scholarly journals A selective inference approach for FDR control using multi-omics covariates yields insights into disease risk

2019 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Hypothesis Tests ◽  
Selective Inference

AbstractTo correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive p-value thresholding (Lei & Fithian 2018, AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association p-values play the role of the primary data for AdaPT; SNPs are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically-correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene-gene coexpression, captured by subnetwork (module) membership. In all 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefontal cortex (Werling et al. 2019). We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

scholarly journals A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hypothesis Tests ◽  
Selective Inference

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals An approach to gene-based testing accounting for dependence of tests among nearby genes

2021 ◽  
Author(s):  
Ronald J Yurko ◽  
Kathryn Roeder ◽  
Bernie Devlin ◽  
Max G'Sell
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Autism Spectrum ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Strongly Correlated ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

scholarly journals The harmonic mean p-value for combining dependent tests

2019 ◽  
Vol 116 (4) ◽  
pp. 1195-1200 ◽  
Author(s):  
Daniel J. Wilson
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Scientific Discovery ◽  
Association Studies ◽  
Harmonic Mean ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Familywise Error Rate ◽  
Significance Threshold ◽  
Genome Wide

Analysis of “big data” frequently involves statistical comparison of millions of competing hypotheses to discover hidden processes underlying observed patterns of data, for example, in the search for genetic determinants of disease in genome-wide association studies (GWAS). Controlling the familywise error rate (FWER) is considered the strongest protection against false positives but makes it difficult to reach the multiple testing-corrected significance threshold. Here, I introduce the harmonic mean p-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human–pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesis groups is greater than the power of the Benjamini–Hochberg procedure to detect significant hypotheses, although the latter only controls the weaker false discovery rate (FDR). The HMP has broad implications for the analysis of large datasets, because it enhances the potential for scientific discovery.

scholarly journals Combinatorial and statistical prediction of gene expression from haplotype sequence

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i194-i202
Author(s):  
Berk A Alpay ◽  
Pinar Demetci ◽  
Sorin Istrail ◽  
Derek Aguiar
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Association Studies ◽  
Classification Problem ◽  
Statistical Prediction ◽  
Model Complexity ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Genome Wide Association Studies ◽  
Regulatory Effects

Abstract Motivation Genome-wide association studies (GWAS) have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, expression quantitative trait loci studies have interpreted many of these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experimental investigation. Results In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that models haplotype sharing with a modified suffix tree data structure and computes expression groups by spectral clustering. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD with three state-of-the-art expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissues. HAPLEXD exhibits significantly higher classification accuracy overall; HAPLEXR shows higher prediction accuracy on approximately half of the genes tested and the largest number of best predicted genes (r2>0.1) among all methods. We show that variant and haplotype features selected by HAPLEXR are smaller in size than competing methods (and thus more interpretable) and are significantly enriched in functional annotations related to gene regulation. These results demonstrate the importance of explicitly modeling non-dosage dependent and intragenic epistatic effects when predicting expression. Availability and implementation Source code and binaries are freely available at https://github.com/rapturous/HAPLEX. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Sean M. Burnard ◽  
Rodney A. Lea ◽  
Miles Benton ◽  
David Eccles ◽  
Daniel W. Kennedy ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Complex Traits ◽  
Multiple Testing ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elastic Net ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

scholarly journals The paltry power of priors versus populations

2019 ◽  
Author(s):  
Jianan Zhan ◽  
Dan E. Arking ◽  
Joel S. Bader
Keyword(s):  
Population Size ◽  
Multiple Testing ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Significant Finding ◽  
Rna Seq ◽  
Test Power ◽  
Genome Wide

AbstractBiological experiments often involve hypothesis testing at the scale of thousands to millions of tests. Alleviating the multiple testing burden has been a goal of many methods designed to boost test power by focusing tests on the alternative hypotheses most likely to be true. Very often, these methods either explicitly or implicitly make use of prior probabilities that bias significance for favored sets thought to be enriched for significant finding. Nevertheless, most genomics experiments, and in particular genome-wide association studies (GWAS), still use traditional univariate tests rather than more sophisticated approaches. Here we use GWAS to demonstrate why unbiased tests remain in favor. We calculate test power assuming perfect knowledge of a prior distribution and then derive the population size increase required to provided the same boost without a prior. We show that population size is exponentially more important than prior, providing a rigorous explanation for the observed avoidance of prior-based methods.Author summaryBiological experiments often test thousands to millions of hypotheses. Gene-based tests for human RNA-Seq data, for example, involve approximately 20,000; genome-wide association studies (GWAS) involve about 1 million effective tests. The conventional approach is to perform individual tests and then apply a Bonferroni correction to account for multiple testing. This approach implies a single-test p-value of 2.5 × 10−6 for RNA-Seq experiments, and a p-value of 5 × 10−8 for GWAS, to control the false-positive rate at a conventional value of 0.05. Many methods have been proposed to alleviate the multiple-testing burden by incorporating a prior probability that boosts the significance for a subset of candidate genes or variants. At the extreme limit, only the candidate set is tested, corresponding to a decreased multiple testing burden. Despite decades of methods development, prior-based tests have not been generally used. Here we compare the power increase possible with a prior with the increase possible with a much simpler strategy of increasing a study size. We show that increasing the population size is exponentially more valuable than increasing the strength of prior, even when the true prior is known exactly. These results provide a rigorous explanation for the continued use of simple, robust methods rather than more sophisticated approaches.

scholarly journals Polymorphic variants in Sweet and Umami taste receptor genes and birthweight

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Riccardo Farinella ◽  
Ilaria Erbi ◽  
Alice Bedini ◽  
Sara Donato ◽  
Manuel Gentiluomo ◽  
...  
Keyword(s):  
Birth Weight ◽  
Multiple Testing ◽  
Association Studies ◽  
Taste Receptor ◽  
Adult Life ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Long Term Effects ◽  
Umami Taste ◽  
Genome Wide

AbstractThe first thousand days of life from conception have a significant impact on the health status with short, and long-term effects. Among several anthropometric and maternal lifestyle parameters birth weight plays a crucial role on the growth and neurological development of infants. Recent genome wide association studies (GWAS) have demonstrated a robust foetal and maternal genetic background of birth weight, however only a small proportion of the genetic hereditability has been already identified. Considering the extensive number of phenotypes on which they are involved, we focused on identifying the possible effect of genetic variants belonging to taste receptor genes and birthweight. In the human genome there are two taste receptors family the bitter receptors (TAS2Rs) and the sweet and umami receptors (TAS1Rs). In particular sweet perception is due to a heterodimeric receptor encoded by the TAS1R2 and the TAS1R3 gene, while the umami taste receptor is encoded by the TAS1R1 and the TAS1R3 genes. We observed that carriers of the T allele of the TAS1R1-rs4908932 SNPs showed an increase in birthweight compared to GG homozygotes Coeff: 87.40 (35.13–139.68) p-value = 0.001. The association remained significant after correction for multiple testing. TAS1R1-rs4908932 is a potentially functional SNP and is in linkage disequilibrium with another polymorphism that has been associated with BMI in adults showing the importance of this variant from the early stages of conception through all the adult life.

scholarly journals Colocalization of GWAS and eQTL Signals Detects Target Genes

2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Martijn van de Bunt ◽  
Ayellet V. Segrè ◽  
Xiao Li ◽  
Jong Wha J Joo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Specific Tissue ◽  
The Individual ◽  
Eqtl Data ◽  
Different Tissues

AbstractThe vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual’s disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/

scholarly journals DOP65 Mirikizumab regulates genes involved in anti-TNF resistance and ulcerative colitis disease activity

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S103-S104
Author(s):  
B Steere ◽  
J Schmitz ◽  
N Powell ◽  
R Higgs ◽  
K Gottlieb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Disease Activity ◽  
Clinical Outcomes ◽  
Large Scale ◽  
Association Studies ◽  
Randomised Clinical Trial ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Mixed Effect

Abstract Background Mirikizumab (miri), a p19-directed IL-23 antibody, demonstrated efficacy and was well-tolerated in a phase 2 randomised clinical trial in patients with moderate-to-severe UC (NCT02589665). This abstract explores gene expression changes in colonic tissue from study patients and their association with clinical outcomes. Methods Patients were randomised 1:1:1:1 to receive intravenous placebo (PBO, N = 63), miri 50 mg (N = 63) or 200 mg (N = 62) with the possibility of exposure-based dose increases, or fixed miri 600 mg (N = 61) every 4 weeks for 12 weeks. Patient biopsies were collected at baseline (BL) and Week 12, and differential gene expression was measured using an Affymetrix HTA2.0 exon-format microarray workflow. Genes were represented by their largest groups of highly correlated exons. Weeks 0 and 12 data were compared in all treatment groups to produce differential expression values (DEVs). Mean fold changes in DEVs between PBO and each dose group were calculated in a mixed-effect model. A threshold of false discovery rate-adjusted p-value ≤ 0.05 was applied to the significance of the fold change values, and a filter of an absolute value for the fold changes of ≥0.5 log2 units was applied. Results The greatest improvement in clinical outcomes at Week 12 was observed in the 200 mg miri group1; likewise, the greatest PBO-adjusted change from BL in transcripts was observed in this group. Transcripts correlating with key UC disease activity indices at BL, including modified Mayo score (MMS), ulcerative colitis Endoscopic Index of Severity (UCEIS), Geboes score, and Robarts Histopathology Index (RHI), included MMP1, MMP3, S100A8, IL1B, and UGT2A3, with the highest correlations occurring with the histopathologic indices (Figure 1). Miri treatment modulated the expression of transcriptional modules predicted to be enriched in cell profiles identified as key drivers of UC2 (Table 1, columns 1–2) as well as genes determined to be associated with UC by genome-wide association studies (GWAS; Table 1, column 3). Moreover, miri treatment affected transcripts involved in resistance to anti-TNF treatments (Table 1, column 4). A number of the genes in these categories were among those most affected by miri treatment (Table 1, columns 5–6). Conclusion This is the first large-scale gene expression study of diseased tissue from UC patients treated with anti-IL23p19 therapy. It is the first study to show how anti-IL23p19 therapy modulates biological pathways involved in resistance to anti-TNFs. These results are consistent with the demonstrated efficacy of miri in patients in whom TNF antagonists have failed. References

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