Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

We propose a new sparsification method for the singular value decomposition—called the constrained singular value decomposition (CSVD)—that can incorporate multiple constraints such as sparsification and orthogonality for the left and right singular vectors. The CSVD can combine different constraints because it implements each constraint as a projection onto a convex set, and because it integrates these constraints as projections onto the intersection of multiple convex sets. We show that, with appropriate sparsification constants, the algorithm is guaranteed to converge to a stable point. We also propose and analyze the convergence of an efficient algorithm for the specific case of the projection onto the balls defined by the norms L₁ and L₂. We illustrate the CSVD and compare it to the standard singular value decomposition and to a non-orthogonal related sparsification method with: 1) a simulated example, 2) a small set of face images (corresponding to a configuration with a number of variables much larger than the number of observations), and 3) a psychometric application with a large number of observations and a small number of variables. The companion R-package, csvd, that implements the algorithms described in this paper, along with reproducible examples, are available for download from https://github.com/vguillemot/csvd.

Rabies is caused by neurotropic rabies virus (RABV), contributing to 60,000 human deaths annually. Even though rabies leads to major public health concerns worldwide, we still do not fully understand factors determining RABV tropism and why glial cells are unable to clear RABV from the infected brain. Here, we compare susceptibilities and immune responses of CNS cell types to infection with two RABV strains, Tha and its attenuated variant Th2P-4M, mutated on phospho- (P-protein) and matrix protein (M-protein). We demonstrate that RABV replicates in human stem cell-derived neurons and astrocytes but fails to infect human iPSC-derived microglia. Additionally, we observed major differences in transcription profiles and quantification of intracellular protein levels between antiviral immune responses mediated by neurons, astrocytes (IFNB1, CCL5, CXCL10, IL1B, IL6, and LIF), and microglia (CCL5, CXCL10, ISG15, MX1, and IL6) upon Tha infection. We also show that P- and M-proteins of Tha mediate evasion of NF-κB- and JAK-STAT-controlled antiviral host responses in neuronal cell types in contrast to glial cells, potentially explaining the strong neuron-specific tropism of RABV. Further, Tha-infected astrocytes and microglia protect neurons from Tha infection via a filtrable and transferable agent. Overall, our study provides novel insights into RABV tropism, showing the interest in studying the interplay of CNS cell types during RABV infection.

Component‐based multitable methods, such as multiple factor analysis (MFA), STATIS, and DiSTATIS, are routinely used to analyze multiblock data, which are now common in chemometrics and sensory evaluation studies. These blocks of data form data tables that—for example, in sensory evaluation—describe how different assessors evaluate a set of products either on a set of descriptors or on the similarity between products. To analyze these data, component‐based multitable methods extract orthogonal components explaining most of the variance of the data. However, when the data tables are heterogeneous or have complex structures, a single component space does not represent the data well and can give components that are difficult to interpret. Previous literature solved this interpretation problem by eliminating irrelevant variables—a process called sparsification—while keeping the components orthogonal. Here, we extended such methods to develop sparsification algorithms for three multitable methods, namely, “sparse MFA” (sMFA), “sparse STATIS” (sSTATIS), and “sparse DiSTATIS” (sDiSTATIS). In these sparse methods, we sparsified the data tables to identify the most informative assessors or products. In sMFA, we show how group sparsity can be used to sparsify whole tables (i.e., assessors or products), hereby greatly increasing the interpretability of sMFA's outcome. In sSTATIS and sDiSTATIS, we developed two different sparsification approaches: One approach creates subgroups of products and simplifies the components to facilitate interpretation; whereas the other approach creates subgroups of assessors and alleviates the problem of heterogeneity. We showed with three examples how these sparse methods increase interpretability of the results in sensory evaluation.

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. Such highly-structured data is strongly confounded, and principal components and their variants reliably estimate latent confounders. Conditioning on PCs in downstream analyses is known to improve power and reduce multiple-testing miscalibration and is an indispensable element of thousands of published functional genomic analyses. Further clarifying this approach is of fundamental interest to the genomics and statistics communities. We uncover a novel bias induced by PC conditioning and provide an analytic, deterministic and intuitive approximation. The bias exists because PCs are, roughly, unshielded colliders on a causal path: because PCs partially incorporate a causal genotype effect on one phenotype, the genotype becomes correlated with every phenotype conditional on PCs. We empirically quantify this bias in realistic simulations. For small genetic effects, a nearly negligible bias is observed for all tested PC variants. For large genetic effects, or other differential covariates, dramatic false positives can arise. Though one PC variant (supervised SVA) largely avoids this bias, it is computationally prohibitive genome-wide; further, its immunity to this bias is novel. Our analysis informs best practices for confounder correction in genomic studies.