Figure S1The genomic landscape of sliding windows significantly associated with Lp(a) levels among AAs on chromosome 6. Seven methods are compared: ACAT-V(1,1), ACAT-V(1,25), SKAT(1,1), SKAT(1,25), Burden(1,1), Burden(1,25) and the omnibus test ACAT-O that combines the other six tests, where the two numbers in the parentheses indicate the choice of the beta(MAF) weight parameters and in the test. A dot means that the sliding window at this location is significant by the method that the color of the dot represents. The numbers on the left of the plot show the number of significant windows identified by each method. Figure S2The genomic landscape of sliding windows significantly associated with Lp(a) levels among EAs on chromosome 6. Seven methods are compared: ACAT-V(1,1), ACAT-V(1,25), SKAT(1,1), SKAT(1,25), Burden(1,1), Burden(1,25) and the omnibus test ACAT-O that combines the other six tests, where the two numbers in the parentheses indicate the choice of the beta(MAF) weight parameters and in the test. A dot means that the sliding window at this location is significant by the method that the color of the dot represents. The numbers on the left of the plot show the number of significant windows identified by each method.
Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes and effect directions of the causal variants and the choices of weights. Here we propose an Aggregated Cauchy Association Test (ACAT), a general, powerful and computationally efficient p-value combination method to boost power in sequencing studies. First, by combining variant-level p-values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of casual variants in a variant set. Second, by combining different variant set-level p-values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests including the burden test, Sequence Kernel Association Test (SKAT) and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and burden test, and that ACAT-O has a substantially more robust and higher power than the alternative tests.
Purpose This study aims to provide a taxonomy of relational benefits that drive customer loyalty in sharing-economy services, assess the relative strengths of these relational benefits in influencing customer loyalty and examine whether commitment mediates the influence of relational benefits on customer loyalty in this context. Design/methodology/approach Relational benefits of sharing-economy services were explored through a focus group interview, followed by an online survey completed by 440 respondents in China. Structural equation modeling was used to test the hypotheses. Findings This study shows that confidence and social benefits have significant and positive effects on commitment in sharing-economy services. In addition, safety benefits, a new type of relational benefits, also significantly affect commitment in this context. Furthermore, the findings suggest that commitment acts as a mediator between confidence, social and safety benefits and customer loyalty. Special treatment benefits had no effect on commitment and loyalty in the sharing-economy context. Practical implications This paper provides sharing-economy service providers with insight on how to better create and sustain loyal relationships with customers through the provision of relational benefits. Originality/value This study offers initial insight into why customers would stay in peer-to-peer relationships in the sharing economy, and suggests how to strengthen relationships between customers and peer service providers.
The anterior pituitary gland drives highly conserved physiologic processes in mammalian species. These hormonally controlled processes are central to somatic growth, pubertal transformation, fertility, lactation, and metabolism. Current cellular models of mammalian anteiror pituitary, largely built on candidate gene based immuno-histochemical and mRNA analyses, suggest that each of the seven hormones synthesized by the pituitary is produced by a specific and exclusive cell lineage. However, emerging evidence suggests more complex relationship between hormone specificity and cell plasticity. Here we have applied massively parallel single-cell RNA sequencing (scRNA-seq), in conjunction with complementary imaging-based single-cell analyses of mRNAs and proteins, to systematically map both cell-type diversity and functional state heterogeneity in adult male and female mouse pituitaries at single-cell resolution and in the context of major physiologic demands. These quantitative single-cell analyses reveal sex-specific cell-type composition under normal pituitary homeostasis, identify an array of cells associated with complex complements of hormone-enrichment, and undercover non-hormone producing interstitial and supporting cell-types. Interestingly, we also identified a Pou1f1-expressing cell population that is characterized by a unique multi-hormone gene expression profile. In response to two well-defined physiologic stresses, dynamic shifts in cellular diversity and transcriptome profiles were observed for major hormone producing and the putative multi-hormone cells. These studies reveal unanticipated cellular complexity and plasticity in adult pituitary, and provide a rich resource for further validating and expanding our molecular understanding of pituitary gene expression programs and hormone production.
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