Dataset Information

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components.

ABSTRACT: Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.

SUBMITTER: Yu C

PROVIDER: S-EPMC7186421 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Detecting Genotype-Population Interaction Effects by Ancestry Principal Components.

Yu Chenglong C Ni Guiyan G van der Werf Julius J Lee S Hong SH

Frontiers in genetics 20200421

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of informatio ...[more]

PMID: 32373165

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Project description: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.

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

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components.

Publications

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components.

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