scholarly journals Selective Inference: The Silent Killer of Replicability

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Yoav Benjamini
Keyword(s):  
Selective Inference

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 Selective inference in complex research

2009 ◽  
Vol 367 (1906) ◽  
pp. 4255-4271 ◽  
Author(s):  
Yoav Benjamini ◽  
Ruth Heller ◽  
Daniel Yekutieli
Keyword(s):  
Type 2 Diabetes ◽  
False Discovery Rate ◽  
Confidence Intervals ◽  
False Discovery ◽  
False Discovery Rate Approach ◽  
Selective Inference

We explain the problem of selective inference in complex research using a recently published study: a replicability study of the associations in order to reveal and establish risk loci for type 2 diabetes. The false discovery rate approach to such problems will be reviewed, and we further address two problems: (i) setting confidence intervals on the size of the risk at the selected locations and (ii) selecting the replicable results.

scholarly journals A knockoff filter for high-dimensional selective inference

2019 ◽  
Vol 47 (5) ◽  
pp. 2504-2537 ◽  
Author(s):  
Rina Foygel Barber ◽  
Emmanuel J. Candès
Keyword(s):  
High Dimensional ◽  
Selective Inference

scholarly journals Selective inference with a randomized response

2018 ◽  
Vol 46 (2) ◽  
pp. 679-710 ◽  
Author(s):  
Xiaoying Tian ◽  
Jonathan Taylor
Keyword(s):  
Randomized Response ◽  
Selective Inference
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