Mutations virtually always have pleiotropic effects, yet most genome-wide association studies (GWAS) analyze effects one trait at a time. In order to investigate the performance of a multivariate approach to GWAS, we simulated scenarios where variation in a d-dimensional phenotype space was caused by a known subset of SNPs. Multivariate analyses of variance were then carried out on k traits, where k could be less than, greater than or equal to d. Our results show that power is maximized and false discovery rate (FDR) minimized when the number of traits analyzed, k, matches the true dimensionality of the phenotype being analyzed, d. When true dimensionality is high, the power of a single univariate analysis can be an order of magnitude less than the k=d case, even when the single trait with the largest genetic variance is chosen for analysis. When traits are added to a study in order of their independent genetic variation, the gains in power from increasing k up to d are much larger than the loss in power when k exceeds d. Simulations that explicitly model linkage disequilibrium (LD) indicate that when SNPs in disequilibrium are subjected to multivariate analysis, the magnitude of the apparent effect induced onto null SNPs by SNPs carrying a true effect weakens as k approaches d, such that the rank of P-values among a set of correlated SNPs becomes an increasingly reliable predictor of true positives. Multivariate GWAS outperform univariate ones under a wide range of conditions, and should become the standard in studies of the inheritance of complex phenotypes.. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g., hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from human EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC, and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-typespecific loci.

SUMMARYEndoC-βH1 is emerging as a critical human beta cell model to study the genetic and environmental etiologies of beta cell function, especially in the context of diabetes. Comprehensive knowledge of its molecular landscape is lacking yet required to fully take advantage of this model. Here, we report extensive chromosomal (spectral karyotyping), genetic (genotyping), epigenetic (ChIP-seq, ATAC-seq), chromatin interaction (Hi-C, Pol2 ChIA-PET), and transcriptomic (RNA-seq, miRNA-seq) maps of this cell model. Integrated analyses of these maps define known (e.g., PDX1, ISL1) and putative (e.g., PCSK1, mir-375) beta cell-specific chromatin interactions and transcriptional cis-regulatory networks, and identify allelic effects on cis-regulatory element use and expression.Importantly, comparative analyses with maps generated in primary human islets/beta cells indicate substantial preservation of chromatin looping, but also highlight chromosomal heterogeneity and fetal genomic signatures in EndoC-βH1. Together, these maps, and an interactive web application we have created for their exploration, provide important tools for the broad community in the design and success of experiments to probe and manipulate the genetic programs governing beta cell identity and (dys)function in diabetes.