Dataset Information

CSYseq: The first Y-chromosome sequencing tool typing a large number of Y-SNPs and Y-STRs to unravel worldwide human population genetics.

ABSTRACT: Male-specific Y-chromosome (chrY) polymorphisms are interesting components of the DNA for population genetics. While single nucleotide polymorphisms (Y-SNPs) indicate distant evolutionary ancestry, short tandem repeats (Y-STRs) are able to identify close familial kinships. Detailed chrY analysis provides thus both biogeographical background information as paternal lineage identification. The rapid advancement of high-throughput massive parallel sequencing (MPS) technology in the past decade has revolutionized genetic research. Using MPS, single-base information of both Y-SNPs as Y-STRs can be analyzed in a single assay typing multiple samples at once. In this study, we present the first extensive chrY-specific targeted resequencing panel, the 'CSYseq', which simultaneously identifies slow mutating Y-SNPs as evolution markers and rapid mutating Y-STRs as patrilineage markers. The panel was validated by paired-end sequencing of 130 males, distributed over 65 deep-rooted pedigrees covering 1,279 generations. The CSYseq successfully targets 15,611 Y-SNPs including 9,014 phylogenetic informative Y-SNPs to identify 1,443 human evolutionary Y-subhaplogroup lineages worldwide. In addition, the CSYseq properly targets 202 Y-STRs, including 81 slow, 68 moderate, 27 fast and 26 rapid mutating Y-STRs to individualize close paternal relatives. The targeted chrY markers cover a high average number of reads (Y-SNP = 717, Y-STR = 150), easy interpretation, powerful discrimination capacity and chrY specificity. The CSYseq is interesting for research on different time scales: to identify evolutionary ancestry, to find distant family and to discriminate closely related males. Therefore, this panel serves as a unique tool valuable for a wide range of genetic-genealogical applications in interdisciplinary research within evolutionary, population, molecular, medical and forensic genetics.

SUBMITTER: Claerhout S

PROVIDER: S-EPMC8423258 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs). METHODS:We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals. RESULTS:On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations. CONCLUSION:Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.

Project description:Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Dataset Information

CSYseq: The first Y-chromosome sequencing tool typing a large number of Y-SNPs and Y-STRs to unravel worldwide human population genetics.

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