Disequilibrium likelihoods for fine-scale mapping of a rare allele.

Genetic linkage studies based on pedigree data have limited resolution, because of the relatively small number of segregations. Disequilibrium mapping, which uses population associations to infer the location of a disease mutation, provides one possible strategy for narrowing the candidate region. The coalescent process provides a model for the ancestry of a sample of disease alleles, and recombination events between disease locus and marker may be placed on this ancestral phylogeny. These events define the recombinant classes, the sets of sampled disease copies descending from the meiosis at which a given recombination occurred. We show how Monte Carlo generation of the recombinant classes leads to a linkage likelihood for fine-scale mapping from disease haplotypes. We compare single-marker disequilibrium mapping with interval-disequilibrium mapping and discuss how the approach may be extended to multipoint-disequilibrium mapping. The method and its properties are illustrated with an example of simulated data, constructed to be typical of fine-scale mapping of a rare disease in the Japanese population. The method can take into account known features of population history, such as changing patterns of population growth.     

Effects of mutation position on frequency of marker rescue by homologous recombination.

Homologous recombination between transferred and chromosomal DNA can be used for mapping mutations by marker rescue, i.e., by identifying which segment of wild-type DNA can recombine with the mutant chromosomal gene and restore normal function. In order to define how much the fragments should overlap each other for reliable mapping, we have measured how the frequency of marker rescue is affected by the position of the chromosomal mutation relative to the ends of the transferred DNA fragments. For this purpose, we used several DNA fragments to effect marker rescue in two mutant hybridomas which bear mutations 673 bp apart in the exons encoding the second and third constant region domains of the immunoglobulin mu heavy chain. The frequency of marker rescue decreased greatly when the mutation was located near one of the ends of the fragments, the results indicating that fragments should be designed to overlap by at least several hundred base pairs. Possible explanations for this "end effect" are considered.     

Bayesian fine-scale mapping of disease loci, by hidden Markov models

We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers-in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain-Monte Carlo simulation methods, we obtain posterior distributions of model parameter estimates including disease-gene location and the age of the disease-predisposing mutation. In addition, we allow for heterogeneity in recombination rates, across the candidate region, to account for recombination hot and cold spots. We also obtain, for the ancestral marker haplotype, a posterior distribution that is unique to our method and that, unlike maximum-likelihood estimation, can properly account for uncertainty. We apply the method to data for cystic fibrosis and Huntington disease, for which mutations in disease genes have already been identified. The new method performs well compared with existing multi-locus mapping methods.     

Sex differences in recombination and mapping adaptations

Since the raw material of marker based mapping is recombination, understanding how and why recombination rates evolve, and how we can use variation in these rates will ultimately help to improve map resolution. For example, using this variation could help in discriminating between linkage and pleiotropy when QTL for several traits co-locate. It might also be used to improve QTL mapping algorithms. The goals of this chapter are: (1) to highlight differences in recombination rates between the sexes, (2) describe why we might expect these differences, and (3) explore how sex difference in recombination can be used to improve resolution in QTL mapping.     

Algorithms to distinguish the role of gene-conversion from single-crossover recombination in the derivation of SNP sequences in populations.

Meiotic recombination is a fundamental biological event and one of the principal evolutionary forces responsible for shaping genetic variation within species. In addition to its fundamental role, recombination is central to several critical applied problems. The most important example is "association mapping" in populations, which is widely hoped to help find genes that influence genetic diseases (Carlson et al., 2004; Clark, 2003). Hence, a great deal of recent attention has focused on problems of inferring the historical derivation of sequences in populations when both mutations and recombinations have occurred. In the algorithms literature, most of that recent work has been directed to single-crossover recombination. However, gene-conversion is an important, and more common, form of (two-crossover) recombination which has been much less investigated in the algorithms literature. In this paper, we explicitly incorporate gene-conversion into discrete methods to study historical recombination. We are concerned with algorithms for identifying and locating the extent of historical crossing-over and gene-conversion (along with single-nucleotide mutation), and problems of constructing full putative histories of those events. The novel technical issues concern the incorporation of gene-conversion into recently developed discrete methods (Myers and Griffiths, 2003; Song et al., 2005) that compute lower and upper-bound information on the amount of needed recombination without gene-conversion. We first examine the most natural extension of the lower bound methods from Myers and Griffiths (2003), showing that the extension can be computed efficiently, but that this extension can only yield weak lower bounds. We then develop additional ideas that lead to higher lower bounds, and show how to solve, via integer-linear programming, a more biologically realistic version of the lower bound problem. We also show how to compute effective upper bounds on the number of needed single-crossovers and gene-conversions, along with explicit networks showing a putative history of mutations, single-crossovers and gene-conversions. Both lower and upper bound methods can handle data with missing entries, and the upper bound method can be used to infer missing entries with high accuracy. We validate the significance of these methods by showing that they can be effectively used to distinguish simulation-derived sequences generated without gene-conversion from sequences that were generated with gene-conversion. We apply the methods to recently studied sequences of Arabidopsis thaliana, identifying many more regions in the sequences than were previously identified (Plagnol et al., 2006), where gene-conversion may have played a significant role. Demonstration software is available at www.csif.cs.ucdavis.edu/~gusfield.     

Estimating the Relative Roles of Recombination and Point Mutation in the Generation of Single Locus Variants in Campylobacter jejuni and Campylobacter coli

Single locus variants (SLVs) are bacterial sequence types that differ at only one of the seven canonical multilocus sequence typing (MLST) loci. Estimating the relative roles of recombination and point mutation in the generation of new alleles that lead to SLVs is helpful in understanding how organisms evolve. The relative rates of recombination and mutation for Campylobacter jejuni and Campylobacter coli were estimated at seven different housekeeping loci from publically available MLST data. The probability of recombination generating a new allele that leads to an SLV is estimated to be roughly seven times more than that of mutation for C. jejuni, but for C. coli recombination and mutation were estimated to have a similar contribution to the generation of SLVs. The majority of nucleotide differences (98?% for C. jejuni and 85?% for C. coli) between strains that make up an SLV are attributable to recombination. These estimates are much larger than estimates of the relative rate of recombination to mutation calculated from more distantly related isolates using MLST data. One explanation for this is that purifying selection plays an important role in the evolution of Campylobacter. A simulation study was performed to test the performance of our method under a range of biologically realistic parameters. We found that our method performed well when the recombination tract length was longer than 3?kb. For situations in which recombination may occur with shorter tract lengths, our estimates are likely to be an underestimate of the ratio of recombination to mutation, and of the importance of recombination for creating diversity in closely related isolates. A parametric bootstrap method was applied to calculate the uncertainty of these estimates.     

Mapping trait loci by use of inferred ancestral recombination graphs

Large-scale association studies are being undertaken with the hope of uncovering the genetic determinants of complex disease. We describe a computationally efficient method for inferring genealogies from population genotype data and show how these genealogies can be used to fine map disease loci and interpret association signals. These genealogies take the form of the ancestral recombination graph (ARG). The ARG defines a genealogical tree for each locus, and, as one moves along the chromosome, the topologies of consecutive trees shift according to the impact of historical recombination events. There are two stages to our analysis. First, we infer plausible ARGs, using a heuristic algorithm, which can handle unphased and missing data and is fast enough to be applied to large-scale studies. Second, we test the genealogical tree at each locus for a clustering of the disease cases beneath a branch, suggesting that a causative mutation occurred on that branch. Since the true ARG is unknown, we average this analysis over an ensemble of inferred ARGs. We have characterized the performance of our method across a wide range of simulated disease models. Compared with simpler tests, our method gives increased accuracy in positioning untyped causative loci and can also be used to estimate the frequencies of untyped causative alleles. We have applied our method to Ueda et al.'s association study of CTLA4 and Graves disease, showing how it can be used to dissect the association signal, giving potentially interesting results of allelic heterogeneity and interaction. Similar approaches analyzing an ensemble of ARGs inferred using our method may be applicable to many other problems of inference from population genotype data.     

Homologous Recombination between Episomal Plasmids and Chromosomes in Yeast

We have observed genetic recombination between ura3( -) mutations (among them extensive deletions) carried on "episomal" (i.e., 2micro DNA-containing) plasmids and other ura3( -) alleles present at the normal chromosomal URA3 locus. The recombination frequency found was comparable to the level observed for classical mitotic recombination but was relatively insensitive to sunlamp radiation, which strongly stimulates mitotic recombination. Three equally frequent classes could be distinguished among the recombinants. Two of these are the apparent result of gene conversions (or double crossovers) which leave the URA3(+) allele on the chromosome (class I) or on the plasmid (class II). The third class is apparently due to a single crossover that results in the integration of the plasmid into a chromosome. Plasmid-chromosome recombination can be useful in fine structure genetic mapping, since recombination between a chromosomal point mutation and a plasmid-borne deletion mutation only 25 base pairs distant was easily detected.     

Genomic haplotype blocks may not accurately reflect spatial variation in historic recombination intensity

Recently, genomic data have revealed a "block-like" structure of haplotype diversity on human chromosomes. This structure is anticipated to facilitate gene mapping studies, because strong associations among loci within a block may allow haplotype variation to be tagged with a limited number of markers. But its usefulness to mapping efforts depends on the consistency of the block structure within and among populations, which in turn depends on how the block structure arises. Recombination hot spots are generally thought to underlie the block structure, but haplotype blocks can also develop stochastically under random recombination, in which case the block structure will show limited consistency among populations. Using coalescent models, which we upscaled to simulate the evolution of haplotypes with many markers at fixed distances, we show that the relationship between block boundaries and historic recombination intensity may be surprisingly weak. The majority of historic recombinations do not leave a footprint in present-day linkage disequilibrium patterns, and the block structure is sensitive to factors that affect the timing of recombination relative to marker mutation events in the genealogy, such as marker frequency bias and historic population size changes. Our results give insight into the potential of stochastic events to affect haplotype block structure, which can limit the usefulness of the block structure to mapping studies.     

Mutations as missing data: inferences on the ages and distributions of nonsynonymous and synonymous mutations

This article describes a new Markov chain Monte Carlo (MCMC) method applicable to DNA sequence data, which treats mutations in the genealogy as missing data. The method facilitates inferences regarding the age and identity of specific mutations while taking the full complexities of the mutational process in DNA sequences into account. We demonstrate the utility of the method in three applications. First, we demonstrate how the method can be used to make inferences regarding population genetical parameters such as theta (the effective population size times the mutation rate). Second, we show how the method can be used to estimate the ages of mutations in finite sites models and for making inferences regarding the distribution and ages of nonsynonymous and synonymous mutations. The method is applied to two previously published data sets and we demonstrate that in one of the data sets the average age of nonsynonymous mutations is significantly lower than the average age of synonymous mutations, suggesting the presence of slightly deleterious mutations. Third, we demonstrate how the method in general can be used to evaluate the posterior distribution of a function of a mapping of mutations on a gene genealogy. This application is useful for evaluating the uncertainty associated with methods that rely on mapping mutations on a phylogeny or a gene genealogy.     

Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm

Meiotic recombination and de novo mutation are the two main contributions toward gamete genome diversity, and many questions remain about how an individual human's genome is edited by these two processes. Here, we describe a high-throughput method for single-cell whole-genome analysis that was used to measure the genomic diversity in one individual's gamete genomes. A microfluidic system was used for highly parallel sample processing and to minimize nonspecific amplification. High-density genotyping results from 91 single cells were used to create a personal recombination map, which was consistent with population-wide data at low resolution but revealed significant differences from pedigree data at higher resolution. We used the data to test for meiotic drive and found evidence for gene conversion. High-throughput sequencing on 31 single cells was used to measure the frequency of large-scale genome instability, and deeper sequencing of eight single cells revealed de novo mutation rates with distinct characteristics.     

Estimating recombinational parameters in Streptococcus pneumoniae from multilocus sequence typing data

Multilocus sequence typing (MLST) is a highly discriminatory molecular typing method that defines isolates of bacterial pathogens using the sequences of approximately 450-bp internal fragments of seven housekeeping genes. This technique has been applied to 575 isolates of Streptococcus pneumoniae and identifies a number of discrete clonal complexes. These clonal complexes are typically represented by a single group of isolates sharing identical alleles at all seven loci, plus single-locus variants that differ from this group at only one out of the seven loci. As MLST is highly discriminatory, the members of each clonal complex can be assumed to have a recent common ancestor, and the molecular events that give rise to the single-locus variants can be used to estimate the relative contributions of recombination and mutation to clonal divergence. By comparing the sequences of the variant alleles within each clonal complex with the allele typically found within that clonal complex, we estimate that recombination has generated new alleles at a frequency approximately 10-fold higher than mutation, and that a single nucleotide site is approximately 50 times more likely to change through recombination than mutation. We also demonstrate how to estimate the average length of recombinational replacements from MLST data.     

Complex recombination events at the hypermutable minisatellite CEB1 (D2S90).

Some minisatellite structures are the site of high rates of DNA recombination in non-pathological situations, with an excess of motif insertion events and a locus-dependent sex-specific mutation bias. We previously reported the cloning of the hypermutable minisatellite locus CEB1 (D2S90), remarkable for its 13% mutation rate in the male germline (compared to approximately 0.4% in female). We have sought to analyse the mechanisms underlying the addition or deletion of motifs at this locus using the minisatellite variant repeat mapping technique. This is possible with a high precision due to the extreme sequence polymorphism seen between different motifs. No crossing-over event was observed among 38 informative neomutations. Four of the 19 informative mutant alleles with an addition of motifs are interallelic events, the others are intra-allelic. Overall, the insertion and deletion mutations are spread along the alleles, although the subset of interallelic events shows clustering towards the analysed end. The apparently complex recombination events observed can all be interpreted as a succession of elementary duplications-deletions of inter- as well as intra-chromosomal origin, suggesting a model in which sister chromatid as well as conversion-like exchanges are involved in these mutation processes.     

Revealing the hidden complexities of mtDNA inheritance

Mitochondrial DNA (mtDNA) is a pivotal tool in molecular ecology, evolutionary and population genetics. The power of mtDNA analyses derives from a relatively high mutation rate and the apparent simplicity of mitochondrial inheritance (maternal, without recombination), which has simplified modelling population history compared to the analysis of nuclear DNA. However, in biology things are seldom simple, and advances in DNA sequencing and polymorphism detection technology have documented a growing list of exceptions to the central tenets of mitochondrial inheritance, with paternal leakage, heteroplasmy and recombination now all documented in multiple systems. The presence of paternal leakage, recombination and heteroplasmy can have substantial impact on analyses based on mtDNA, affecting phylogenetic and population genetic analyses, estimates of the coalescent and the myriad of other parameters that are dependent on such estimates. Here, we review our understanding of mtDNA inheritance, discuss how recent findings mean that established ideas may need to be re‐evaluated, and we assess the implications of these new‐found complications for molecular ecologists who have relied for decades on the assumption of a simpler mode of inheritance. We show how it is possible to account for recombination and heteroplasmy in evolutionary and population analyses, but that accurate estimates of the frequencies of biparental inheritance and recombination are needed. We also suggest how nonclonal inheritance of mtDNA could be exploited, to increase the ways in which mtDNA can be used in analyses.     

Female site-specific transposase-induced recombination: a high-efficiency method for fine mapping mutations on the X chromosome in Drosophila

P-element transposons in the Drosophila germline mobilize only in the presence of the appropriate transposase enzyme. Sometimes, instead of mobilizing completely, P elements will undergo site-specific recombination with the homologous chromosome. Site-specific recombination is the basis for male recombination mapping, since the male germline does not normally undergo recombination. Site-specific recombination also takes place in females, but this has been difficult to study because of the obscuring effects of meiotic recombination. Using map functions, I demonstrate that it is possible to employ female site-specific transposase-induced recombination (FaSSTIR) to map loci on the X chromosome and predict that FaSSTIR mapping should be more efficient than meiotic mapping over short genetic intervals. Both FaSSTIR mapping and meiotic mapping were used to fine map the crossveinless locus on the X chromosome. Both techniques identified the same 10-kb interval as the probable location of the crossveinless mutation. Over short intervals (< approximately 7.6 cM), FaSSTIR produces more informative recombination events than does meiotic recombination. Over longer intervals, FaSSTIR is not always more efficient than meiotic mapping, but it produces the correct gene order. FaSSTIR matches the expectations suggested by the map functions and promises to be a useful technique, particularly for mapping X-linked loci.     

Constructing minimal ancestral recombination graphs.

By viewing the ancestral recombination graph as defining a sequence of trees, we show how possible evolutionary histories consistent with given data can be constructed using the minimum number of recombination events. In contrast to previously known methods, which yield only estimated lower bounds, our method of detecting recombination always gives the minimum number of recombination events if the right kind of rooted trees are used in our algorithm. A new lower bound can be defined if rooted trees with fewer constraints are used. As well as studying how often it actually is equal to the minimum, we test how this new lower bound performs in comparison to some other lower bounds. Our study indicates that the new lower bound is an improvement on earlier bounds. Also, using simulated data, we investigate how well our method can recover the actual site-specific evolutionary relationships. In the presence of recombination, using a single tree to describe the evolution of the entire locus clearly leads to lower average recovery percentages than does our method. Our study shows that recovering the actual local tree topologies can be done more accurately than estimating the actual number of recombination events.     

A coalescent-based method for detecting and estimating recombination from gene sequences

Determining the amount of recombination in the genealogical history of a sample of genes is important to both evolutionary biology and medical population genetics. However, recurrent mutation can produce patterns of genetic diversity similar to those generated by recombination and can bias estimates of the population recombination rate. Hudson 2001 has suggested an approximate-likelihood method based on coalescent theory to estimate the population recombination rate, 4N(e)r, under an infinite-sites model of sequence evolution. Here we extend the method to the estimation of the recombination rate in genomes, such as those of many viruses and bacteria, where the rate of recurrent mutation is high. In addition, we develop a powerful permutation-based method for detecting recombination that is both more powerful than other permutation-based methods and robust to misspecification of the model of sequence evolution. We apply the method to sequence data from viruses, bacteria, and human mitochondrial DNA. The extremely high level of recombination detected in both HIV1 and HIV2 sequences demonstrates that recombination cannot be ignored in the analysis of viral population genetic data.     

Traffic Lines: New Tools for Genetic Analysis in Arabidopsis thaliana

Genetic analysis requires the ability to identify the genotypes of individuals in a segregating population. This task is straightforward if each genotype has a distinctive phenotype, but is difficult if these genotypes are phenotypically similar or identical. We show that Arabidopsis seeds homozygous or heterozygous for a mutation of interest can be identified in a segregating family by placing the mutation in trans to a chromosome carrying a pair of seed-expressed green and red fluorescent transgenes (a “traffic line”) that flank the mutation. Nonfluorescent seeds in the self-pollinated progeny of such a heterozygous plant are usually homozygous for the mutation, whereas seeds with intermediate green and red fluorescence are typically heterozygous for the mutation. This makes it possible to identify seedlings homozygous for mutations that lack an obvious seedling phenotype, and also facilitates the analysis of lethal or sterile mutations, which must be propagated in heterozygous condition. Traffic lines can also be used to identify progeny that have undergone recombination within a defined region of the genome, facilitating genetic mapping and the production of near-isogenic lines. We produced 488 transgenic lines containing single genome-mapped insertions of NAP:dsRED and NAP:eGFP in Columbia (330 lines) and Landsberg erecta (158 lines) and generated sets of traffic lines that span most regions of the Arabidopsis genome. We demonstrated the utility of these lines for identifying seeds of a specific genotype and for generating near-isogenic lines using mutations of WUSCHEL and SHOOTMERISTEMLESS. This new resource significantly decreases the effort and cost of genotyping segregating families and increases the efficiency of experiments that rely on the ability to detect recombination in a defined chromosomal segment.