GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap

Abstract

With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from http://sailing.cs.cmu.edu/genamap.

Multilayer knockoff filter: Controlled variable selection at multiple resolutions

“…When tackling high dimensional variable selection, researchers have at their disposal several methods based on regularized regression with penalties that can reflect an array of sparse structures [see, e.g., Jalali et al (2010), Kim and Xing (2009), Rao et al (2013)], corresponding to a multiplicity of possible resolutions for discoveries. While several of them have been implemented in the context of genetic association studies [Xing et al. (2014), Zhou et al (2010)], their application has been hampered by the lack of inferential guarantees on the selection.…”

Methodology in phenome-wide association studies: a systematic review

“…[48,49] In term of its application to PheWAS, SPA has the ability to correct the inflated type I error caused by the unbalanced case-control ratio through adjusting single-variant score statistics and is faster than other existing rare-variant tests. Based on the availability of multi-omic data such as genotype markers (genome), gene expression measurements (transcriptome) and clinical traits measurements (phenome), BioBin [50] and GenAMap [51] were developed to enable the identification of the biological mechanisms underlying significant PheWAS associations. In particular, BioBin can be applied for PheWAS analysis of rare genetic variant to enhance study power.…”

Explaining artificial intelligence with visual analytics in healthcare

“…(1) Tooltips that pop up when hovering or clicking a visualization can show raw data (e.g., Malik et al, 2015;Xing et al, 2014) or additional visualizations (e.g., Afzal et al, 2011;Gotz et al, 2014). ( 2) Collapsing components removes visual clutter, for example lines in line graphs (Afzal et al, 2011) or parallel coordinates (Huang et al, 2019), identical rows in matrices (Dang et al, 2015), or similar points in scatter plots (Kwon et al, 2018).…”

“…An interesting zooming variant is lensing, which enlarges a specific area and compresses the rest: Filter interactions allow to focus on insights of interest by setting conditions on the data with check-boxes, radio buttons or sliders. Examples are: filtering network connections above a correlation threshold (Xing et al, 2014), filtering results that are statistically significant (Jönsson et al, 2019, Figure 3b), and adjusting the range of attributes in parallel coordinates by brushing axes (e.g., Yu et al, 2017) or manipulating sliders on the axes (e.g., Jönsson et al, 2019;Santamaría et al, 2008).…”