Evaluation of population stratification adjustment using genome-wide or exonic variants.
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ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC7722041 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The search for expression quantitative trait loci has traditionally centred entirely on the process of transcription, whereas variants with effects on messenger RNA translation have not been systematically studied. Here we present a high-throughput approach for measuring translational cis-regulation in the human genome. Using ribosomal association as proxy for translational efficiency of polymorphic messenger RNAs, we test the ratio of polysomal/non-polysomal messenger RNA level as a quantitative trait for association with single nucleotide polymorphisms on the same messenger RNA transcript. We identify one important ribosomal distribution effect, from rs1131017 in the 5'-untranslated region of RPS26, that is in high linkage disequilibrium with the 12q13 locus for susceptibility to type 1 diabetes. The effect on translation is confirmed at the protein level by quantitative western blots, both ex vivo and after in vitro translation. Our results are a proof-of-principle that allelic effects on translation can be detected at a transcriptome-wide scale.
| S-EPMC3749366 | biostudies-literature
Project description:To date, the search for expression quantitative trait loci (eQTL) has centered entirely on transcription; variants with effects on mRNA translation have not been systematically studied. We tested the use of ribosomal association as proxy for translational efficiency of polymorphic mRNAs to develop a novel high throughput approach for translational cis-regulation in the human genome.
2013-04-18 | GSE46195 | GEO
Project description:Rare variation in protein coding sequence is poorly captured by GWAS arrays and has been hypothesized to contribute to disease heritability. Using the Illumina HumanExome SNP array, we successfully genotyped 191,032 common and rare non-synonymous, splice site, or nonsense variants in a multiethnic sample of 2,984 breast cancer cases, 4,376 prostate cancer cases, and 7,545 controls. In breast cancer, the strongest associations included either SNPs in or gene burden scores for genes LDLRAD1, SLC19A1, FGFBP3, CASP5, MMAB, SLC16A6, and INS-IGF2. In prostate cancer, one of the most associated SNPs was in the gene GPRC6A (rs2274911, Pro91Ser, OR = 0.88, P = 1.3 × 10(-5)) near to a known risk locus for prostate cancer; other suggestive associations were noted in genes such as F13A1, ANXA4, MANSC1, and GP6. For both breast and prostate cancer, several of the most significant associations involving SNPs or gene burden scores (sum of minor alleles) were noted in genes previously reported to be associated with a cancer-related phenotype. However, only one of the associations (rs145889899 in LDLRAD1, p = 2.5 × 10(-7) only seen in African Americans) for overall breast or prostate cancer risk was statistically significant after correcting for multiple comparisons. In addition to breast and prostate cancer, other cancer-related traits were examined (body mass index, PSA level, and alcohol drinking) with a number of known and potentially novel associations described. In general, these findings do not support there being many protein coding variants of moderate to high risk for breast and prostate cancer with odds ratios over a range that is probably required for protein coding variation to play a truly outstanding role in risk heritability. Very large sample sizes will be required to better define the role of rare and less penetrant coding variation in prostate and breast cancer disease genetics.
| S-EPMC3610631 | biostudies-literature
Project description:BackgroundGenome-wide association studies can provide novel insights into diseases of interest, as well as to the responsiveness of an individual to specific treatments. In such studies, it is very important to correct for population stratification, which refers to allele frequency differences between cases and controls due to systematic ancestry differences. Population stratification can cause spurious associations if not adjusted properly. The principal component analysis (PCA) method has been relied upon as a highly useful methodology to adjust for population stratification in these types of large-scale studies. Recently, the linear mixed model (LMM) has also been proposed to account for family structure or cryptic relatedness. However, neither of these approaches may be optimal in properly correcting for sample structures in the presence of subject outliers.ResultsWe propose to use robust PCA combined with k-medoids clustering to deal with population stratification. This approach can adjust for population stratification for both continuous and discrete populations with subject outliers, and it can be considered as an extension of the PCA method and the multidimensional scaling (MDS) method. Through simulation studies, we compare the performance of our proposed methods with several widely used stratification methods, including PCA and MDS. We show that subject outliers can greatly influence the analysis results from several existing methods, while our proposed robust population stratification methods perform very well for both discrete and admixed populations with subject outliers. We illustrate the new method using data from a rheumatoid arthritis study.ConclusionsWe demonstrate that subject outliers can greatly influence the analysis result in GWA studies, and propose robust methods for dealing with population stratification that outperform existing population stratification methods in the presence of subject outliers.
| S-EPMC3637636 | biostudies-literature
Project description:Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of Type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the Genetic Investigation of Anthropometric Traits consortium.
| S-EPMC6239891 | biostudies-literature
Project description:Owing to the development of next-generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine-rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open-access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA-seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE-disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE-disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE-disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE-disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE-disrupting nonsense variants tended to escape nonsense-mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE-disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon-junction read counts from the tumor samples.
| S-EPMC6885893 | biostudies-literature
Project description:ObjectivesLetrozole is a nonsteroidal aromatase inhibitor used to treat hormone-receptor-positive breast cancer. Variability in letrozole efficacy and toxicity may be partially attributable to variable systemic drug exposure, which may be influenced by germline variants in the enzymes responsible for letrozole metabolism, including cytochrome P450 2A6 (CYP2A6). The objective of this genome-wide association study (GWAS) was to identify polymorphisms associated with steady-state letrozole concentrations.MethodsThe Exemestane and Letrozole Pharmacogenetics (ELPh) Study randomized postmenopausal patients with hormone-receptor-positive nonmetastatic breast cancer to letrozole or exemestane treatment. Germline DNA was collected pretreatment and blood samples were collected after 1 or 3 months of treatment to measure steady-state letrozole (and exemestane) plasma concentrations via HPLC/MS. Genome-wide genotyping was conducted on the Infinium Global Screening Array (>650 000 variants) followed by imputation. The association of each germline variant with age- and BMI-adjusted letrozole concentrations was tested in self-reported white patients via linear regression assuming an additive genetic model.ResultsThere were 228 patients who met the study-specific inclusion criteria and had both DNA and letrozole concentration data for this GWAS. The association for one genotyped polymorphism (rs7937) with letrozole concentration surpassed genome-wide significance (P = 5.26 × 10-10), explaining 13% of the variability in untransformed steady-state letrozole concentrations. Imputation around rs7937 and in silico analyses identified rs56113850, a variant in the CYP2A6 intron that may affect CYP2A6 expression and activity. rs7937 was associated with age- and BMI-adjusted letrozole levels even after adjusting for genotype-predicted CYP2A6 metabolic phenotype (P = 3.86 × 10-10).ConclusionOur GWAS findings confirm that steady-state letrozole plasma concentrations are partially determined by germline polymorphisms that affect CYP2A6 activity, including variants near rs7937 such as the intronic rs56113850 variant. Further research is needed to confirm whether rs56113850 directly affects CYP2A6 activity and to integrate nonexonic variants into CYP2A6 phenotypic activity prediction systems.
| S-EPMC8185249 | biostudies-literature
Project description:BackgroundA usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure.ResultsWe assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer's disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach.ConclusionsMatching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.
| S-EPMC4367953 | biostudies-literature
Project description:Population stratification or confounding by genetic ancestry is a potential cause of false associations in genetic association studies. Estimation of and adjustment for genetic ancestry has become common practice thanks in part to the availability of ancestry informative markers on genome-wide association study (GWAS) arrays. While array data is now widespread, these data are not ubiquitous as several large epidemiologic and clinic-based studies lack genome-wide data. One such large epidemiologic-based study lacking genome-wide data accessible to investigators is the National Health and Nutrition Examination Surveys (NHANES), population-based cross-sectional surveys of Americans linked to demographic, health, and lifestyle data conducted by the Centers for Disease Control and Prevention. DNA samples (n = 14,998) were extracted from biospecimens from consented NHANES participants between 1991-1994 (NHANES III, phase 2) and 1999-2002 and represent three major self-identified racial/ethnic groups: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We as the Epidemiologic Architecture for Genes Linked to Environment study genotyped candidate gene and GWAS-identified index variants in NHANES as part of the larger Population Architecture using Genomics and Epidemiology I study for collaborative genetic association studies. To enable basic quality control such as estimation of genetic ancestry to control for population stratification in NHANES san genome-wide data, we outline here strategies that use limited genetic data to identify the markers optimal for characterizing genetic ancestry. From among 411 and 295 autosomal SNPs available in NHANES III and NHANES 1999-2002, we demonstrate that markers with ancestry information can be identified to estimate global ancestry. Despite limited resolution, global genetic ancestry is highly correlated with self-identified race for the majority of participants, although less so for ethnicity. Overall, the strategies outlined here for a large epidemiologic study can be applied to other datasets accessible for genotype-phenotype studies but are sans genome-wide data.
| S-EPMC4858524 | biostudies-literature
Project description:Cuba is the most populated country in the Caribbean and has a rich and heterogeneous genetic heritage. Here, we take advantage of dense genomic data from 860 Cuban individuals to reconstruct the genetic structure and ancestral origins of this population. We found distinct admixture patterns between and within the Cuban provinces. Eastern provinces have higher African and Native American ancestry contributions (average 26% and 10%, respectively) than the rest of the Cuban provinces (average 17% and 5%, respectively). Furthermore, in the Eastern Cuban region, we identified more intense sex-specific admixture patterns, strongly biased towards European male and African/Native American female ancestries. Our subcontinental ancestry analyses in Cuba highlight the Iberian population as the best proxy European source population, South American and Mesoamerican populations as the closest Native American ancestral component, and populations from West Central and Central Africa as the best proxy sources of the African ancestral component. Finally, we found complex admixture processes involving two migration pulses from both Native American and African sources. Most of the inferred Native American admixture events happened early during the Cuban colonial period, whereas the African admixture took place during the slave trade and more recently as a probable result of large-scale migrations from Haiti.
| S-EPMC6065444 | biostudies-other