A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms

Sachidanandam, Ravi; Weissman, David B.; Schmidt, Steven C.; Kakol, Jerzy M.; Stein, Lincoln; Marth, Gábor; Sherry, Stephen T.; Mullikin, James C.; Mortimore, Beverley; Willey, David L.; Hunt, Sarah; Cole, Charlotte G.; Coggill, Penny; Rice, Catherine M.; Ning, Zemin; Rogers, Jane; Bentley, David; Kwok, Pui‐Yan; Mardis, Elaine R.; Yeh, Raymond T.; Schultz, Brian; Cook, Lisa L.; Davenport, R. John; Dante, Michael; Fulton, Lucinda L.; Hillier, LaDeana W.; Waterston, Robert H.; Mcpherson, John Douglas; Gilman, Brian; Schaffner, Stephen F.; Etten, William J. Van; Reich, David; Higgins, John M.; Daly, Mark J.; Blumenstiel, Brendan; Baldwin, Jennifer; Stange-Thomann, Nicole; Zody, Michael C.; Linton, Lauren; Lander, Eric S.; Altshuler, David

doi:10.1038/35057149

Cited by 2,575 publications

(433 citation statements)

References 42 publications

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“…- mutation rate: According to different studies Homo sapiens , as a species, has a mutation rate around 1.5 × 10 -8 per base pair per generation (bp/gen for short) [10]. However, this value could change along the genome.…”

Section: Introductionmentioning

confidence: 99%

Sum of parts is greater than the whole: inference of common genetic history of populations

Utro¹,

Pybus

Parida³

2013

BMC Genomics

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BackgroundReconstructability of population history, from genetic information of extant individuals, is studied under a simulation setting. We do not address the issue of accuracy of the reconstruction algorithms: we assume the availability of the theoretical best algorithm. On the other hand, we focus on the fraction (1 - f) of the common genetic history that is irreconstructible or impenetrable. Thus the fraction, f, gives an upper bound on the extent of estimability. In other words, there exists no method that can reconstruct a fraction larger than f of the entire common genetic history. For the realization of such a study, we first define a natural measure of the amount of genetic history. Next, we use a population simulator (from literature) that has at least two features. Firstly, it has the capability of providing samples from different demographies, to effectively reflect reality. Secondly, it also provides the underlying relevant genetic history, captured in its entirety, where such a measure is applicable. Finally, to compute f, we use an information content measure of the relevant genetic history. The simulator of choice provided the following demographies: Africans, Europeans, Asians and Afro-Americans.ResultsWe observe that higher the rate of recombination, lower the value of f, while f is invariant over varying mutation rates, in each of the demographies. The value of f increases with the number of samples, reaching a plateau and suggesting that in all the demographies at least about one-third of the relevant genetic history is impenetrable. The most surprising observation is that the the sum of the reconstructible history of the subsegments is indeed larger than the reconstructible history of the whole segment. In particular, longer the chromosomal segment, smaller the value of f, in all the demographies.ConclusionsWe present the very first framework for measuring the fraction of the relevant genetic history of a population that is mathematically elusive. Our observed results on the tested demographies suggest that it may be better to aggregate the analysis of smaller chunks of chromosomal segments than fewer large chunks. Also, no matter the richness of samples in a population, at least one-third of the population genetic history is impenetrable. The framework also opens up possible new lines of investigation along the following. Given the characteristics of a population, possibly derived from observed extant individuals, to estimate the (1) optimal sample size and (2) optimal sequence length for the most informative analysis.

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Section: Introductionmentioning

confidence: 99%

Sum of parts is greater than the whole: inference of common genetic history of populations

Utro¹,

Pybus

Parida³

2013

BMC Genomics

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“…SNPs are generally abundant, but their density differs considerably between different regions of a genome between genotypes in any species, and more so between species. For instance, Sachidanandam et al (2001) [49] reported that the average density of SNPs in the human genome was estimated at about 1 in 1000 bp but is considerably larger in some genomic areas such as the noncoding human leukocyte antigen (HLA) regions [50]. In plant species analysed so far, approximately one SNP was usually present per 200 to 500 bp with large differences between different plant species.…”

Section: Single Nucleotide Polymorphisms (Snps)mentioning

confidence: 99%

Molecular Markers for Genetic Diversity Studies in African Leafy Vegetables

Omondi¹,

Debener²,

Linde³

et al. 2016

ABB

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African leafy vegetables are becoming important crops in tackling nutrition and food security in many parts of sub-Saharan Africa, since they provide important micronutrients and vitamins, and help resource-poor farm families bridge lean periods of food shortage. Genetic diversity studies are essential for crop improvement programmes as well as germplasm conservation efforts, and research on genetic diversity of these vegetables using molecular markers has been increasing over time. Diversity studies have evolved from the use of morphological and biochemical markers to molecular markers. Molecular markers provide valuable data, since they detect mostly selectively neutral variations at the DNA level. They are well established and their strengths and limitations have been described. New marker types are being developed from a combination of the strengths of the basic techniques to improve sensitivity, reproducibility, polymorphic information content, speed and cost. This review discusses the principles of some of the established molecular markers and their application to genetic diversity studies of African leafy vegetables with a main focus on the most common Solanum, Amaranthus, Cleome and Vigna species.

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“…Over 99.9% of genome sequences are the same in all humans [1,2] , yet individuals show great distinction from each other.…”

Section: Transcriptome Profilingmentioning

confidence: 99%

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

Miecznikowski

Liu

Ren

2012

JST

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RNA-Seq is a recently developed technology for transcriptome profiling. Numerous advantages of RNA-Seq suggest that it will be the platform of choice for genome-wide expression studies. RNA-Seq generates large volumes of data which require statistical methods for data processing and accurate inference. This article reviews the RNA-Seq technologies followed by a detailed discussion of current statistical methods for normalization and differential expression analysis. Key wordsRNA-Seq, Normalization, Biological counts, Differential expression Transcriptome profilingOver 99.9% of genome sequences are the same in all humans [1,2] , yet individuals show great distinction from each other.In a multicellular organism nearly all cells contain the same genome, but they develop into different tissues. A major source for many of these variations is the different gene expression patterns [3] . In control of gene expression, the transcriptome is the complete set of ribonucleic acid (RNA) transcripts, including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and other non-coding RNA in a given cell type. It is the connection between genes and phenotype. The constitution of the transcriptome for an organism varies at different developmental stages or under different physiological conditions. As a quantitatively cataloged transcriptome provides us information on the underlying genetic mechanisms, the transcriptome is studied in relation to many diseases, e.g., cancers. One of the main goals in a cancer transcriptome study is quantifying the changes in expression levels of all the transcripts in tumor cells. In this manuscript, we discuss the cutting edge methods for quantifying the transcriptome and how the resulting data is used to determine significantly differentially expressed transcripts. MicroarraysMicroarrays have been the primary technology for quantitative transcriptome analysis since the mid-1990s [4] , and they have discovered many results in cancer research [5][6][7][8] . Although expression studies by microarrays have been very successful in the last decade, there are at least three intrinsic limitations to this hybridization-based technology. First, the background noise in microarrays is large due to cross-hybridization of closely related genes. Second, the microarray signal often reaches a limit of detection or saturation, therefore microarrays have a limited dynamic range (a few hundredfold) [9] .

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A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms

Cited by 2,575 publications

References 42 publications

Sum of parts is greater than the whole: inference of common genetic history of populations

Sum of parts is greater than the whole: inference of common genetic history of populations

Molecular Markers for Genetic Diversity Studies in African Leafy Vegetables

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

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