A modified association test for rare and common variants based on affected sib-pair design

Current genome-wide association analysis has identified a great number of rare and common variants associated with common complex traits, however, more effective approaches for detecting associations between rare and common variants with common diseases are still demanded. Approaches for detecting rare variant association analysis will compromise the power when detecting the effects of rare and common variants simultaneously. In this paper, we extend an existing method of testing for rare variant association based on affected sib pairs (TOW-sib) and propose a variable weight test for rare and common variants association based on affected sib pairs (abbreviated as VW-TOWsib). The VW-TOWsib can be used to achieve the purpose of detecting the association of rare and common variants with complex diseases. Simulation results in various scenarios show that our proposed method is more powerful than existing methods for detecting effects of rare and common variants. At the same time, the VW-TOWsib also performs well as a method for rare variant association analysis.

Flood vulnerability assessment in the Ili River Basin based on the comprehensive symmetric Kullback-Leibler distance

In vulnerability assessments, accurately determining the indicator weights is essential to ensure the results' precision and reliability. This paper proposes an optimized comprehensive symmetric Kullback-Leibler (K-L) distance weighting method, in which the comprehensive symmetric K-L distance for each indicator is calculated using a grid-based approach, and the normalized distance serves as the weight for each indicator. ArcGIS software was employed to assess the Ili River Basin flood vulnerability as a case study. The results reveal the following: (1) The grid-based optimized symmetric K-L distance method facilitated variable weight processing in the disaster assessments, where it offered a scientific and adaptable approach for indexing and weighting the Ili River Basin flood vulnerability, thus improving both the evaluation accuracy and practicality. (2) The spatial distribution of the Ili River Basin flood vulnerability levels was uneven, with higher vulnerability observed in the northwestern, southwestern, and southeastern regions, and lower vulnerability in the eastern and northeastern areas. Yining County, Yining City, and certain southern regions of Cocodala City were particularly vulnerable due to multiple influencing factors, including the population, economy, and society. These areas require focused attention and preventive measures.