Dataset Information

Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans.

ABSTRACT: BACKGROUND:The identification and use of Ancestry-Sensitive Markers (ASMs), i.e. genetic polymorphisms facilitating the genetic reconstruction of geographical origins of individuals, is far from straightforward. RESULTS:Here we describe the ascertainment and application of five different sets of 47 single nucleotide polymorphisms (SNPs) allowing the inference of major human groups of different continental origin. For this, we first used 74 cell lines, representing human males from six different geographical areas and screened them with the Affymetrix Mapping 10K assay. In addition to using summary statistics estimating the genetic diversity among multiple groups of individuals defined by geography or language, we also used the program STRUCTURE to detect genetically distinct subgroups. Subsequently, we used a pairwise F(ST) ranking procedure among all pairs of genetic subgroups in order to identify a single best performing set of ASMs. Our initial results were independently confirmed by genotyping this set of ASMs in 22 individuals from Somalia, Afghanistan and Sudan and in 919 samples from the CEPH Human Genome Diversity Panel (HGDP-CEPH) CONCLUSION: By means of our pairwise population F(ST) ranking approach we identified a set of 47 SNPs that could serve as a panel of ASMs at a continental level.

SUBMITTER: Kersbergen P

PROVIDER: S-EPMC2775748 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans.

Kersbergen Paula P van Duijn Kate K Kloosterman Ate D AD den Dunnen Johan T JT Kayser Manfred M de Knijff Peter P

BMC genetics 20091027

<h4>Background</h4>The identification and use of Ancestry-Sensitive Markers (ASMs), i.e. genetic polymorphisms facilitating the genetic reconstruction of geographical origins of individuals, is far from straightforward.<h4>Results</h4>Here we describe the ascertainment and application of five different sets of 47 single nucleotide polymorphisms (SNPs) allowing the inference of major human groups of different continental origin. For this, we first used 74 cell lines, representing human males from ...[more]

PMID: 19860882

Similar Datasets

Project description:BackgroundGenetic ancestry is known to impact outcomes of genotype-phenotype studies that are designed to identify risk for common diseases in human populations. Failure to control for population stratification due to genetic ancestry can significantly confound results of disease association studies. Moreover, ancestry is a critical factor in assessing lifetime risk of disease, and can play an important role in optimizing treatment. As modern medicine moves towards using personal genetic information for clinical applications, it is important to determine genetic ancestry in an accurate, cost-effective and efficient manner. Self-identified race is a common method used to track and control for population stratification; however, social constructs of race are not necessarily informative for genetic applications. The use of ancestry informative markers (AIMs) is a more accurate method for determining genetic ancestry for the purposes of population stratification.Methodology/principal findingsHere we introduce a novel panel of 36 microsatellite (MSAT) AIMs that determines continental admixture proportions. This panel, which we have named Continental Ancestry Informative Markers or CoAIMs, consists of MSAT AIMs that were chosen based upon their measure of genetic variance (F(st)), allele frequencies and their suitability for efficient genotyping. Genotype analysis using CoAIMs along with a Bayesian clustering method (STRUCTURE) is able to discern continental origins including Europe/Middle East (Caucasians), East Asia, Africa, Native America, and Oceania. In addition to determining continental ancestry for individuals without significant admixture, we applied CoAIMs to ascertain admixture proportions of individuals of self declared race.Conclusion/significanceCoAIMs can be used to efficiently and effectively determine continental admixture proportions in a sample set. The CoAIMs panel is a valuable resource for genetic researchers performing case-control genetic association studies, as it can control for the confounding effects of population stratification. The MSAT-based approach used here has potential for broad applicability as a cost effective tool toward determining admixture proportions.

Project description:Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays. Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only 0.6% in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than 10% of the AIMs. Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina's Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations.

Project description:BackgroundPopulation stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification.ResultsWe propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual's continental and sub-continental ancestry. To predict an individual's continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of ? 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control's ? from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% ± 2.4%, 95.6% ± 3.9%, 95.6% ± 2.1%, 98.3% ± 2.0%, and 95.9% ± 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver.ConclusionsETHNOPRED is a novel technique for producing classifiers that can identify an individual's continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values.

Dataset Information

Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans.

Publications

Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets