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Search Results: 1 - 10 of 17845 matches for " Mark Stoneking "
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A new approach for detecting low-level mutations in next-generation sequence data
Mingkun Li, Mark Stoneking
Genome Biology , 2012, DOI: 10.1186/gb-2012-13-5-r34
Abstract: Next-generation sequencing (NGS) is now widely used in biological and medical studies. Most re-sequencing studies have the goal of identifying homozygous or heterozygous mutations in diploid genomes (that is, mutations present at 50% or 100% frequency in sequence reads), and use this information to study genome evolution, infer population history, or identify causal genes/mutations in disease-association studies [1,2]. However, some applications require the identification of low-level mutations (LLMs) that are present at frequencies well below 50% within the population of molecules that is typically sequenced in an NGS study; examples include heteroplasmic mutations in mitochondrial DNA (mtDNA) genomes [3], somatic mutations in tumors [4], or mutations in pooled DNA samples [5].Challenges in detecting true LLMs come from sequencing error, library contamination, PCR artifacts, and so on. Sequencing error is the most common problem; for instance, the Illumina Genome Analyzer, which is one of the most popular NGS platforms, has an average error rate of 0.01 [6]. Moreover, sequencing error is unevenly distributed along the genome and may be influenced by the sequence context, position on the read, and molecule structure, resulting in sequencing error 'hot spots' where the error rate can be ten-fold greater (or more) than the genome average [3,7-10]. Unfortunately, those features resulting in sequencing error hot spots have not been fully characterized, thus making it difficult to distinguish sequencing errors from true LLMs [10].Detecting 'true' mutations involves genotype estimation (that is, the mutation frequency is expected to be 0%, 50%, or 100% for diploid data), and methods exist to provide accurate inference at a coverage of around 20× [2,11]. By contrast, even though much higher sequencing depth is typically obtained for NGS studies designed to detect LLMs (often ≥1,000×), the challenge remains to distinguish LLMs from sequencing errors [12]. Recently, several
Authors' Reply
Mark Stoneking,Brigitte Pakendorf,Hiroki Oota
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0030270
Abstract:
A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome
Kun Tang,Kevin R. Thornton,Mark Stoneking
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0050171
Abstract: Genome-wide scanning for signals of recent positive selection is essential for a comprehensive and systematic understanding of human adaptation. Here, we present a genomic survey of recent local selective sweeps, especially aimed at those nearly or recently completed. A novel approach was developed for such signals, based on contrasting the extended haplotype homozygosity (EHH) profiles between populations. We applied this method to the genome single nucleotide polymorphism (SNP) data of both the International HapMap Project and Perlegen Sciences, and detected widespread signals of recent local selection across the genome, consisting of both complete and partial sweeps. A challenging problem of genomic scans of recent positive selection is to clearly distinguish selection from neutral effects, given the high sensitivity of the test statistics to departures from neutral demographic assumptions and the lack of a single, accurate neutral model of human history. We therefore developed a new procedure that is robust across a wide range of demographic and ascertainment models, one that indicates that certain portions of the genome clearly depart from neutrality. Simulations of positive selection showed that our tests have high power towards strong selection sweeps that have undergone fixation. Gene ontology analysis of the candidate regions revealed several new functional groups that might help explain some important interpopulation differences in phenotypic traits.
An assessment of the portability of ancestry informative markers between human populations
Sean Myles, Mark Stoneking, Nic Timpson
BMC Medical Genomics , 2009, DOI: 10.1186/1755-8794-2-45
Abstract: We genotyped 10 BritAIMs in ~1000 individuals from 53 populations worldwide. We assessed the degree to which these 10 BritAIMs capture population stratification in other groups of populations by use of the Fst statistic. We used Fst values from 2750 random markers typed in the same set of individuals as an empirical distribution to which the Fst values of the 10 BritAIMs were compared.Allele frequency differences between continental groups for the BritAIMs are not unusually high. This is also the case for comparisons within continental groups distantly related to Britain. However, two BritAIMs show high Fst between European populations and two BritAIMs show high Fst between populations from the Middle East. Overall the median Fst across all BritAIMs is not unusually high compared to the empirical distribution.We find that BritAIMs are generally not useful to distinguish between continental groups or within continental groups distantly related to Britain. Moreover, our analyses suggest that the portability of AIMs across geographical scales (e.g. between Europe and Britain) can be limited and should therefore be taken into consideration in the design and interpretation of genetic association studies.Whole-genome association studies (GWASs) have proven extraordinarily successful in mapping loci that associate with common complex human diseases [for reviews see [1,2]]. Whereas candidate gene and linkage analyses have identified a few dozen replicable associations between genetic markers and complex diseases [3], GWASs have provided compelling evidence for more than 150 gene-disease associations since their introduction in 2006 [1]. The presence of population stratification has presented one of the main statistical challenges in GWASs. Population stratification refers to differences in allele frequencies between cases and controls related to ancestry rather than disease status. Long before technologies for GWASs were available, it was recognized that differences in ance
Authors' reply.
Stoneking Mark,Pakendorf Brigitte,Oota Hiroki
PLOS Biology , 2005,
Abstract:
A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome
Kun Tang ,Kevin R Thornton,Mark Stoneking
PLOS Biology , 2007, DOI: 10.1371/journal.pbio.0050171
Abstract: Genome-wide scanning for signals of recent positive selection is essential for a comprehensive and systematic understanding of human adaptation. Here, we present a genomic survey of recent local selective sweeps, especially aimed at those nearly or recently completed. A novel approach was developed for such signals, based on contrasting the extended haplotype homozygosity (EHH) profiles between populations. We applied this method to the genome single nucleotide polymorphism (SNP) data of both the International HapMap Project and Perlegen Sciences, and detected widespread signals of recent local selection across the genome, consisting of both complete and partial sweeps. A challenging problem of genomic scans of recent positive selection is to clearly distinguish selection from neutral effects, given the high sensitivity of the test statistics to departures from neutral demographic assumptions and the lack of a single, accurate neutral model of human history. We therefore developed a new procedure that is robust across a wide range of demographic and ascertainment models, one that indicates that certain portions of the genome clearly depart from neutrality. Simulations of positive selection showed that our tests have high power towards strong selection sweeps that have undergone fixation. Gene ontology analysis of the candidate regions revealed several new functional groups that might help explain some important interpopulation differences in phenotypic traits.
Authors' Reply
Mark Stoneking,Brigitte Pakendorf,Hiroki Oota
PLOS Biology , 2005, DOI: 10.1371/journal.pbio.0030270
Abstract:
Dating the age of admixture via wavelet transform analysis of genome-wide data
Irina Pugach, Rostislav Matveyev, Andreas Wollstein, Manfred Kayser, Mark Stoneking
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-2-r19
Abstract: An admixed population arises when individuals from two or more distinct populations start exchanging genetic material. Studying admixed populations can be particularly useful for understanding differences in disease prevalence and drug response among different populations. There is ample evidence that human populations have different susceptibility to diseases, exhibiting substantial variation in risk allele frequencies [1]. For example, genetic predisposition to asthma differs among the differentially-admixed Hispanic populations of the United States, with the highest prevalence observed in Puerto Ricans. Genetic variants responsible for the increased asthma prevalence in this population were localized using an admixture mapping approach [2]. This method allows the identification of disease causing variants by estimating ancestry along the genome, and narrowing the search to the genomic regions with ancestry from a population that has a greater risk for the disease [3,4]. The same approach was used to identify genetic loci that influence susceptibility to obesity, which is about 1.5-fold more prevalent in African-Americans than in European-Americans [5].Admixed populations are also of interest to population geneticists as they offer invaluable insights into the impact of various human migrations. For example, Polynesian populations are of dual Melanesian and Austronesian ancestry, with more maternal Austronesian and paternal Melanesian ancestry, highlighting the importance of sex-specific processes in human migrations [6]. The analysis of the pattern of sharing of chromosomal regions between populations has provided important insights into human colonization history including multiple migration waves into the Americas, and a complex movement of people across Europe [7]. A study of admixture patterns in Indian populations revealed that most Indians today trace their ancestry to two ancient, genetically-divergent populations [8].Analyses of admixture patterns in huma
Molecular evolution of UCP1 and the evolutionary history of mammalian non-shivering thermogenesis
David A Hughes, Martin Jastroch, Mark Stoneking, Martin Klingenspor
BMC Evolutionary Biology , 2009, DOI: 10.1186/1471-2148-9-4
Abstract: Models of adaptive evolution through phylogenetic analysis of amino acid sequences by maximum likelihood were implemented to determine the mode of UCP1 protein evolution in Eutherians. An increase in the rate of amino acid substitutions on the branch leading to Eutherians is observed, but is best explained by relaxed constraints, not positive selection. Further, evidence for branch and site heterogeneity in selection pressures, as well as divergent selection pressures between UCP1 and its paralogs (UCP2-3) is observed.We propose that the unique thermogenic function of UCP1 in Eutherians may be best explained by neutral processes. Along with other evidence, this suggests that the primary biochemical properties of UCP1 may not differ between Eutherians and non-Eutherians.Uncoupling protein 1 (UCP1) is a mitochondrial protein carrier which, until recently, was thought to be found only in endothermic placental (Eutherian) mammals [1,2]. In Eutherians, UCP1 is the only gene known to be exclusively expressed in brown adipose tissue (BAT), accounting for up to 5% of the total mitochondrial protein in BAT [3]; UCP1 (also known as thermogenin) provides Eutherians, particularly small mammals, hibernators and newborns, with a unique mechanism of non-shivering thermogenesis (NST) [4]. UCP1-dependent NST is probably a feature of most Eutherian mammals, as it has been found recently in the rock elephant shrew, a member of the Afrotherian mammalian lineage which separated early during the evolution of the Eutherians [5]. NST is produced by increasing the proton conductance in the inner membrane of brown adipocyte mitochondria. This increased proton conductance uncouples mitochondrial respiration from ATP synthesis and thereby dissipates the proton motive force as heat [6-9]. It is the high oxidative capacity of mitochondria in BAT and the cellular composition of BAT that allows heat dissipation rates at a power of 300 – 400 W/kg [10-12]. It is these properties of BAT, their mitoch
Worldwide population differentiation at disease-associated SNPs
Sean Myles, Dan Davison, Jeffrey Barrett, Mark Stoneking, Nic Timpson
BMC Medical Genomics , 2008, DOI: 10.1186/1755-8794-1-22
Abstract: We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals.On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations.Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.A broadly accepted model for the genetic architecture of complex disease is the common disease – common variant (CDCV) hypothesis. This hypothesis proposes that risk alleles for common complex diseases should be common (i.e. ≥ 5%) and thus are likely old and found in multiple human populations, rather than being population specific [1-4]. From analyses of genome-wide polymorphism data from populations of African, Asian and European ancestry, it has been shown that common alleles in one population are frequently bo
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