Ancestry informative marker set for han chinese population.
Ontology highlight
ABSTRACT:
SUBMITTER: Qu HQ
PROVIDER: S-EPMC3291503 | biostudies-literature | 2012 Mar
REPOSITORIES: biostudies-literature
Publications
Ancestry informative marker set for han chinese population.
Qu Hui-Qi HQ Li Quan Q Xu Shuhua S McCormick Joseph B JB Fisher-Hoch Susan P SP Xiong Momiao M Qian Ji J Jin Li L
G3 (Bethesda, Md.) 20120301 3
The population of Han Chinese is ∼1.226 billion people. Genetic heterogeneity between northern Han Chinese (N-Han) and southern Han Chinese (S-Han) has been demonstrated by recent genome-wide studies. As an initial step toward health disparities and personalized medicine in Chinese population, this study developed a set of ancestry informative markers (AIM) for Han Chinese population. ...[more]
Similar Datasets
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.
| S-EPMC9997643 | biostudies-literature
Project description:Population stratification acts as a confounding factor in genetic association studies and may lead to false-positive or false-negative results. Previous studies have analyzed the genetic substructures in Han Chinese population, the largest ethnic group in the world comprising ?20% of the global human population. In this study, we examined 5540 Han Chinese individuals with about 1 million single-nucleotide polymorphisms (SNPs) and screened a panel of ancestry informative markers (AIMs) to facilitate the discerning and controlling of population structure in future association studies on Han Chinese. Based on genome-wide data, we first confirmed our previous observation of the north-south differentiation in Han Chinese population. Second, we developed a panel of 150 validated SNP AIMs to determine the northern or southern origin of each Han Chinese individual. We further evaluated the performance of our AIMs panel in association studies in simulation analysis. Our results showed that this AIMs panel had sufficient power to discern and control population stratification in Han Chinese, which could significantly reduce false-positive rates in both genome-wide association studies (GWAS) and candidate gene association studies (CGAS). We suggest this AIMs panel be genotyped and used to control and correct population stratification in the study design or data analysis of future association studies, especially in CGAS which is the most popular approach to validate previous reports on genetic associations of diseases in post-GWAS era.
| S-EPMC3895631 | biostudies-literature
Project description:The inference of genomic ancestry using ancestry informative markers (AIMs) can be useful for a range of studies in evolutionary genetics, biomedical research, and forensic analyses. However, the determination of AIMs for highly admixed populations with complex ancestries has remained a formidable challenge. Given the immense genetic heterogeneity and unique population structure of the Indian subcontinent, here we sought to derive AIMs that would yield a cohesive and faithful understanding of South Asian genetic origins. To discern the most optimal strategy for extracting AIMs for South Asians we compared three commonly used AIMs-determining methods namely, Infocalc, FST, and Smart Principal Component Analysis with ADMIXTURE, using previously published whole genome data from the Indian subcontinent. Our findings suggest that the Infocalc approach is likely most suitable for delineation of South Asian AIMs. In particular, Infocalc-2,000 (N = 2,000) appeared as the most informative South Asian AIMs panel that recapitulated the finer structure within South Asian genomes with high degree of sensitivity and precision, whereas a negative control with an equivalent number of randomly selected markers when used to interrogate the South Asian populations, failed to do so. We discuss the utility of all approaches under evaluation for AIMs derivation and interpreting South Asian genomic ancestries. Notably, this is the first report of an AIMs panel for South Asian ancestry inference. Overall these findings may aid in developing cost-effective resources for large-scale demographic analyses and foster expansion of our knowledge of human origins and disease, in the South Asian context.
| S-EPMC6143162 | biostudies-literature
Project description:BackgroundCase-control genetic studies of complex human diseases can be confounded by population stratification. This issue can be addressed using panels of ancestry informative markers (AIMs) that can provide substantial population substructure information. Previously, we described a panel of 128 SNP AIMs that were designed as a tool for ascertaining the origins of subjects from Europe, Sub-Saharan Africa, Americas, and East Asia.ResultsIn this study, genotypes from Human Genome Diversity Panel populations were used to further evaluate a 93 SNP AIM panel, a subset of the 128 AIMS set, for distinguishing continental origins. Using both model-based and relatively model-independent methods, we here confirm the ability of this AIM set to distinguish diverse population groups that were not previously evaluated. This study included multiple population groups from Oceana, South Asia, East Asia, Sub-Saharan Africa, North and South America, and Europe. In addition, the 93 AIM set provides population substructure information that can, for example, distinguish Arab and Ashkenazi from Northern European population groups and Pygmy from other Sub-Saharan African population groups.ConclusionThese data provide additional support for using the 93 AIM set to efficiently identify continental subject groups for genetic studies, to identify study population outliers, and to control for admixture in association studies.
| S-EPMC2728728 | biostudies-literature
Project description:BACKGROUND:Europeans and American Indians were major genetic ancestry of Hispanics in the U.S. These ancestral groups have markedly different incidence rates and outcomes in many types of cancers. Therefore, the genetic admixture may cause biased genetic association study with cancer susceptibility variants specifically in Hispanics. For example, the incidence rate of liver cancer has been shown with substantial disparity between Hispanic, Asian and non-Hispanic white populations. Currently, ancestry informative marker (AIM) panels have been widely utilized with up to a few hundred ancestry-informative single nucleotide polymorphisms (SNPs) to infer ancestry admixture. Notably, current available AIMs are predominantly located in intron and intergenic regions, while the whole exome sequencing (WES) protocols commonly used in translational research and clinical practice do not cover these markers. Thus, it remains challenging to accurately determine a patient's admixture proportion without additional DNA testing. RESULTS:In this study we designed an unique AIM panel that infers 3-way genetic admixture from three distinct and selective continental populations (African (AFR), European (EUR), and East Asian (EAS)) within evolutionarily conserved exonic regions. Initially, about 1 million exonic SNPs from selective three populations in the 1000 Genomes Project were trimmed by their linkage disequilibrium (LD), restricted to biallelic variants, and finally we optimized to an AIM panel with 250 SNP markers, or the UT-AIM250 panel, using their ancestral informativeness statistics. Comparing to published AIM panels, UT-AIM250 performed better accuracy when we tested with three ancestral populations (accuracy: 0.995?±?0.012 for AFR, 0.997?±?0.007 for EUR, and 0.994?±?0.012 for EAS). We further demonstrated the performance of the UT-AIM250 panel to admixed American (AMR) samples of the 1000 Genomes Project and obtained similar results (AFR, 0.085?±?0.098; EUR, 0.665?±?0.182; and EAS, 0.250?±?0.205) to previously published AIM panels (Phillips-AIM34: AFR, 0.096?±?0.127, EUR, 0.575?±?0.290, and EAS, 0.330?±?0.315; Wei-AIM278: AFR, 0.070?±?0.096, EUR, 0.537?±?0.267, and EAS, 0.393?±?0.300). Subsequently, we applied the UT-AIM250 panel to a clinical dataset of 26 self-reported Hispanic patients in South Texas with hepatocellular carcinoma (HCC). We estimated the admixture proportions using WES data of adjacent non-cancer liver tissues (AFR, 0.065?±?0.043; EUR, 0.594?±?0.150; and EAS, 0.341?±?0.160). Similar admixture proportions were identified from corresponding tumor tissues. In addition, we estimated admixture proportions of The Cancer Genome Atlas (TCGA) collection of hepatocellular carcinoma (TCGA-LIHC) samples (376 patients) using the UT-AIM250 panel. The panel obtained consistent admixture proportions from tumor and matched normal tissues, identified 3 possible incorrectly reported race/ethnicity, and/or provided race/ethnicity determination if necessary. CONCLUSIONS:Here we demonstrated the feasibility of using evolutionarily conserved exonic regions to infer admixture proportions and provided a robust and reliable control for sample collection or patient stratification for genetic analysis. R implementation of UT-AIM250 is available at https://github.com/chenlabgccri/UT-AIM250.
| S-EPMC6936141 | biostudies-literature
Project description:The Brazilian population is considered to be highly admixed. The main contributing ancestral populations were European and African, with Amerindians contributing to a lesser extent. The aims of this study were to provide a resource for determining and quantifying individual continental ancestry using the smallest number of SNPs possible, thus allowing for a cost- and time-efficient strategy for genomic ancestry determination. We identified and validated a minimum set of 192 ancestry informative markers (AIMs) for the genetic ancestry determination of Brazilian populations. These markers were selected on the basis of their distribution throughout the human genome, and their capacity of being genotyped on widely available commercial platforms. We analyzed genotyping data from 6487 individuals belonging to three Brazilian cohorts. Estimates of individual admixture using this 192 AIM panels were highly correlated with estimates using ~370?000 genome-wide SNPs: 91%, 92%, and 74% of, respectively, African, European, and Native American ancestry components. Besides that, 192 AIMs are well distributed among populations from these ancestral continents, allowing greater freedom in future studies with this panel regarding the choice of reference populations. We also observed that genetic ancestry inferred by AIMs provides similar association results to the one obtained using ancestry inferred by genomic data (370?K SNPs) in a simple regression model with rs1426654, related to skin pigmentation, genotypes as dependent variable. In conclusion, these markers can be used to identify and accurately quantify ancestry of Latin Americans or US Hispanics/Latino individuals, in particular in the context of fine-mapping strategies that require the quantification of continental ancestry in thousands of individuals.
| S-EPMC4930091 | biostudies-literature
Project description:BACKGROUND:Using DNA to determine an individual's ancestry from among human populations is generally interesting and useful for many purposes, including admixture mapping, controlling for population structure in disease or trait association studies and forensic ancestry inference. However, to estimate ancestry, including possible admixture within an individual, as well as heterogeneity within a group of individuals, allele frequencies are necessary for what are believed to be the contributing populations. For this purpose, panels of ancestry informative markers (AIMs) have been developed. RESULTS:We are presenting our work on one such panel, composed of 128 ancestry informative single-nucleotide polymorphisms (AISNPs) already proposed in the literature. Compared to previous studies of these AISNPs, we have studied three times the number of individuals (4,871) in three times as many population samples (119). We have validated this panel for many ancestry assignment and admixture studies, especially those that were the rationale for the original selection of the 128 SNPs: African Americans and Mexican Americans. At the same time, the limitations of the panel for distinguishing ancestry and quantifying admixture among Eurasian populations are noted. CONCLUSION:We demonstrate the simultaneous importance of the specific set of population samples and their relative sample sizes in the use of the structure program to determine which groups cluster together and consequently influence the ability of a marker panel to infer ancestry. We demonstrate the strengths and weaknesses of this particular panel of AISNPs in a global context.
| S-EPMC3025953 | biostudies-literature
Project description:Identification of genetic structure within wildlife populations have implications in their conservation and management. Accurately inferring population genetic structure requires whole-genome data across the geographical range of the species, which can be resource-intensive. A cheaper strategy is to employ a subset of markers that can efficiently recapitulate the population genetic structure inferred by the whole genome data. Such ancestry informative markers (AIMs), have rarely been developed for endangered species such as tigers utilizing single nucleotide polymorphisms (SNPs). Here, we first identify the population structure of the Indian tiger using whole-genome sequences and then develop an AIMs panel with a minimum number of SNPs that can recapitulate this structure. We identified four population clusters of Indian tigers with North-East, North-West, and South Indian tigers forming three separate groups, and Terai and Central Indian tigers forming a single cluster. To evaluate the robustness of our AIMs, we applied it to a separate dataset of tigers from across India. Out of 92 SNPs present in our AIMs panel, 49 were present in the new dataset. These 49 SNPs were sufficient to recapitulate the population genetic structure obtained from the whole genome data. To the best of our knowledge, this is the first-ever SNP-based AIMs panel for big cats, which can be used as a cost-effective alternative to whole-genome sequencing for detecting the biogeographical origin of Indian tigers. Our study can be used as a guideline for developing an AIMs panel for the management of other endangered species where obtaining whole genome sequences are difficult.
| S-EPMC8813985 | biostudies-literature
Project description:Estimates of different ancestral proportions in admixed populations are very important in population genetics studies, especially for the detection of population substructure effects in studies of case-control associations. Brazil is one of the most heterogeneous countries in the world, both from a socio-cultural and a genetic point of view. In this work, we investigated a previously developed set of 61 ancestry informative markers (AIM), aiming to estimate the proportions of four different ancestral groups (African, European, Native American and Asian) in Brazilian populations. To the best of our knowledge, this is the first study to use a set of AIM to investigate the genetic contribution of all four main parental populations to the Brazilian population, including Asian contribution. All selected markers were genotyped through multiplex PCR and capillary electrophoresis. The set was able to successfully differentiate the four ancestral populations (represented by 939 individuals) and identify their genetic contributions to the Brazilian population. In addition, it was used to estimate individual interethnic admixture of 1050 individuals from the Southeast region of Brazil and it showed that these individuals present a higher European ancestry contribution, followed by African, Asian and Native American ancestry contributions. Therefore, the 61 AIM set has proved to be a valuable tool to estimate individual and global ancestry proportions in populations mainly formed by these four groups. Our findings highlight the importance of using sets of AIM to evaluate population substructure in studies carried in admixed populations, in order to avoid misinterpretation of results.
| S-EPMC6222592 | biostudies-literature
Project description:Psoriasis is a chronic inflammatory skin disease related to immune, whose complexity of molecular mechanisms is still not fully clear. RNA sequencing has been widely applied in various fields including biological medicine. According to the bioinformatics analysis of differential genes, biomarkers and drug targets have been discovered for the diagnosis and treatment of diseases. Besides, the pathological mechanisms of disease and functions of gene can be evaluated. In the present study, we report the application of RNA sequencing in skin tissues from psoriatic and healthy persons. By obtaining 2139 differential expressed genes (DEGs), 208 significantly differential GO terms and 44 significantly differential pathways were generated. We found that the functions of DEGs were mainly related to cell cycle, inflammatory, virus, immune response and metabolic process.The major pathways included cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, chemokine signaling pathway, cell cycle, metabolic pathways, ribosome, peroxisome, steroid biosynthesis and biosynthesis of unsaturated fatty acids. Furthermore, co-expression network was constructed to identify core genes and relations between genes. we considered genes with high values of degree and k-core difference in the co-expression network as core genes, such as IFNG, IL26, TLR3, PRKCQ, TLR4, CD274, CDK1 and IL17A. We chose CD274, an important immune checkpoint, to evaluate its regulatory mechanisms. Candidate genes related to CD274 were evaluated by the co-expression network analysis, and the relations between CD274 and candidate genes were validated in epidermal keratinocytes. Finally, IFNG and CDK1 inhibitor (indirubin) were found increasing the expression levels of CD274. In addition, indirubin was confirmed to attenuate mouse psoriasis-like skin lesion with the mechanisms related to CD274. In conclusion, this study provides us a comprehensive transcriptome analysis method on psoriasis to identify core genes and explore the important regulatory functions of genes.
2018-11-28 | GSE114286 | GEO