Dataset Information

GRAF-pop: A Fast Distance-Based Method To Infer Subject Ancestry from Multiple Genotype Datasets Without Principal Components Analysis.

ABSTRACT: Inferring subject ancestry using genetic data is an important step in genetic association studies, required for dealing with population stratification. It has become more challenging to infer subject ancestry quickly and accurately since large amounts of genotype data, collected from millions of subjects by thousands of studies using different methods, are accessible to researchers from repositories such as the database of Genotypes and Phenotypes (dbGaP) at the National Center for Biotechnology Information (NCBI). Study-reported populations submitted to dbGaP are often not harmonized across studies or may be missing. Widely-used methods for ancestry prediction assume that most markers are genotyped in all subjects, but this assumption is unrealistic if one wants to combine studies that used different genotyping platforms. To provide ancestry inference and visualization across studies, we developed a new method, GRAF-pop, of ancestry prediction that is robust to missing genotypes and allows researchers to visualize predicted population structure in color and in three dimensions. When genotypes are dense, GRAF-pop is comparable in quality and running time to existing ancestry inference methods EIGENSTRAT, FastPCA, and FlashPCA2, all of which rely on principal components analysis (PCA). When genotypes are not dense, GRAF-pop gives much better ancestry predictions than the PCA-based methods. GRAF-pop employs basic geometric and probabilistic methods; the visualized ancestry predictions have a natural geometric interpretation, which is lacking in PCA-based methods. Since February 2018, GRAF-pop has been successfully incorporated into the dbGaP quality control process to identify inconsistencies between study-reported and computationally predicted populations and to provide harmonized population values in all new dbGaP submissions amenable to population prediction, based on marker genotypes. Plots, produced by GRAF-pop, of summary population predictions are available on dbGaP study pages, and the software, is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/Software.cgi.

SUBMITTER: Jin Y

PROVIDER: S-EPMC6686921 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GRAF-pop: A Fast Distance-Based Method To Infer Subject Ancestry from Multiple Genotype Datasets Without Principal Components Analysis.

Jin Yumi Y Schaffer Alejandro A AA Feolo Michael M Holmes J Bradley JB Kattman Brandi L BL

G3 (Bethesda, Md.) 20190808 8

Inferring subject ancestry using genetic data is an important step in genetic association studies, required for dealing with population stratification. It has become more challenging to infer subject ancestry quickly and accurately since large amounts of genotype data, collected from millions of subjects by thousands of studies using different methods, are accessible to researchers from repositories such as the database of Genotypes and Phenotypes (dbGaP) at the National Center for Biotechnology ...[more]

PMID: 31151998

Similar Datasets

Project description:Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals.We developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American).We developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies.

Project description:BACKGROUND:Current South American populations trace their origins mainly to three continental ancestries, i.e. European, Amerindian and African. Individual variation in relative proportions of each of these ancestries may be confounded with socio-economic factors due to population stratification. Therefore, ancestry is a potential confounder variable that should be considered in epidemiologic studies and in public health plans. However, there are few studies that have assessed the ancestry of the current admixed Chilean population. This is partly due to the high cost of genome-scale technologies commonly used to estimate ancestry. In this study we have designed a small panel of SNPs to accurately assess ancestry in the largest sampling to date of the Chilean mestizo population (n?=?3349) from eight cities. Our panel is also able to distinguish between the two main Amerindian components of Chileans: Aymara from the north and Mapuche from the south. RESULTS:A panel of 150 ancestry-informative markers (AIMs) of SNP type was selected to maximize ancestry informativeness and genome coverage. Of these, 147 were successfully genotyped by KASPar assays in 2843 samples, with an average missing rate of 0.012, and a 0.95 concordance with microarray data. The ancestries estimated with the panel of AIMs had relative high correlations (0.88 for European, 0.91 for Amerindian, 0.70 for Aymara, and 0.68 for Mapuche components) with those obtained with AXIOM LAT1 array. The country's average ancestry was 0.53?±?0.14 European, 0.04?±?0.04 African, and 0.42?±?0.14 Amerindian, disaggregated into 0.18?±?0.15 Aymara and 0.25?±?0.13 Mapuche. However, Mapuche ancestry was highest in the south (40.03%) and Aymara in the north (35.61%) as expected from the historical location of these ethnic groups. We make our results available through an online app and demonstrate how it can be used to adjust for ancestry when testing association between incidence of a disease and nongenetic risk factors. CONCLUSIONS:We have conducted the most extensive sampling, across many different cities, of current Chilean population. Ancestry varied significantly by latitude and human development. The panel of AIMs is available to the community for estimating ancestry at low cost in Chileans and other populations with similar ancestry.

Project description:BackgroundAccurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies.MethodsIn this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships.ResultsWe demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations.ConclusionsOur results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

Project description:To decipher gene regulatory networks, we used systematic suppression of 97 transcription factors (TFs) and 7 other genes with shRNA in mouse embryonic stem (ES) cells, followed by global gene expression profiling. Meta-analysis of these data together with the earlier data obtained by the induction of 50 TFs and the existing genome-wide data on TF binding sites identified the sets of regulated target genes for 23 TFs. Principal component analysis shows different roles of two groups of TFs that are active in ES cells: Pou5f1 and Sox2 support the expression of their target genes and prevent the upregulation of trophectoderm-related genes, whereas Esrrb, Sall4, Nanog, Gbx2, Grhl2, Mtf2, Aff1, Tcfap4, and Cdc5l support the expression of targets of Esrrb, including glycolysis genes, and prevent upregulation of targets of Trp53 and Polycomb TFs. If TFs from the second group are downregulated while Pou5f1 and Sox2 are still active, then the cell state changes towards epiblast lineages. Sequences for shRNA were designed to target 3 untranslated region of genes (Supplementary Table S8). Gene expression change was checked with real time qPCR (Supplementary Figure S7) and Westerm blot. ES cells (ES[MC1R(20)], passage 20) were cultured without feeders and were co-transfected with 1.6 g of shRNA expression vector and 0.4 g of pPyCAG-EGFP-IP carrying expression cassettes for puromycin resistant genes and EGFP using Effectene (Qiagen). Transfected cells were cultured in presence of 1.5 g/ml of puromycin and were harvested at 72 h after transfection. Mock cells were treated with transfection reagent without DNA and cultured in the absence of puromycin. Experiments were done in 3 replications with 2 of them used for gene expression profiling with microarrays. Total RNA was isolated by TRIzol (Invitrogen) after 2 days. Cy3-CTP labeled sample targets were prepared with total RNA by Low RNA Input Fluorescent Linear Amplification Kit (Agilent). Cy5-CTP labeled reference target was Stratagene Universal Mouse Reference RNA. Note that the processed data in the paper, which is also attached as GSE26520_Table_data.txt.gz, is slightly different from the values columns in each sample. The original processed data are normalized with a batch method, as the new batches of arrays added in the set. The value columns in this submission reflect full normalization as described in the data processing fields in each sample.

Dataset Information

GRAF-pop: A Fast Distance-Based Method To Infer Subject Ancestry from Multiple Genotype Datasets Without Principal Components Analysis.

Publications

GRAF-pop: A Fast Distance-Based Method To Infer Subject Ancestry from Multiple Genotype Datasets Without Principal Components Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets