Unique contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies

Gene regulation is known to play a fundamental role in human disease, but mechanisms of regulation vary greatly across genes. Here, we explore the contributions to disease of two types of genes: genes whose regulation is driven by enhancer regions as opposed to promoter regions (Enhancer-driven) and genes that regulate many other genes in trans (Master-regulator). We link these genes to SNPs using a comprehensive set of SNP-to-gene (S2G) strategies and apply stratified LD score regression to the resulting SNP annotations to draw three main conclusions about 11 autoimmune diseases and blood cell traits (average N =306K). First, Enhancer-driven genes defined in blood using functional genomics data (e.g. ATAC-seq, RNA-seq, PC-HiC) are uniquely informative for autoimmune disease heritability, after conditioning on a broad set of regulatory annotations from the baseline-LD model. Second, Master-regulator genes defined using trans-eQTL in blood are also uniquely informative for autoimmune disease heritability. Third, integrating Enhancer-driven and Master-regulator gene sets with protein-protein interaction (PPI) network information magnified their unique disease signal. The resulting PPI-enhancer gene score produced >2x stronger conditional signal (maximum standardized SNP annotation effect size (τ*) = 2.0 (s.e. 0.3) vs. 0.91 (s.e. 0.21)), and >2x stronger gene-level enrichment for approved autoimmune disease drug targets (5.3x vs. 2.1x), as compared to the recently proposed Enhancer Domain Score (EDS). In each case, using functionally informed S2G strategies to link genes to SNPs that may regulate them produced much stronger disease signals (4.1x-13x larger τ* values) than conventional window-based S2G strategies. We conclude that Enhancer-driven and Master-regulator genes are uniquely informative for human disease, and that PPI networks and S2G strategies magnify these signals.

Search Design and Broad Matching

We study decentralized mechanisms for allocating firms into search pools. The pools are created in response to noisy preference signals provided by consumers, who then browse the pools via costly random sequential search. Surplus-maximizing search pools are implementable in symmetric Nash equilibrium. Full extraction of the maximal surplus is implementable if and only if the distribution of consumer types satisfies a set of simple inequalities, which involve the relative fractions of consumers who like different products and the Bhattacharyya coefficient of similarity between their conditional signal distributions. The optimal mechanism can be simulated by a keyword auction with broad matching. (JEL C78, D44, D82)

A corpus-based study of metaphor signaling variations in three genres

This study examines the use of words and phrases that signal metaphors in three genres in order to further examine the corpus-based evidence for signaling variation mentioned in previous research. While previous studies have focused on pragmatic functions, discourse functions, and the level of conventionalization, this study demonstrates that the communicative goals within each genre underlie the reasons for the metaphor signaling. Three corpora of approximately 600,000 words were created for this research, and they were made up of US presidential addresses, popular science articles, and business periodical articles. The corpora were electronically queried for the use of sixteen previously identified metaphor signals in order to obtain comparable quantitative data. The study was complemented by a qualitative analysis of the identified instances of signaled metaphors. We found that three metaphor signal categories – copular similes, verbal processes, and modals/conditionals – accounted for the large majority of the signals analyzed in the genres. Furthermore, we found that while copular similes and verbal processes signals were used for different rhetorical purposes, depending on the communicative goals of each genre, the conditional signal was always used to foreshadow metaphorically expressed possibilities, regardless of the genre in which it was used.

Polarization and Ambiguity

We offer a theory of polarization as an optimal response to ambiguity. Suppose individual A's beliefs first-order stochastically dominate individual B's. They observe a common signal. They exhibit polarization if A's posterior dominates her prior and B's prior dominates her posterior. Given agreement on conditional signal likelihoods, we show that polarization is impossible under Bayesian updating or after observing extreme signals. However, we also show that polarization can arise after intermediate signals as ambiguity averse individuals implement their optimal prediction strategies. We explore when this polarization will occur and the logic underlying it. (JEL D81, D82, D83)