Mathematical constraints onFST: multiallelic markers in arbitrarily many populations

Alcala, Nicolas; Rosenberg, Noah A.

doi:10.1098/rstb.2020.0414

Cited by 10 publications

(5 citation statements)

References 48 publications

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“…This is, however, a misleading interpretation: the repertoire of HLA alleles present in populations from different regions is in fact quite different (e.g. figure 5 ), and the reason why F ST is low is because within-population diversity is extremely high, constraining the maximum F ST value that can be reached [ 43 ]. Thus, in the case of HLA genes, equating ‘low F ST ’ to ‘shared diversity’ is inappropriate.…”

Section: Discussionmentioning

confidence: 99%

“…While this interpretation is in general appropriate for the analysis of predominantly biallelic SNP (figures 2 and 3 ), for highly polymorphic multi-allelic loci low F ST values can be found even when there are few (or even no) shared alleles among populations [ 41 , 42 ]. The analysis of Alcala and Rosenberg [ 43 ] highlights this, by mathematically exploring constraints on F ST , and demonstrating that high mutation rates decrease the frequency of the most frequent allele in a multi-allelic locus, and thus leads to lower F ST values. Although the mutation rate at individual sites within the MHC does not exceed genomewide averages, the high density of non-synonymous polymorphism results in an extremely large number of alleles present in our species (see box 1 ), an effect analogous to a high mutation rate at the allele level.…”

Section: How Is Human Leucocyte Antigen Diversity Apportioned?mentioning

confidence: 99%

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How HLA diversity is apportioned: influence of selection and relevance to transplantation

Marostica

Nunes

Castelli

et al. 2022

Phil. Trans. R. Soc. B

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In his 1972 paper ‘The apportionment of human diversity’, Lewontin showed that, when averaged over loci, genetic diversity is predominantly attributable to differences among individuals within populations. However, selection can alter the apportionment of diversity of specific genes or genomic regions. We examine genetic diversity at the human leucocyte antigen (HLA) loci, located within the major histocompatibility complex (MHC) region. HLA genes code for proteins that are critical to adaptive immunity and are well-documented targets of balancing selection. The single-nucleotide polymorphisms (SNPs) within HLA genes show strong signatures of balancing selection on large timescales and are broadly shared among populations, displaying low F ST values. However, when we analyse haplotypes defined by these SNPs (which define ‘HLA alleles’), we find marked differences in frequencies between geographic regions. These differences are not reflected in the F ST values because of the extreme polymorphism at HLA loci, illustrating challenges in interpreting F ST . Differences in the frequency of HLA alleles among geographic regions are relevant to bone-marrow transplantation, which requires genetic identity at HLA loci between patient and donor. We discuss the case of Brazil's bone marrow registry, where a deficit of enrolled volunteers with African ancestry reduces the chance of finding donors for individuals with an MHC region of African ancestry. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

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Section: Discussionmentioning

confidence: 99%

Section: How Is Human Leucocyte Antigen Diversity Apportioned?mentioning

confidence: 99%

How HLA diversity is apportioned: influence of selection and relevance to transplantation

Marostica

Nunes

Castelli

et al. 2022

Phil. Trans. R. Soc. B

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“…Possible values of FAVA range between 0 and 1 for any sample size. However, when the number of samples is small, F ST can be constrained by the mean frequency of the dominant taxon, especially if this frequency is close to 0 or 1 (Alcala and Rosenberg, 2022). Normalizing F ST by its theoretical upper bound conditional on the number of samples and the mean frequency of the most abundant taxon (F max ST ) can account for this property, allowing for differences in variability to be distinguished from differences in the abundance of the dominant taxon.…”

Section: Definition Of Favamentioning

confidence: 99%

Quantifying compositional variability in microbial communities with FAVA

Morrison,

Xue,

Rosenberg

2024

Preprint

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Microbial communities vary across space, time, and individual hosts, presenting new challenges for the development of statistics measuring the variability of community composition. To understand differences across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA, a new normalized measure for characterizing compositional variability across multiple microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and equaling 1 when each sample is entirely comprised of a single taxon. Its definition relies on the population-genetic statisticFST, with samples playing the role of “populations” and taxa playing the role of “alleles.” Its convenient mathematical properties allow users to compare disparate data sets. For example, FAVA values are commensurable across different numbers of taxonomic categories and different numbers of samples considered. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We illustrate how FAVA can be used to describe across-individual taxonomic variability in ruminant microbiomes at different regions along the gastrointestinal tract. In a second example, a longitudinal analysis of gut microbiomes of healthy human adults taking an antibiotic, we use FAVA to quantify the increase in temporal variability of microbiomes following the antibiotic course and to measure the duration of the antibiotic’s influence on microbial variability. We have implemented this tool in an R package,FAVA, which can fit easily into existing pipelines for the analysis of microbial relative abundances.Significance statementStudies of microbial community composition across time, space, or biological replicates often rely on summary statistics that analyze just one or two samples at a time. Although these statistics effectively summarize the diversity of one sample or the compositional dissimilarity between two samples, they are ill-suited for measuring variability across many samples at once. Measuring compositional variability among many samples is key to understanding the temporal stability of a community across multiple time points, or the heterogeneity of microbiome composition across multiple experimental replicates or host individuals. Our proposed measure, FAVA, meets the need for a statistic summarizing compositional variability across many microbiome samples all at once.

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“…This statistic is defined in terms of both , the mean within-sample diversity, and , the variability of the pooled signature activities across all samples. The variability of the pooled signature activities is computed by first computing the average activity of each signature across all samples, and then computing the diversity of this pooled activity vector: F ST is then defined as the normalized difference between this pooled diversity and the mean within-sample diversity 62 , F ST = 1 when each sample contains a single signature, since each individual has no diversity and thus (approx. Figure 1c).…”

Section: Signature Variability Analysismentioning

confidence: 99%

“…F ST is then defined as the normalized difference between this pooled diversity and the mean within-sample diversity 62 ,…”

Section: Signature Variability Analysismentioning

confidence: 99%

Variability of mutational signatures is a footprint of carcinogens

Morrison,

Mangé,

Senkin

et al. 2023

Preprint

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Understanding the genomic impact of carcinogens is fundamental to cancer biology and prevention. However, recent coordinated efforts to detect such fingerprints have been largely unsuccessful, challenging the paradigm that carcinogens induce identifiable mutational signatures. Here we introduce a new method based on statistics from population genetics, signature variability analysis (SVA), that elucidates both the diversity of tumorcausing processes and the heterogeneity of population carcinogen exposure. When we use SVA to re-analyze four prominent studies commonly cited as evidence of nonmutagenic carcinogens, we find that tumors induced by environmental carcinogens do possess mutational signature patterns that distinguish them from spontaneous tumors, even if a specific mutational signature cannot be detected. We find that, across cancers, organs, and model organisms, carcinogen exposure generally increases both the diversity of mutational signatures within a tumor and the homogeneity of signature activity across subjects. Importantly, we show that this increase in signature diversity, far from being a background effect, is associated with the geographic incidence of cancer and can facilitate the acquisition of cancer driver mutations. Our results both encourage a re-examination of the genomic impact of numerous substances and introduce new tools for the analysis of the genomic effects of other substances, potentially influencing carcinogen classifications and cancer prevention policies.

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Mathematical constraints onF_ST: multiallelic markers in arbitrarily many populations

Cited by 10 publications

References 48 publications

How HLA diversity is apportioned: influence of selection and relevance to transplantation

How HLA diversity is apportioned: influence of selection and relevance to transplantation

Quantifying compositional variability in microbial communities with FAVA

Variability of mutational signatures is a footprint of carcinogens

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