Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.

Selfish mutations dysregulating RAS-MAPK signaling are pervasive in aged human testes

Mosaic mutations present in the germline have important implications for reproductive risk and disease transmission. We previously demonstrated a phenomenon occurring in the male germline, whereby specific mutations arising spontaneously in stem cells (spermatogonia) lead to clonal expansion, resulting in elevated mutation levels in sperm over time. This process, termed "selfish spermatogonial selection," explains the high spontaneous birth prevalence and strong paternal age-effect of disorders such as achondroplasia and Apert, Noonan and Costello syndromes, with direct experimental evidence currently available for specific positions of six genes (FGFR2, FGFR3, RET, PTPN11, HRAS, and KRAS). We present a discovery screen to identify novel mutations and genes showing evidence of positive selection in the male germline, by performing massively parallel simplex PCR using RainDance technology to interrogate mutational hotspots in 67 genes (51.5 kb in total) in 276 biopsies of testes from five men (median age, 83 yr). Following ultradeep sequencing (about 16,000×), development of a low-frequency variant prioritization strategy, and targeted validation, we identified 61 distinct variants present at frequencies as low as 0.06%, including 54 variants not previously directly associated with selfish selection. The majority (80%) of variants identified have previously been implicated in developmental disorders and/or oncogenesis and include mutations in six newly associated genes (BRAF, CBL, MAP2K1, MAP2K2, RAF1, and SOS1), all of which encode components of the RAS-MAPK pathway and activate signaling. Our findings extend the link between mutations dysregulating the RAS-MAPK pathway and selfish selection, and show that the aging male germline is a repository for such deleterious mutations.

Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as "sparsistent" (i.e., capable of recovering the graph structure). We then show how proximal gradient algorithms and semi-definite programming techniques can be employed to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs significantly better than its counterparts, for example, by accommodating more latent variables. Finally, the suggested method is applied to two datasets comprising individual level data on genetic variants and metabolites levels. We show our results replicate better than alternative approaches and show enriched biological signal. Supplementary materials for this article are available online.

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

This corrects the article DOI: 10.1038/sdata.2017.179.

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Whole-genome sequencing of spermatocytic tumors provides insights into the mutational processes operating in the male germline

Adult male germline stem cells (spermatogonia) proliferate by mitosis and, after puberty, generate spermatocytes that undertake meiosis to produce haploid spermatozoa. Germ cells are under evolutionary constraint to curtail mutations and maintain genome integrity. Despite constant turnover, spermatogonia very rarely form tumors, so-called spermatocytic tumors (SpT). In line with the previous identification of FGFR3 and HRAS selfish mutations in a subset of cases, candidate gene screening of 29 SpTs identified an oncogenic NRAS mutation in two cases. To gain insights in the etiology of SpT and into properties of the male germline, we performed whole-genome sequencing of five tumors (4/5 with matched normal tissue). The acquired single nucleotide variant load was extremely low (~0.2 per Mb), with an average of 6 (2-9) non-synonymous variants per tumor, none of which is likely to be oncogenic. The observed mutational signature of SpTs is strikingly similar to that of germline de novo mutations, mostly involving C>T transitions with a significant enrichment in the ACG trinucleotide context. The tumors exhibited extensive aneuploidy (50-99 autosomes/tumor) involving whole-chromosomes, with recurrent gains of chr9 and chr20 and loss of chr7, suggesting that aneuploidy itself represents the initiating oncogenic event. We propose that SpT etiology recapitulates the unique properties of male germ cells; because of evolutionary constraints to maintain low point mutation rate, rare tumorigenic driver events are caused by a combination of gene imbalance mediated via whole-chromosome aneuploidy. Finally, we propose a general framework of male germ cell tumor pathology that accounts for their mutational landscape, timing and cellular origin.

Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus

Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. We use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals chronically infected with HCV, predominately genotype 3. We show that both HLA alleles and interferon lambda innate immune system genes drive viral genome polymorphism, and that IFNL4 genotypes determine HCV viral load through a mechanism that is dependent on a specific polymorphism in the HCV polyprotein. We highlight the interplay between innate immune responses and the viral genome in HCV control.

Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes

A central challenge in the analysis of genetic variation is to provide realistic genome simulation across millions of samples. Present day coalescent simulations do not scale well, or use approximations that fail to capture important long-range linkage properties. Analysing the results of simulations also presents a substantial challenge, as current methods to store genealogies consume a great deal of space, are slow to parse and do not take advantage of shared structure in correlated trees. We solve these problems by introducing sparse trees and coalescence records as the key units of genealogical analysis. Using these tools, exact simulation of the coalescent with recombination for chromosome-sized regions over hundreds of thousands of samples is possible, and substantially faster than present-day approximate methods. We can also analyse the results orders of magnitude more quickly than with existing methods.

Our understanding of the distribution of genetic variation in natural populations has been driven by mathematical models of the underlying biological and demographic processes. A key strength of such coalescent models is that they enable efficient simulation of data we might see under a variety of evolutionary scenarios. However, current methods are not well suited to simulating genome-scale data sets on hundreds of thousands of samples, which is essential if we are to understand the data generated by population-scale sequencing projects. Similarly, processing the results of large simulations also presents researchers with a major challenge, as it can take many days just to read the data files. In this paper we solve these problems by introducing a new way to represent information about the ancestral process. This new representation leads to huge gains in simulation speed and storage efficiency so that large simulations complete in minutes and the output files can be processed in seconds.

Trinculo: Bayesian and frequentist multinomial logistic regression for genome-wide association studies of multi-category phenotypes

Motivation: For many classes of disease the same genetic risk variants underly many related phenotypes or disease subtypes. Multinomial logistic regression provides an attractive framework to analyze multi-category phenotypes, and explore the genetic relationships between these phenotype categories. We introduce Trinculo, a program that implements a wide range of multinomial analyses in a single fast package that is designed to be easy to use by users of standard genome-wide association study software.

Availability and implementation: An open source C implementation, with code and binaries for Linux and Mac OSX, is available for download at http://sourceforge.net/projects/trinculo

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact: lj4@well.ox.ac.uk

Factors influencing success of clinical genome sequencing across a broad spectrum of disorders

To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges.

Comprehensive genome-wide evaluation of lapatinib-induced liver injury yields a single genetic signal centered on known risk allele HLA-DRB1*07:01

Lapatinib is associated with a low incidence of serious liver injury. Previous investigations have identified and confirmed the Class II allele HLA-DRB1*07:01 to be strongly associated with lapatinib-induced liver injury; however, the moderate positive predictive value limits its clinical utility. To assess whether additional genetic variants located within the major histocompatibility complex locus or elsewhere in the genome may influence lapatinib-induced liver injury risk, and potentially lead to a genetic association with improved predictive qualities, we have taken two approaches: a genome-wide association study and a whole-genome sequencing study. This evaluation did not reveal additional associations other than the previously identified association for HLA-DRB1*07:01. The present study represents the most comprehensive genetic evaluation of drug-induced liver injury (DILI) or hypersensitivity, and suggests that investigation of possible human leukocyte antigen associations with DILI and other hypersensitivities represents an important first step in understanding the mechanism of these events.

Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia

We describe an analysis of genome variation in 825 P. falciparum samples from Asia and Africa that identifies an unusual pattern of parasite population structure at the epicenter of artemisinin resistance in western Cambodia. Within this relatively small geographic area, we have discovered several distinct but apparently sympatric parasite subpopulations with extremely high levels of genetic differentiation. Of particular interest are three subpopulations, all associated with clinical resistance to artemisinin, which have skewed allele frequency spectra and high levels of haplotype homozygosity, indicative of founder effects and recent population expansion. We provide a catalog of SNPs that show high levels of differentiation in the artemisinin-resistant subpopulations, including codon variants in transporter proteins and DNA mismatch repair proteins. These data provide a population-level genetic framework for investigating the biological origins of artemisinin resistance and for defining molecular markers to assist in its elimination.

Whole-exome sequencing studies of nonfunctioning pituitary adenomas

CONTEXT

The tumorigenic role of genetic abnormalities in sporadic pituitary nonfunctioning adenomas (NFAs), which usually originate from gonadotroph cells, is unknown.

OBJECTIVE

The objective of the study was to identify somatic genetic abnormalities in sporadic pituitary NFAs.

DESIGN

Whole-exome sequencing was performed using DNA from 7 pituitary NFAs and leukocyte samples obtained from the same patients. Somatic variants were confirmed by dideoxynucleotide sequencing, and candidate driver genes were assessed in an additional 24 pituitary NFAs.

RESULTS

Whole-exome sequencing achieved a high degree of coverage such that approximately 97% of targeted bases were represented by more than 10 base reads; 24 somatic variants were identified and confirmed in the discovery set of 7 pituitary NFAs (mean 3.5 variants/tumor; range 1-7). Approximately 80% of variants occurred as missense single nucleotide variants and the remainder were synonymous changes or small frameshift deletions. Each of the 24 mutations occurred in independent genes with no recurrent mutations. Mutations were not observed in genes previously associated with pituitary tumorigenesis, although somatic variants in putative driver genes including platelet-derived growth factor D (PDGFD), N-myc down-regulated gene family member 4 (NDRG4), and Zipper sterile-α-motif kinase (ZAK) were identified; however, DNA sequence analysis of these in the validation set of 24 pituitary NFAs did not reveal any mutations indicating that these genes are unlikely to contribute significantly in the etiology of sporadic pituitary NFAs.

CONCLUSIONS

Pituitary NFAs harbor few somatic mutations consistent with their low proliferation rates and benign nature, but mechanisms other than somatic mutation are likely involved in the etiology of sporadic pituitary NFAs.

Multiple instances of ancient balancing selection shared between humans and chimpanzees

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.

Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas

Many individuals with multiple or large colorectal adenomas or early-onset colorectal cancer (CRC) have no detectable germline mutations in the known cancer predisposition genes. Using whole-genome sequencing, supplemented by linkage and association analysis, we identified specific heterozygous POLE or POLD1 germline variants in several multiple-adenoma and/or CRC cases but in no controls. The variants associated with susceptibility, POLE p.Leu424Val and POLD1 p.Ser478Asn, have high penetrance, and POLD1 mutation was also associated with endometrial cancer predisposition. The mutations map to equivalent sites in the proofreading (exonuclease) domain of DNA polymerases ɛ and δ and are predicted to cause a defect in the correction of mispaired bases inserted during DNA replication. In agreement with this prediction, the tumors from mutation carriers were microsatellite stable but tended to acquire base substitution mutations, as confirmed by yeast functional assays. Further analysis of published data showed that the recently described group of hypermutant, microsatellite-stable CRCs is likely to be caused by somatic POLE mutations affecting the exonuclease domain.

Recessive mutations in SPTBN2 implicate β-III spectrin in both cognitive and motor development

β-III spectrin is present in the brain and is known to be important in the function of the cerebellum. Heterozygous mutations in SPTBN2, the gene encoding β-III spectrin, cause Spinocerebellar Ataxia Type 5 (SCA5), an adult-onset, slowly progressive, autosomal-dominant pure cerebellar ataxia. SCA5 is sometimes known as "Lincoln ataxia," because the largest known family is descended from relatives of the United States President Abraham Lincoln. Using targeted capture and next-generation sequencing, we identified a homozygous stop codon in SPTBN2 in a consanguineous family in which childhood developmental ataxia co-segregates with cognitive impairment. The cognitive impairment could result from mutations in a second gene, but further analysis using whole-genome sequencing combined with SNP array analysis did not reveal any evidence of other mutations. We also examined a mouse knockout of β-III spectrin in which ataxia and progressive degeneration of cerebellar Purkinje cells has been previously reported and found morphological abnormalities in neurons from prefrontal cortex and deficits in object recognition tasks, consistent with the human cognitive phenotype. These data provide the first evidence that β-III spectrin plays an important role in cortical brain development and cognition, in addition to its function in the cerebellum; and we conclude that cognitive impairment is an integral part of this novel recessive ataxic syndrome, Spectrin-associated Autosomal Recessive Cerebellar Ataxia type 1 (SPARCA1). In addition, the identification of SPARCA1 and normal heterozygous carriers of the stop codon in SPTBN2 provides insights into the mechanism of molecular dominance in SCA5 and demonstrates that the cell-specific repertoire of spectrin subunits underlies a novel group of disorders, the neuronal spectrinopathies, which includes SCA5, SPARCA1, and a form of West syndrome.

Bayesian refinement of association signals for 14 loci in 3 common diseases

To further investigate susceptibility loci identified by genome-wide association studies, we genotyped 5,500 SNPs across 14 associated regions in 8,000 samples from a control group and 3 diseases: type 2 diabetes (T2D), coronary artery disease (CAD) and Graves' disease. We defined, using Bayes theorem, credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs. In 3 of the 14 regions, TCF7L2 (T2D), CTLA4 (Graves' disease) and CDKN2A-CDKN2B (T2D), much of the posterior probability rested on a single SNP, and, in 4 other regions (CDKN2A-CDKN2B (CAD) and CDKAL1, FTO and HHEX (T2D)), the 95% sets were small, thereby excluding most SNPs as potentially causal. Very few SNPs in our credible sets had annotated functions, illustrating the limitations in understanding the mechanisms underlying susceptibility to common diseases. Our results also show the value of more detailed mapping to target sequences for functional studies.

Detecting novel associations in large data sets

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

The variant call format and VCFtools

SUMMARY

The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.

AVAILABILITY

http://vcftools.sourceforge.net

Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility

Ankylosing spondylitis is a common form of inflammatory arthritis predominantly affecting the spine and pelvis that occurs in approximately 5 out of 1,000 adults of European descent. Here we report the identification of three variants in the RUNX3, LTBR-TNFRSF1A and IL12B regions convincingly associated with ankylosing spondylitis (P < 5 × 10(-8) in the combined discovery and replication datasets) and a further four loci at PTGER4, TBKBP1, ANTXR2 and CARD9 that show strong association across all our datasets (P < 5 × 10(-6) overall, with support in each of the three datasets studied). We also show that polymorphisms of ERAP1, which encodes an endoplasmic reticulum aminopeptidase involved in peptide trimming before HLA class I presentation, only affect ankylosing spondylitis risk in HLA-B27-positive individuals. These findings provide strong evidence that HLA-B27 operates in ankylosing spondylitis through a mechanism involving aberrant processing of antigenic peptides.

The variant call format and VCFtools

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.

Availability:http://vcftools.sourceforge.net

Contact:rd@sanger.ac.uk

High recombination rates and hotspots in a Plasmodium falciparum genetic cross

Background

The human malaria parasite Plasmodium falciparum survives pressures from the host immune system and antimalarial drugs by modifying its genome. Genetic recombination and nucleotide substitution are the two major mechanisms that the parasite employs to generate genome diversity. A better understanding of these mechanisms may provide important information for studying parasite evolution, immune evasion and drug resistance.

Results

Here, we used a high-density tiling array to estimate the genetic recombination rate among 32 progeny of a P. falciparum genetic cross (7G8 × GB4). We detected 638 recombination events and constructed a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb per centimorgan and identified 54 candidate recombination hotspots. Similar to centromeres in other organisms, the sequences of P. falciparum centromeres are found in chromosome regions largely devoid of recombination activity. Motifs enriched in hotspots were also identified, including a 12-bp G/C-rich motif with 3-bp periodicity that may interact with a protein containing 11 predicted zinc finger arrays.

Conclusions

These results show that the P. falciparum genome has a high recombination rate, although it also follows the overall rule of meiosis in eukaryotes with an average of approximately one crossover per chromosome per meiosis. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination rate observed. The lack of recombination activity in centromeric regions is consistent with the observations of reduced recombination near the centromeres of other organisms.

Dindel: accurate indel calls from short-read data

Small insertions and deletions (indels) are a common and functionally important type of sequence polymorphism. Most of the focus of studies of sequence variation is on single nucleotide variants (SNVs) and large structural variants. In principle, high-throughput sequencing studies should allow identification of indels just as SNVs. However, inference of indels from next-generation sequence data is challenging, and so far methods for identifying indels lag behind methods for calling SNVs in terms of sensitivity and specificity. We propose a Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs. We show that our method is sensitive and achieves low false discovery rates on simulated and real data sets, although challenges remain. The algorithm is implemented in the program Dindel, which has been used in the 1000 Genomes Project call sets.

A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.

Estimating meiotic gene conversion rates from population genetic data

Gene conversion plays an important part in shaping genetic diversity in populations, yet estimating the rate at which it occurs is difficult because of the short lengths of DNA involved. We have developed a new statistical approach to estimating gene conversion rates from genetic variation, by extending an existing model for haplotype data in the presence of crossover events. We show, by simulation, that when the rate of gene conversion events is at least comparable to the rate of crossover events, the method provides a powerful approach to the detection of gene conversion and estimation of its rate. Application of the method to data from the telomeric X chromosome of Drosophila melanogaster, in which crossover activity is suppressed, indicates that gene conversion occurs approximately 400 times more often than crossover events. We also extend the method to estimating variable crossover and gene conversion rates and estimate the rate of gene conversion to be approximately 1.5 times higher than the crossover rate in a region of human chromosome 1 with known recombination hotspots.

A model-based approach to capture genetic variation for future association studies

Genome-wide association studies are still constrained by the cost of genotyping. For this reason, the selection of a reduced set of markers or tags able to capture a significant proportion of the genetic variation is an important aspect of these studies. Most tagging SNP selection methods have been successful in capturing the genetic variation of the data from which the tags have been chosen. However, when these tags are used in an independent data set, a significant proportion of the remaining SNPs (non-tags) are not captured and, in most cases, there is no information on which SNPs are captured. We propose to use a probabilistic model to predict the non-tags based on a set of tags, as a way to capture genetic variation. An important advantage of this method is that it directly predicts the genotype of the non-tags with which we can test for association with the phenotype and which could help to elucidate the location of genes responsible for increasing disease susceptibility. Additionally, this method provides an estimate of the probabilities with which the predictions are made, which reflects the confidence of the probabilistic model. We also propose new methods to select the tagging SNPs. We empirically show by using HapMap data that our approach is able to capture significantly more genetic variation than methods based solely on a pairwise LD measure.

The distribution and causes of meiotic recombination in the human genome

Using the statistical analysis of genetic variation, we have developed a high-resolution genetic map of recombination hotspots and recombination rate variation across the human genome. This map, which has a resolution several orders of magnitude greater than previous studies, identifies over 25,000 recombination hotspots and gives new insights into the distribution and determination of recombination. Wavelet-based analysis demonstrates scale-specific influences of base composition, coding context and DNA repeats on recombination rates, though, in contrast with other species, no association with DNase I hypersensitivity. We have also identified specific DNA motifs that are strongly associated with recombination hotspots and whose activity is influenced by local context. Comparative analysis of recombination rates in humans and chimpanzees demonstrates very high rates of evolution of the fine-scale structure of the recombination landscape. In the light of these observations, we suggest possible resolutions of the hotspot paradox.

The DNA sequence and biological annotation of human chromosome 1

The reference sequence for each human chromosome provides the framework for understanding genome function, variation and evolution. Here we report the finished sequence and biological annotation of human chromosome 1. Chromosome 1 is gene-dense, with 3,141 genes and 991 pseudogenes, and many coding sequences overlap. Rearrangements and mutations of chromosome 1 are prevalent in cancer and many other diseases. Patterns of sequence variation reveal signals of recent selection in specific genes that may contribute to human fitness, and also in regions where no function is evident. Fine-scale recombination occurs in hotspots of varying intensity along the sequence, and is enriched near genes. These and other studies of human biology and disease encoded within chromosome 1 are made possible with the highly accurate annotated sequence, as part of the completed set of chromosome sequences that comprise the reference human genome.

Estimating diversifying selection and functional constraint in the presence of recombination

Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques.

The influence of mutation, recombination, population history, and selection on patterns of genetic diversity in Neisseria meningitidis

Patterns of genetic diversity within populations of human pathogens, shaped by the ecology of host-microbe interactions, contain important information about the epidemiological history of infectious disease. Exploiting this information, however, requires a systematic approach that distinguishes the genetic signal generated by epidemiological processes from the effects of other forces, such as recombination, mutation, and population history. Here, a variety of quantitative techniques were employed to investigate multilocus sequence information from isolate collections of Neisseria meningitidis, a major cause of meningitis and septicemia world wide. This allowed quantitative evaluation of alternative explanations for the observed population structure. A coalescent-based approach was employed to estimate the rate of mutation, the rate of recombination, and the size distribution of recombination fragments from samples from disease-associated and carried meningococci obtained in the Czech Republic in 1993 and a global collection of disease-associated isolates collected globally from 1937 to 1996. The parameter estimates were used to reject a model in which genetic structure arose by chance in small populations, and analysis of molecular variation showed that geographically restricted gene flow was unlikely to be the cause of the genetic structure. The genetic differentiation between disease and carriage isolate collections indicated that, whereas certain genotypes were overrepresented among the disease-isolate collections (the "hyperinvasive" lineages), disease-associated and carried meningococci exhibited remarkably little differentiation at the level of individual nucleotide polymorphisms. In combination, these results indicated the repeated action of natural selection on meningococcal populations, possibly arising from the coevolutionary dynamic of host-pathogen interactions.

Germs, genomes and genealogies

Genetic diversity in pathogen species contains information about evolutionary and epidemiological processes, including the origins and history of disease, the nature of the selective forces acting on pathogen genes and the role of recombination in generating genetic novelty. Here, we review recent developments in these fields and compare the use of population genetic, or population-model based, approaches to phylogenetic, or population-model free, methodologies. We show how simple epidemiological models can be related to the ancestral, or coalescent, process underlying samples from pathogen species, enabling detailed inference about pathogen biology from patterns of molecular variation.

Evolutionary genetics: what is driving male mutation?

In mammals, most new mutations occur in males. But a study of the evolution of a human X to Y chromosomal translocation has revealed a sex bias much lower than previous estimates. Patterns of substitution suggest that differential methylation between male and female germ lines is a key determinant of the mutation rate.

Genetic conflicts and the paradox of sex determination: three paths to the evolution of female intersexuality in a mammal

That sex determining systems ever change is paradoxical but can be explained by noting that conflict between selfish elements and their modifiers will often cause a shift in sex determining strategy. The evolution of the novel sex determining system of moles (Talpa europaea and T. occidentalis) may, we argue, be an example of just such a process. Three different models for the evolution of female intersexuality are presented. These all attempt to account for (1) the fact that a few years ago populations of moles had high frequencies of sterile XX individuals that were either morphologically male or intersex (other XX individuals were normal females) and (2) that presently, the XX individuals in the same population are exclusively fertile intersexes that are functionally female; i.e. have follicle producing ovotestes. This case history is compared to that of the wood lemming and two similarities are discussed. First, in both cases it is noted that one end product could be approached from different routes. Second, selfish elements may be involved in the evolution of both systems. In general, it is suggested that XY sex determination, far from being resilient to evolutionary change, is vulnerable to take-over by selfish elements. This is particularly the case in mammals in which transplacental interactions could allow manipulation of sex determination in one foetus by another. This, we also suggest, is a good candidate explanation for the evolution of novel sex determination in Talpa.