scholarly journals Population Differentiation as an Indicator of Recent Positive Selection in Humans: An Empirical Evaluation

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1065-1077 ◽  
Author(s):  
Yali Xue ◽  
Xuelong Zhang ◽  
Ni Huang ◽  
Allan Daly ◽  
Christopher J. Gillson ◽  
...  
Keyword(s):  
Positive Selection ◽  
Population Differentiation ◽  
Empirical Evaluation ◽  
High Population ◽  
Data Sets ◽  
Extended Haplotype ◽  
Frequency Spectra ◽  
Significance Threshold ◽  
Recent Positive Selection ◽  
Extended Haplotypes

We have evaluated the extent to which SNPs identified by genomewide surveys as showing unusually high levels of population differentiation in humans have experienced recent positive selection, starting from a set of 32 nonsynonymous SNPs in 27 genes highlighted by the HapMap1 project. These SNPs were genotyped again in the HapMap samples and in the Human Genome Diversity Project–Centre d'Etude du Polymorphisme Humain (HGDP–CEPH) panel of 52 populations representing worldwide diversity; extended haplotype homozygosity was investigated around all of them, and full resequence data were examined for 9 genes (5 from public sources and 4 from new data sets). For 7 of the genes, genotyping errors were responsible for an artifactual signal of high population differentiation and for 2, the population differentiation did not exceed our significance threshold. For the 18 genes with confirmed high population differentiation, 3 showed evidence of positive selection as measured by unusually extended haplotypes within a population, and 7 more did in between-population analyses. The 9 genes with resequence data included 7 with high population differentiation, and 5 showed evidence of positive selection on the haplotype carrying the nonsynonymous SNP from skewed allele frequency spectra; in addition, 2 showed evidence of positive selection on unrelated haplotypes. Thus, in humans, high population differentiation is (apart from technical artifacts) an effective way of enriching for recently selected genes, but is not an infallible pointer to recent positive selection supported by other lines of evidence.

scholarly journals Examination of signatures of recent positive selection on genes involved in human sialic acid biology

2017 ◽  
Author(s):  
Jiyun M. Moon ◽  
David M. Aronoff ◽  
John A. Capra ◽  
Patrick Abbot ◽  
Antonis Rokas
Keyword(s):  
Sialic Acid ◽  
Positive Selection ◽  
Transfer Functions ◽  
Demographic History ◽  
Neutral Evolution ◽  
Human Lineage ◽  
Selective Sweeps ◽  
Frequency Spectra ◽  
Recent Positive Selection ◽  
Soft Selective Sweeps

AbstractSialic acids are nine carbon sugars ubiquitously found on the surfaces of vertebrate cells and are involved in various immune response-related processes. In humans, at least 58 genes spanning diverse functions, from biosynthesis and activation to recycling and degradation, are involved in sialic acid biology. Because of their role in immunity, sialic acid biology genes have been hypothesized to exhibit elevated rates of evolutionary change. Consistent with this hypothesis, several genes involved in sialic acid biology have experienced higher rates of non-synonymous substitutions in the human lineage than their counterparts in other great apes, perhaps in response to ancient pathogens that infected hominins millions of years ago (paleopathogens). To test whether sialic acid biology genes have also experienced more recent positive selection during the evolution of the modern human lineage, reflecting adaptation to contemporary cosmopolitan or geographically-restricted pathogens, we examined whether their protein-coding regions showed evidence of recent hard and soft selective sweeps. This examination involved the calculation of four measures that quantify changes in allele frequency spectra, extent of population differentiation, and haplotype homozygosity caused by recent hard and soft selective sweeps for 55 sialic acid biology genes using publicly available whole genome sequencing data from 1,668 humans from three ethnic groups. To disentangle evidence for selection from confounding demographic effects, we compared the observed patterns in sialic acid biology genes to simulated sequences of the same length under a model of neutral evolution that takes into account human demographic history. We found that the patterns of genetic variation of most sialic acid biology genes did not significantly deviate from neutral expectations and were not significantly different among genes belonging to different functional categories. Those few sialic acid biology genes that significantly deviated from neutrality either experienced soft sweeps or population-specific hard sweeps. Interestingly, while most hard sweeps occurred on genes involved in sialic acid recognition, most soft sweeps involved genes associated with recycling, degradation and activation, transport, and transfer functions. We propose that the lack of signatures of recent positive selection for the majority of the sialic acid biology genes is consistent with the view that these genes regulate immune responses against ancient rather than contemporary cosmopolitan or geographically restricted pathogens.

scholarly journals A Hidden Markov Model for Investigating Recent Positive Selection through Haplotype Structure

2014 ◽  
Author(s):  
Hua Chen ◽  
Jody Hey ◽  
Montgomery Slatkin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Positive Selection ◽  
Genetic Model ◽  
Hidden Markov ◽  
Large Data ◽  
Data Sets ◽  
Recent Positive Selection ◽  
A Genome ◽  
Wide Range

Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients.

scholarly journals Codon-Substitution Models for Heterogeneous Selection Pressure at Amino Acid Sites

Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 431-449 ◽  
Author(s):  
Ziheng Yang ◽  
Rasmus Nielsen ◽  
Nick Goldman ◽  
Anne-Mette Krabbe Pedersen
Keyword(s):  
Amino Acid ◽  
Positive Selection ◽  
Selective Pressure ◽  
Acid Sites ◽  
Data Sets ◽  
Protein Coding ◽  
Important Indicator ◽  
Diversifying Selection ◽  
Codon Substitution ◽  
Neutral Mutations

AbstractComparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio (ω = dN/dS) is an important indicator of selective pressure at the protein level, with ω = 1 meaning neutral mutations, ω < 1 purifying selection, and ω > 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying ω ratios. We develop models that account for heterogeneous ω ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of ω among sites. In all data sets analyzed, the selective pressure indicated by the ω ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average ω ratio across sites is <1, but in which some sites are clearly under diversifying selection with ω > 1. Genes undergoing positive selection include the β-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for ω and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of ω among sites from real data sets.

scholarly journals Evidence for Very Recent Positive Selection in Mongolians

2017 ◽  
Vol 34 (8) ◽  
pp. 1936-1946 ◽  
Author(s):  
Kazuhiro Nakayama ◽  
Jun Ohashi ◽  
Kazuhisa Watanabe ◽  
Lkagvasuren Munkhtulga ◽  
Sadahiko Iwamoto
Keyword(s):  
Positive Selection ◽  
Recent Positive Selection

scholarly journals High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

2018 ◽  
Author(s):  
Pier Francesco Palamara ◽  
Jonathan Terhorst ◽  
Yun S. Song ◽  
Alkes L. Price
Keyword(s):  
Positive Selection ◽  
Snp Array ◽  
Complex Trait ◽  
Joint Analysis ◽  
Background Selection ◽  
Sequencing Data ◽  
Data Set ◽  
Coalescence Time ◽  
Recent Positive Selection ◽  
Coalescence Times

AbstractInterest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. We developed a new method, ASMC, that can estimate coalescence times using only SNP array data, and is 2-4 orders of magnitude faster than previous methods when sequencing data are available. We were thus able to apply ASMC to 113,851 phased British samples from the UK Biobank, aiming to detect recent positive selection by identifying loci with unusually high density of very recent coalescence times. We detected 12 genome-wide significant signals, including 6 loci with previous evidence of positive selection and 6 novel loci, consistent with coalescent simulations showing that our approach is well-powered to detect recent positive selection. We also applied ASMC to sequencing data from 498 Dutch individuals (Genome of the Netherlands data set) to detect background selection at deeper time scales. We observed highly significant correlations between average coalescence time inferred by ASMC and other measures of background selection. We investigated whether this signal translated into an enrichment in disease and complex trait heritability by analyzing summary association statistics from 20 independent diseases and complex traits (average N=86k) using stratified LD score regression. Our background selection annotation based on average coalescence time was strongly enriched for heritability (p = 7×10−153) in a joint analysis conditioned on a broad set of functional annotations (including other background selection annotations), meta-analyzed across traits; SNPs in the top 20% of our annotation were 3.8x enriched for heritability compared to the bottom 20%. These results underscore the widespread effects of background selection on disease and complex trait heritability.

scholarly journals Genomic evidence for recent positive selection at the human MDR1 gene locus

2004 ◽  
Vol 13 (8) ◽  
pp. 783-797 ◽  
Author(s):  
Kun Tang ◽  
Li Peng Wong ◽  
Edmund J.D. Lee ◽  
Samuel S. Chong ◽  
Caroline G.L. Lee
Keyword(s):  
Positive Selection ◽  
Gene Locus ◽  
Mdr1 Gene ◽  
Recent Positive Selection ◽  
Human Mdr1

SKT

2021 ◽  
Vol 14 (11) ◽  
pp. 2369-2382
Author(s):  
Monica Chiosa ◽  
Thomas B. Preußer ◽  
Gustavo Alonso
Keyword(s):  
Frequency Distribution ◽  
Empirical Evaluation ◽  
Large Data ◽  
Cloud Service ◽  
Data Sets ◽  
Data Set ◽  
Single Pass ◽  
Trade Offs ◽  
Significant Performance ◽  
Spatial Architecture

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically extracted by using sketch algorithms, now widely employed to process very large data sets in manageable space and in a single pass over the data. Often, analysts need more than one parameter to characterize the stream. However, computing multiple sketches becomes expensive even when using high-end CPUs. Exploiting the increasing adoption of hardware accelerators, this paper proposes SKT , an FPGA-based accelerator that can compute several sketches along with basic statistics (average, max, min, etc.) in a single pass over the data. SKT has been designed to characterize a data set by calculating its cardinality, its second frequency moment, and its frequency distribution. The design processes data streams coming either from PCIe or TCP/IP, and it is built to fit emerging cloud service architectures, such as Microsoft's Catapult or Amazon's AQUA. The paper explores the trade-offs of designing sketch algorithms on a spatial architecture and how to combine several sketch algorithms into a single design. The empirical evaluation shows how SKT on an FPGA offers a significant performance gain over high-end, server-class CPUs.

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