Genomic boundaries between human populations

Different authors disagree on whether human genome variation should be described as continuous or discontinuous; in the latter case, by attributing an individual's genotype to one genetic cluster, one would also obtain information on the individual's genome in general. An analysis of 377 microsatellites of the CEPH human diversity panel was interpreted as evidence that most genotypes cluster into one of five distinct groups, approximately corresponding to continents, which were pro- posed by some authors as the major biological subdivisions of humankind. Here we analyse the same dataset by a specific numerical method, designed to detect genomic boundaries, i.e. zones of increased change in maps of genomic variation. We show that statistically significant boundaries can be described between groups of populations, but different clusters are identified, depending on the assumptions of the model. In addition, these clusters do not correspond to the clusters inferred from previous analyses of the same or of other polymorphisms. We conclude that it is indeed possible to cluster genotypes according to geography, but no study so far identified unambiguously anything that can be regarded as a major genetic subdivision of humankind, and hence discontinuous models of human diversity are unsupported by data.     

新型冠状病毒基因组序列的网络平台与基因分型

新型冠状病毒(SARS-CoV-2)是引起2019新型冠状病毒肺炎(COVID-19)的病原体,目前COVID-19仍在世界范围内大规模流行。随着学者对SARS-CoV-2研究的不断深入,以及各大数据库的序列资源共享,一些学者开发了SARS-CoV-2相关序列分析网络在线平台,并发表了对SARS-CoV-2基因分型、命名的规则。"GISAID"是目前SARS-CoV-2基因组序列共享和储存最大的数据库,"Nextstrain"和"CoV-GLUE"是国际最常用的SARS-CoV-2序列分析平台。目前有四种比较通用的SARS-CoV-2的基因分型方法,在本文中分别简称为:"中国分型法"、"Pangolin分型法"、"GISAID分型法"和"Nextstrain分型法"。这四种分型方法的定义不尽相同,但又有相似之处。本综述对目前SARS-CoV-2在线分析网络平台和不同的基因分型方法进行了较为系统的介绍、对比和总结。     

Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

ABSTRACT: BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

Widespread Signals of Convergent Adaptation to High Altitude in Asia and America

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.     

Heterogeneity of primary glioblastoma cells in the expression of caspase-8 and the response to TRAIL-induced apoptosis

Recent studies suggest that cancer stem cells (CSCs) are responsible for cancer resistance to therapies. We therefore investigated how glioblastoma-derived CSCs respond to the treatment of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Neurospheres were generated from glioblastomas, characterized for CSC properties including self-renewal, cell differentiation and xenograft formation capacity, and analyzed for TRAIL-induced apoptosis, CASP8 genomic status, and caspase-8 protein expression. The neurosphere NSC326 was sensitive to TRAIL-induced apoptosis as evidenced by cell death and caspase-8, -3, and -7 enzymatic activities. In contrast, however, the neurosphere NSC189 was TRAIL-resistant. G-banding analysis identified five chromosomally distinguishable cell populations in the neurospheres. Fluorescence in situ hybridization revealed the variation of chromosome 2 copy number in these populations and the loss of CASP8 locus in 2q33-34 region in a small set of cell populations in the neurosphere. Immunohistochemistry of NSC189 cell blocks revealed the lack of caspase-8 protein in a subset of neurosphere cells. Western blotting and immunohistochemistry of human glioblastoma tumors demonstrated the expression of caspase-8 protein in the vast majority of the tumors as compared to normal human brain tissues that lack the caspase-8 expression. This study shows heterogeneity of glioblastomas and derived CSCs in the genomic status of CASP8, expression of caspase-8, and thus responsiveness to TRAIL-induced apoptosis. Clinic trials may consider genomic analysis of the cancer tissue to identify the genomic loss of CASP8 and use it as a genomic marker to predict the resistance of glioblastomas to TRAIL apoptosis pathway-targeted therapies.     

Variation in estimated recombination rates across human populations

Recently it has been reported that recombination hotspots appear to be highly variable between humans and chimpanzees, and there is evidence for between-person variability in hotspots, and evolutionary transience. To understand the nature of variation in human recombination rates, it is important to describe patterns of variability across populations. Direct measurement of recombination rates remains infeasible on a large scale, and population-genetic approaches can be imprecise, and are affected by demographic history. Reports to date have suggested broad similarity in recombination rates at large genomic scales and across human populations. Here, we examine recombination rate estimates at a finer population and genomic scale: 28 worldwide populations and 107 SNPs in a 1 Mb stretch of chromosome 22q. We employ analysis of variance of recombination rate estimates, corrected for differences in effective population size using genome-wide microsatellite mutation rate estimates. We find substantial variation in fine-scale rates between populations, but reduced variation within continental groups. All effects examined (SNP-pair, region, population and interactions) were highly significant. Adjustment for effective population size made little difference to the conclusions. Observed hotspots tended to be conserved across populations, albeit at varying intensities. This holds particularly for populations from the same region, and also to a considerable degree across geographical regions. However, some hotspots appear to be population-specific. Several results from studies on the population history of humans are in accordance with our analysis. Our results suggest that between-population variation in DNA sequences may underly recombination rate variation.     

Performance comparison of exome DNA sequencing technologies

Whole exome sequencing by high-throughput sequencing of target-enriched genomic DNA (exome-seq) has become common in basic and translational research as a means of interrogating the interpretable part of the human genome at relatively low cost. We present a comparison of three major commercial exome sequencing platforms from Agilent, Illumina and Nimblegen applied to the same human blood sample. Our results suggest that the Nimblegen platform, which is the only one to use high-density overlapping baits, covers fewer genomic regions than the other platforms but requires the least amount of sequencing to sensitively detect small variants. Agilent and Illumina are able to detect a greater total number of variants with additional sequencing. Illumina captures untranslated regions, which are not targeted by the Nimblegen and Agilent platforms. We also compare exome sequencing and whole genome sequencing (WGS) of the same sample, demonstrating that exome sequencing can detect additional small variants missed by WGS.     

A Simple Screen to Identify Promoters Conferring High Levels of Phenotypic Noise

Genetically identical populations of unicellular organisms often show marked variation in some phenotypic traits. To investigate the molecular causes and possible biological functions of this phenotypic noise, it would be useful to have a method to identify genes whose expression varies stochastically on a certain time scale. Here, we developed such a method and used it for identifying genes with high levels of phenotypic noise in Salmonella enterica ssp. I serovar Typhimurium (S. Typhimurium). We created a genomic plasmid library fused to a green fluorescent protein (GFP) reporter and subjected replicate populations harboring this library to fluctuating selection for GFP expression using fluorescent-activated cell sorting (FACS). After seven rounds of fluctuating selection, the populations were strongly enriched for promoters that showed a high amount of noise in gene expression. Our results indicate that the activity of some promoters of S. Typhimurium varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. The genomic fragments that conferred the highest levels of phenotypic variation were promoters controlling the synthesis of flagella, which are associated with virulence and host–pathogen interactions. This confirms earlier reports that phenotypic noise may play a role in pathogenesis and indicates that these promoters have among the highest levels of noise in the S. Typhimurium genome. This approach can be applied to many other bacterial and eukaryotic systems as a simple method for identifying genes with noisy expression.     

Deep-sequencing protocols influence the results obtained in small-RNA sequencing

Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.     

The impact of next-generation sequencing on genomics

This article reviews basic concepts,general applications,and the potential impact of next-generation sequencing(NGS)technologies on genomics,with particular reference to currently available and possible future platforms and bioinformatics.NGS technologies have demonstrated the capacity to sequence DNA at unprecedented speed,thereby enabling previously unimaginable scientific achievements and novel biological applications.But,the massive data produced by NGS also presents a significant challenge for data storage,analyses,and management solutions.Advanced bioinformatic tools are essential for the successful application of NGS technology.As evidenced throughout this review,NGS technologies will have a striking impact on genomic research and the entire biological field.With its ability to tackle the unsolved challenges unconquered by previous genomic technologies,NGS is likely to unravel the complexity of the human genome in terms of genetic variations,some of which may be confined to susceptible loci for some common human conditions.The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.     

Isozyme Variation among Biological Species in the Gibberella fujikuroi Species Complex (Fusarium Section Liseola)

Isozyme phenotypes were determined for 101 strains of Gibberella fujikuroi and 2 strains of Gibberella nygamai that represent seven biological species (mating populations) isolated from a variety of plant hosts in dispersed geographic locations. Fourteen enzymes were resolved in one or more of three buffer systems. Two of the enzymes, arylesterase and acid phosphatase, were polymorphic within two or more biological species and are suitable for intraspecific studies of population variation. Six enzymes, alcohol dehydrogenase, aspartate aminotransferase, glucose-6-phosphate dehydrogenase, mannitol dehydrogenase, phosphoglucomutase, and phosphogluconate dehydrogenase, were monomorphic in all of the isolates examined. The remaining six enzymes, fumarase, glucose phosphate isomerase, glutamate dehydrogenase (NADP), isocitrate dehydrogenase (NADP), malate dehydrogenase, and triose-phosphate isomerase, could potentially be used to distinguish the different biological species. Mating populations C and D are the most similar, since the mating population C isolates examined had the same isozyme phenotype as did a subset of the isolates in mating population D. Mating population E is the least similar to the other taxa examined. Unique isozyme phenotypes are present but are composed of banding patterns shared among the biological species. This finding supports the hypothesis that these biological species, with the possible exception of mating populations C and D, are reproductively isolated from one another and that no significant gene flow is occurring between them. Isozyme analysis is a useful method to distinguish these closely related biological species. Examination of isozyme phenotypes is more rapid than the present technique, which is based on sexual crosses; can be applied to strains that are not sexually fertile; and is more sensitive than traditional morphological characters, which cannot distinguish more than three or four morphological groups among the seven biological species. While emphasizing the discreteness of the mating populations as biological entities, our isozyme data also reaffirm the close genetic relationship among these groups.     

Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.     

Low levels of genetic divergence across geographically and linguistically diverse populations from India

Ongoing modernization in India has elevated the prevalence of many complex genetic diseases associated with a western lifestyle and diet to near-epidemic proportions. However, although India comprises more than one sixth of the world's human population, it has largely been omitted from genomic surveys that provide the backdrop for association studies of genetic disease. Here, by genotyping India-born individuals sampled in the United States, we carry out an extensive study of Indian genetic variation. We analyze 1,200 genome-wide polymorphisms in 432 individuals from 15 Indian populations. We find that populations from India, and populations from South Asia more generally, constitute one of the major human subgroups with increased similarity of genetic ancestry. However, only a relatively small amount of genetic differentiation exists among the Indian populations. Although caution is warranted due to the fact that United States–sampled Indian populations do not represent a random sample from India, these results suggest that the frequencies of many genetic variants are distinctive in India compared to other parts of the world and that the effects of population heterogeneity on the production of false positives in association studies may be smaller in Indians (and particularly in Indian-Americans) than might be expected for such a geographically and linguistically diverse subset of the human population.     

Consistent long-range linkage disequilibrium generated by admixture in a Bantu-Semitic hybrid population

Both the optimal marker density for genome scans in case-control association studies and the appropriate study design for the testing of candidate genes depend on the genomic pattern of linkage disequilibrium (LD). In this study, we provide the first conclusive demonstration that the diverse demographic histories of human populations have produced dramatic differences in genomewide patterns of LD. Using a panel of 66 markers spanning the X chromosome, we show that, in the Lemba, a Bantu-Semitic hybrid population, markers 2 cM. Moreover, analysis of Bantu and Ashkenazi populations as putative parental populations of the Lemba shows a significant relationship between allele-frequency differentials and the LD observed in the Lemba, which demonstrates that much of the excess LD is due to admixture. Our results suggest that demographic history has such a profound effect on LD that it will not be possible to predict patterns a priori but that it will be necessary to empirically evaluate the patterns in all populations of interest.     

一种快速高效提取病原真菌DNA作为PCR模板的方法

真菌rDNA-ITS序列分析适合于较高等级水平的生物群体间的系统分析。真菌DNA的提取采用传统的方法,步骤繁琐,需要较长时间。采用Chelex-100法提取真菌DNA,使用PCR扩增rDNA-ITS序列评价提取核酸的质量。结果显示,该方法具有经济、简便、快速、高效的特点,是一种比较理想的提取真菌基因组DNA作为PCR模板的方法。     

Retroviral integration profiles: their determinants and implications for gene therapy

Retroviruses have often been used for gene therapy because of their capacity for the long-term expression of transgenes via stable integration into the host genome. However, retroviral integration can also result in the transformation of normal cells into cancer cells, as demonstrated by the incidence of leukemia in a recent retroviral gene therapy trial in Europe. This unfortunate outcome has led to the rapid initiation of studies examining various biological and pathological aspects of retroviral integration. This review summarizes recent findings from these studies, including the global integration patterns of various types of retroviruses, viral and cellular determinants of integration, implications of integration for gene therapy and retrovirus-mediated infectious diseases, and strategies to shift integration to safe host genomic loci. A more comprehensive and mechanistic understanding of retroviral integration processes will eventually make it possible to generate safer retroviral vector platforms in the near future.