Two complementary perspectives on inter-individual genetic distance

Tal, Omri

doi:10.1016/j.biosystems.2012.07.005

Cited by 14 publications

(23 citation statements)

References 43 publications

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“…Subsequent studies extended similar approaches to still larger numbers of markers (Ramachandran et al 2005; Rosenberg et al 2005) and additional populations from different regions of the world (Rosenberg et al 2006; Wang et al 2007, 2008; Friedlaender et al 2008; Kopelman et al 2009; Tishkoff et al 2009; Pemberton et al 2012). The data sets from these studies have become widely used in numerous types of analyses (Barnholtz-Sloan et al 2005; Mountain and Ramakrishnan 2005; Amos 2006; Barbujani and Belle 2006; Handley et al 2007; Takezaki and Nei 2008; Romero et al 2009; Hunley and Healy 2011; Ramachandran and Rosenberg 2011; Rosenberg 2011), including tests of new statistical methods (Rosenberg et al 2003; Corander et al 2004; Pfaff et al 2004; Rosenberg 2005; Foll and Gaggiotti 2006; Francois et al 2006; Patterson et al 2006; Cercueil et al 2007; Szpiech et al 2008; DeGiorgio and Rosenberg 2009; Hubisz et al 2009; Shringarpure and Xing 2009; Jombart et al 2010; Fu et al 2011; Gao et al 2011) and evaluations of theoretical results (Rosenberg and Calabrese 2004; Rosenberg and Blum 2007; Rosenberg and Jakobsson 2008; Boca and Rosenberg 2011; DeGiorgio et al 2011; Szpiech and Rosenberg 2011; Reddy and Rosenberg 2012; Tal 2012; Jakobsson et al 2013). They have provided insights into such topics as the worldwide spread of anatomically modern humans (Zhivotovsky et al 2003; Prugnolle et al 2005a; Ray et al 2005; Liu et al 2006; Schroeder et al 2007; DeGiorgio et al 2009; Deshpande et al 2009; Hunley et al 2009; Amos and Hoffman 2010; Ray et al 2010), the relationship of genetic and linguistic variation (Hunley et al 2008, 2012; Lewis 2010; Jay et al 2011; de Filippo et al 2012), and the mechanisms of microsatellite mutation itself (Amos et al 2008; Pemberton e...…”

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confidence: 99%

Population Structure in a Comprehensive Genomic Data Set on Human Microsatellite Variation

Pemberton

DeGiorgio

Rosenberg

2013

G3 Genes|Genomes|Genetics

154

244

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Over the past two decades, microsatellite genotypes have provided the data for landmark studies of human population-genetic variation. However, the various microsatellite data sets have been prepared with different procedures and sets of markers, so that it has been difficult to synthesize available data for a comprehensive analysis. Here, we combine eight human population-genetic data sets at the 645 microsatellite loci they share in common, accounting for procedural differences in the production of the different data sets, to assemble a single data set containing 5795 individuals from 267 worldwide populations. We perform a systematic analysis of genetic relatedness, detecting 240 intra-population and 92 inter-population pairs of previously unidentified close relatives and proposing standardized subsets of unrelated individuals for use in future studies. We then augment the human data with a data set of 84 chimpanzees at the 246 loci they share in common with the human samples. Multidimensional scaling and neighbor-joining analyses of these data sets offer new insights into the structure of human populations and enable a comparison of genetic variation patterns in chimpanzees with those in humans. Our combined data sets are the largest of their kind reported to date and provide a resource for use in human population-genetic studies.

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confidence: 99%

Population Structure in a Comprehensive Genomic Data Set on Human Microsatellite Variation

Pemberton

DeGiorgio

Rosenberg

2013

G3 Genes|Genomes|Genetics

154

244

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“…For diploid genotypes, it is commonly defined as 2 minus the number of shared alleles at each locus, averaged across loci [21][22]. For multiple loci genotypes we use a normalized (by the number of considered loci) version of ASD to simplify the analysis of means and variances of the ASD distribution, as in Tal (2013). Under the assumption of Hardy-Weinberg Equilibrium, allele frequencies fully determine per-locus genotype frequencies.…”

Section: A Model Of Pairwise Genetic Distances For Genotypes From Twomentioning

confidence: 99%

“…A simulation of SD T and SD S under a two panmictic population model demonstrating the divergent behavior of these two statistics with an increasing number of SNP loci. SNP frequencies are modeled on Beta distributions (as in [5]). A: with F ST =0.10.…”

Section: μW=e[w] μY=e[y] μZ=e[z]mentioning

confidence: 99%

“…FST can be expressed mathematically in terms of population diversities as FST=1-S/T, where S and T represent the heterozygosity in subpopulations and in the total population, respectively [4][5]. The validity of FST as a measure of differentiation has been brought into question, especially when gene diversity is high (e.g., in microsatellites), and various metrics, including G'ST [6] and Jost's D [7], have been proposed to address this inadequacy (though see [8] for a counter-perspective).…”

Section: Introductionmentioning

confidence: 99%

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On the Apportionment of Population Structure

Granot

Tal

Skorecki

2015

Preprint

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Measures of population differentiation, such as F ST , are traditionally derived from the partition of diversity within and between populations. However, the emergence of population clusters from multilocus analysis is a function of genetic structure (departures from panmixia) rather than of diversity. If the populations are close to panmixia, slight differences between the mean pairwise distance within and between populations (low F ST ) can manifest as strong separation between the populations, thus population clusters are often evident even when the vast majority of diversity is partitioned within populations rather than between them. For any given F ST value, clusters can be tighter (more panmictic) or looser (more stratified), and in this respect higher F ST does not always imply stronger differentiation. In this study we propose a measure for the partition of structure, denoted E ST , which is more consistent with results from clustering schemes. Crucially, our measure is based on a statistic of the data that is a good measure of internal structure, mimicking the information extracted by unsupervised clustering or dimensionality reduction schemes. To assess the utility of our metric, we ranked various human (HGDP) population pairs based on F ST and E ST and found substantial differences in ranking order. E ST ranking seems more consistent with population clustering and classification and possibly with geographic distance between populations. Thus, E ST may at times outperform F ST in identifying evolutionary significant differentiation.

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“…There exist various methods to quantify individual relatedness using genomic sequences (e.g., see Wang () for a nonexhaustive list). The simplest of these calculate genomewide averages across a panel of single‐SNP haploid or diploid genetic distances (Tal, ) or allele‐sharing coefficients (Pemberton, Wang, Li, & Rosenberg, ; Speed & Balding, ). More advanced methods identify IBD regions as shared haplotype segments within densely spaced, preferably unlinked genomic markers, and then infer relationships from the total proportion of IBD (Browning & Browning, , ; Gusev et al., ; Hill & White, ; Kong et al., ; Purcell et al., ).…”

Section: Introductionmentioning

confidence: 99%

Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations

et al. 2017

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We develop and evaluate methods for inferring relatedness among individuals from low-coverage DNA sequences of their genomes, with particular emphasis on sequences obtained from fossil remains. We suggest the major factors complicating the determination of relatedness among ancient individuals are sequencing depth, the number of overlapping sites, the sequencing error rate and the presence of contamination from present-day genetic sources. We develop a theoretical model that facilitates the exploration of these factors and their relative effects, via measurement of pairwise genetic distances, without calling genotypes, and determine the power to infer relatedness under various scenarios of varying sequencing depth, present-day contamination and sequencing error. The model is validated by a simulation study as well as the analysis of aligned sequences from present-day human genomes. We then apply the method to the recently published genome sequences of ancient Europeans, developing a statistical treatment to determine confidence in assigned relatedness that is, in some cases, more precise than previously reported. As the majority of ancient specimens are from animals, this method would be applicable to investigate kinship in nonhuman remains. The developed software grups (Genetic Relatedness Using Pedigree Simulations) is implemented in Python and freely available.

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Two complementary perspectives on inter-individual genetic distance

Cited by 14 publications

References 43 publications

Population Structure in a Comprehensive Genomic Data Set on Human Microsatellite Variation

Population Structure in a Comprehensive Genomic Data Set on Human Microsatellite Variation

On the Apportionment of Population Structure

Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations

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