increases from Africa. Although the extent of linkage disequilibrium (LD) varies markedly across populations, considerable sharing of haplotype structure exists, and inferred recombination hotspot locations generally match across groups. The four samples in the International HapMap Project contain the majority of common haplotypes found in most populations: averaging across populations, 83% of common 20-kb haplotypes in a population are also common in the most similar HapMap sample. Consequently, although the portability of tag SNPs based on the HapMap is reduced in low-LD Africans, the HapMap will be helpful for the design of genome-wide association mapping studies in nearly all human populations.

Using Environmental Correlations to Identify Loci Underlying Local Adaptation

Loci involved in local adaptation can potentially be identified by an unusual correlation between allele frequencies and important ecological variables or by extreme allele frequency differences between geographic regions. However, such comparisons are complicated by differences in sample sizes and the neutral correlation of allele frequencies across populations due to shared history and gene flow. To overcome these difficulties, we have developed a Bayesian method that estimates the empirical pattern of covariance in allele frequencies between populations from a set of markers and then uses this as a null model for a test at individual SNPs. In our model the sample frequencies of an allele across populations are drawn from a set of underlying population frequencies; a transform of these population frequencies is assumed to follow a multivariate normal distribution. We first estimate the covariance matrix of this multivariate normal across loci using a Monte Carlo Markov chain. At each SNP, we then provide a measure of the support, a Bayes factor, for a model where an environmental variable has a linear effect on the transformed allele frequencies compared to a model given by the covariance matrix alone. This test is shown through power simulations to outperform existing correlation tests. We also demonstrate that our method can be used to identify SNPs with unusually large allele frequency differentiation and offers a powerful alternative to tests based on pairwise or global FST. Software is available at http://www.eve.ucdavis.edu/gmcoop/.

THE SIGNATURE OF POSITIVE SELECTION ON STANDING GENETIC VARIATION

Abstract Considerable interest is focused on the use of polymorphism data to identify regions of the genome that underlie recent adaptations. These searches are guided by a simple model of positive selection, in which a mutation is favored as soon as it arises. This assumption may not be realistic, as environmental changes and range expansions may lead previously neutral or deleterious alleles to become beneficial. We examine what effect this mode of selection has on patterns of variation at linked neutral sites by implementing a new coalescent model of positive directional selection on standing variation. In this model, a neutral allele arises and drifts in the population, then at frequency f becomes beneficial, and eventually reaches fixation. Depending on the value of f, this scenario can lead to a large variance in allele frequency spectra and in levels of linkage disequilibrium at linked, neutral sites. In particular, for intermediate f, the beneficial substitution often leads to a loss of rare alleles–a pattern that differs markedly from the signature of directional selection currently relied on by researchers. These findings highlight the importance of an accurate characterization of the effects of positive selection, if we are to reliably identify recent adaptations from polymorphism data.

The Role of Geography in Human Adaptation

Various observations argue for a role of adaptation in recent human evolution, including results from genome-wide studies and analyses of selection signals at candidate genes. Here, we use genome-wide SNP data from the HapMap and CEPH-Human Genome Diversity Panel samples to study the geographic distributions of putatively selected alleles at a range of geographic scales. We find that the average allele frequency divergence is highly predictive of the most extreme FST values across the whole genome. On a broad scale, the geographic distribution of putatively selected alleles almost invariably conforms to population clusters identified using randomly chosen genetic markers. Given this structure, there are surprisingly few fixed or nearly fixed differences between human populations. Among the nearly fixed differences that do exist, nearly all are due to fixation events that occurred outside of Africa, and most appear in East Asia. These patterns suggest that selection is often weak enough that neutral processes—especially population history, migration, and drift—exert powerful influences over the fate and geographic distribution of selected alleles.

High-Resolution Mapping of Crossovers Reveals Extensive Variation in Fine-Scale Recombination Patterns Among Humans

Recombination plays a crucial role in meiosis, ensuring the proper segregation of chromosomes. Recent linkage disequilibrium (LD) and sperm-typing studies suggest that recombination rates vary tremendously across the human genome, with most events occurring in narrow “hotspots.” To examine variation in fine-scale recombination patterns among individuals, we used dense, genome-wide single-nucleotide polymorphism data collected in nuclear families to localize crossovers with high spatial resolution. This analysis revealed that overall recombination hotspot usage is similar in males and females, with individual hotspots often active in both sexes. Across the genome, roughly 60% of crossovers occurred in hotspots inferred from LD studies. Notably, however, we found extensive and heritable variation among both males and females in the proportion of crossovers occurring in these hotspots.

Adaptations to climate in candidate genes for common metabolic disorders.

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.

An evolutionary view of human recombination

Recombination has essential functions in mammalian meiosis, which impose several constraints on the recombination process. However, recent studies have shown that, in spite of these roles, recombination rates vary tremendously among humans, and show marked differences between humans and closely related species. These findings provide important insights into the determinants of recombination rates and raise new questions about the selective pressures that affect recombination over different genomic scales, with implications for human genetics and evolutionary biology.