Biomedical subjects

Peter Donnelly

Publications and source records attributed to Peter Donnelly.

15 recordsLinked to original sources

PubMed2006

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.

Algorithms↗

PubMed2006

The influence of recombination on human genetic diversity.

In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale. Despite considerable debate, it is not clear whether these factors are causally linked or, if they are, whether this is driven by the repeated action of adaptive evolution or molecular processes such as double-strand break formation and mismatch repair. We introduce three innovations to the analysis of recombination and diversity: fine-scale genetic maps estimated from genotype experiments that identify recombination hotspots at the kilobase scale, analysis of an entire human chromosome, and the use of wavelet techniques to identify correlations acting at different scales. We show that recombination influences genetic diversity only at the level of recombination hotspots. Hotspots are also associated with local increases in GC content and the relative frequency of GC-increasing mutations but have no effect on substitution rates. Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition. To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations. However, that hotspots have no influence on substitution rates suggests that they are too ephemeral on an evolutionary time scale to have a strong influence on broader scale patterns of base composition and long-term molecular evolution.

Animals↗

PubMed2006

A comparison of phasing algorithms for trios and unrelated individuals.

Knowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million-SNP HapMap data set. Finally, we evaluated methods of estimating the value of r(2) between a pair of SNPs and concluded that all methods estimated r(2) well when the estimated value was >or=0.8.

Algorithms↗

PubMed2005

A fine-scale map of recombination rates and hotspots across the human genome.

Genetic maps, which document the way in which recombination rates vary over a genome, are an essential tool for many genetic analyses. We present a high-resolution genetic map of the human genome, based on statistical analyses of genetic variation data, and identify more than 25,000 recombination hotspots, together with motifs and sequence contexts that play a role in hotspot activity. Differences between the behavior of recombination rates over large (megabase) and small (kilobase) scales lead us to suggest a two-stage model for recombination in which hotspots are stochastic features, within a framework in which large-scale rates are constrained.

Animals↗

PubMed2005

Human recombination hot spots hidden in regions of strong marker association.

The fine-scale distribution of meiotic recombination events in the human genome can be inferred from patterns of haplotype diversity in human populations but directly studied only by high-resolution sperm typing. Both approaches indicate that crossovers are heavily clustered into narrow recombination hot spots. But our direct understanding of hot-spot properties and distributions is largely limited to sperm typing in the major histocompatibility complex (MHC). We now describe the analysis of an unremarkable 206-kb region on human chromosome 1, which identified localized regions of linkage disequilibrium breakdown that mark the locations of sperm crossover hot spots. The distribution, intensity and morphology of these hot spots are markedly similar to those in the MHC. But we also accidentally detected additional hot spots in regions of strong association. Coalescent analysis of genotype data detected most of the hot spots but showed significant differences between sperm crossover frequencies and historical recombination rates. This raises the possibility that some hot spots, particularly those in regions of strong association, may have evolved very recently and not left their full imprint on haplotype diversity. These results suggest that hot spots could be very abundant and possibly fluid features of the human genome.

Chromosomes, Human, Pair 1↗

PubMed2005

Genome-wide strategies for detecting multiple loci that influence complex diseases.

After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.

Alleles↗

PubMed2005

Patterns of Eurasian HSV-1 molecular diversity and inferences of human migrations.

Following our recent report of high levels of recombination and geographic structuring amongst isolates from two populations, we have investigated global patterns of herpes simplex virus type 1 (HSV-1) molecular diversity using population samples from six countries in Europe, Asia and Africa. Sequence comparisons show that HSV-1 from Kenya is both highly diverse and distinct from either European or Asian HSV-1. HSV-1 populations are much more highly differentiated than human populations at the same geographic scales, with 35% of total variation at the level of inter-population comparisons, a difference likely to be due to higher rates of both mutation and genetic drift in HSV-1 than in equivalent human data. There is substantial differentiation between northwestern European HSV-1 populations and those from East Asia, and while patterns of British and Swedish HSV-1 variation were indistinguishable, differentiation was detectable amongst Chinese, Korean and Japanese HSV-1 samples, in spite of their lower overall diversity. The program Structure was used to reconstruct ancestral Eurasian lineages, which we estimated to have originated approximately 60,000 years ago. A specific pattern detected amongst East Asian HSV-1 isolates is currently best explained by the two waves of migration responsible for the peopling of Japan.

Afghanistan↗

PubMed2005

Comparison of fine-scale recombination rates in humans and chimpanzees.

We compared fine-scale recombination rates at orthologous loci in humans and chimpanzees by analyzing polymorphism data in both species. Strong statistical evidence for hotspots of recombination was obtained in both species. Despite approximately 99% identity at the level of DNA sequence, however, recombination hotspots were found rarely (if at all) at the same positions in the two species, and no correlation was observed in estimates of fine-scale recombination rates. Thus, local patterns of recombination rate have evolved rapidly, in a manner disproportionate to the change in DNA sequence.

Alleles↗

PubMed2005

Software process improvement for the medical industry.

This chapter describes a software process improvement framework, structured to ensure regulatory compliance for the software developed in medical devices. Software is becoming an increasingly important aspect of medical devices and medical device regulation. Medical devices can only be marketed if compliance and approval from the appropriate regulatory bodies of the Food and Drug Administration (US requirement), and the European Commission under its Medical Device Directives (CE marking requirement) is achieved.

Device Approval↗

PubMed2004

The fine-scale structure of recombination rate variation in the human genome.

The nature and scale of recombination rate variation are largely unknown for most species. In humans, pedigree analysis has documented variation at the chromosomal level, and sperm studies have identified specific hotspots in which crossing-over events cluster. To address whether this picture is representative of the genome as a whole, we have developed and validated a method for estimating recombination rates from patterns of genetic variation. From extensive single-nucleotide polymorphism surveys in European and African populations, we find evidence for extreme local rate variation spanning four orders in magnitude, in which 50% of all recombination events take place in less than 10% of the sequence. We demonstrate that recombination hotspots are a ubiquitous feature of the human genome, occurring on average every 200 kilobases or less, but recombination occurs preferentially outside genes.

Base Composition↗

PubMed2004

The effects of human population structure on large genetic association studies.

Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.

Genetic Markers↗

PubMed2004

High recombination rate in herpes simplex virus type 1 natural populations suggests significant co-infection.

Herpes simplex virus type 1 (HSV-1) is highly prevalent in all human populations and has been presumed to evolve in a clonal manner because of a lack of evidence for significant levels of co-infection. Different HSV-1 populations have distinct distributions of strains and the long timescale evident from HSV-1 population diversity has led to the suggestion that studies of virus variability may yield information about host population history. In this sequencing study of three segments of the HSV-1 genome in population samples from the UK and Korea, evidence of recombination was widespread both at the level of reassortment between widely separated loci and within shorter contiguous sequences and the estimated rate of recombination was comparable to that of mutation. Since recombination requires the coexistence of two viral genomes, these results suggest that co-infection by genetically distinct strains may be a more important aspect of HSV-1 epidemiology than previously realized. With its capacity to make new combinations of variants available for selection, substantial recombination requires a radically revised model for the rate and mode of evolution of the virus.

Base Sequence↗

PubMed2004

Application of coalescent methods to reveal fine-scale rate variation and recombination hotspots.

There has been considerable recent interest in understanding the way in which recombination rates vary over small physical distances, and the extent of recombination hotspots, in various genomes. Here we adapt, apply, and assess the power of recently developed coalescent-based approaches to estimating recombination rates from sequence polymorphism data. We apply full-likelihood estimation to study rate variation in and around a well-characterized recombination hotspot in humans, in the beta-globin gene cluster, and show that it provides similar estimates, consistent with those from sperm studies, from two populations deliberately chosen to have different demographic and selectional histories. We also demonstrate how approximate-likelihood methods can be used to detect local recombination hotspots from genomic-scale SNP data. In a simulation study based on 80 100-kb regions, these methods detect 43 out of 60 hotspots (ranging from 1 to 2 kb in size), with only two false positives out of 2000 subregions that were tested for the presence of a hotspot. Our study suggests that new computational tools for sophisticated analysis of population diversity data are valuable for hotspot detection and fine-scale mapping of local recombination rates.

DNA↗

PubMed2003

Fatal hemorrhagic pneumonia caused by infection due to Kytococcus sedentarius--a pathogen or passenger?

A 55-year old man developed a hemorrhagic pneumonia, likely due to infection with Kytococcus sedentarius during neutropenia following induction chemotherapy for acute myeloid leukemia. Severe mucosal barrier injury and the selective pressure of broad-spectrum antibiotics probably made it possible for this normally harmless commensal to penetrate the gut, spread through the blood stream, and invade the lungs.

Actinomycetales↗

PubMed2003

A comparison of bayesian methods for haplotype reconstruction from population genotype data.

In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational strategies they employ. We introduce a new algorithm that combines the modeling strategy of one method with the computational strategies of another. In comparisons using real and simulated data, this new algorithm outperforms all three existing methods. The new algorithm is included in the software package PHASE, version 2.0, available online (http://www.stat.washington.edu/stephens/software.html).

Algorithms↗