Testing for homogeneity of gametic disequilibrium across strata

Copyright © 2007 Yin et al. Background: Assessing the non-random associations of alleles at different loci, or gametic disequilibrium, can provide clues about aspects of population histories and mating behavior and can be useful in locating disease genes. For gametic data which are available from several strata with different allele probabilities, it is necessary to verify that the strata are homogeneous in terms of gametic disequilibrium. Results: Using the likelihood score theory generalized to nuisance parameters we derive a score test for homogeneity of gametic disequilibrium across several independent populations. Simulation results demonstrate that the empirical type I error rates of our score homogeneity test perform satisfactorily in the sense that they are close to the pre-chosen 0.05 nominal level. The associated power and sample size formulae are derived. We illustrate our test with a data set from a study of the cystic fibrosis transmembrane conductance regulator gene. Conclusion: We propose a large-sample homogeneity test on gametic disequilibrium across several independent populations based on the likelihood score theory generalized to nuisance parameters. Our simulation results show that our test is more reliable than the traditional test based on the Fisher's test of homogeneity among correlation coefficients. © 2007 Yin et al; licensee BioMed Central Ltd.This research was supported by the National Natural Science Foundation of China (Grant Numbers 10431010 and 10701022), National 973 Key Project of China (2007CB311002), NCET-04-0310, EYTP, the Jilin Distinguished Young Scholars Program (Grant Number 20030113) and the Program Innovative Research Team (PCSIRT) in University (#IRT0519). The work of ML Tang was fully supported by a grant from the Research Grant Council of the Hong Kong Special Administration (Project no. HKBU261007)

An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm

Agricultural and rural carbon (ARC) emissions are a major source of greenhouse gas emissions in China and have profound implications for implementing the rural revitalization strategy. This study takes Shandong Province, a leading agricultural province in China, as a case study to explore the relationship between ARC emissions and their influencing factors. It employs the Logarithmic Mean Divisia Index (LMDI) model to decompose changes in ARC emissions from 2000 to 2021, analyzing the contributions of factors such as agricultural production efficiency and agricultural industrial structure. The study then expands the indicator system and applies feature selection methods to identify the main influencing factors. It establishes Bayes model averaging (BMA), STIRPAT-Ridge regression and Long Short-Term Memory (LSTM) models to evaluate their performance in modeling historical ARC emissions. Finally, the study makes prospective forecasts of ARC emissions in Shandong Province from 2022 to 2050 under low, medium and high speed development scenarios. The findings show that from 2000 to 2021, ARC emission intensity decreased by 71.86% in Shandong. Key factors like agricultural production efficiency and agricultural industrial structure exhibited emission reduction effects. Agricultural production efficiency, electricity consumption, agricultural economic level, and transportation travel positively impact ARC emissions, with agricultural production efficiency and electricity consumption as the dominant factors. Under the development high-speed scenario, ARC emissions are projected to peak around 2030. Reducing carbon emissions intensity, improving resource use efficiency and maintaining steady economic growth are crucial for controlling future ARC emissions and achieving sustainable development in Shandong Province