Retrospective investigation of the origin and epidemiology of the dengue outbreak in Yunnan, China from 2017 to 2018.

Since 2013, a dengue epidemic has broken out in Yunnan, China and neighboring countries. However, after the COVID-19 pandemic in 2019, the number of dengue cases decreased significantly. In this retrospective study, epidemiological and genetic diversity characterizations of dengue viruses (DENV) isolated in Yunnan between 2017 and 2018 were performed. The results showed that the dengue outbreak in Yunnan from 2017 to 2018 was mainly caused by DENV1 (genotype I and genotype V) and DENV2 (Asia I, Asia II, and Cosmopolitan). Furthermore, correlation analysis indicated a significant positive correlation between the number of imported and local cases (correlation coefficient = 0.936). Multiple sequence alignment and phylogenetic divergence analysis revealed that the local isolates are closely related to the isolates from Myanmar and Laos. Interestingly, recombination analysis found that the DENV1 and DENV2 isolates in this study had widespread intra-serotype recombination. Taken together, the results of the epidemiological investigation imply that the dengue outbreak in Yunnan was primarily due to imported cases. This study provides a new reference for further investigations on the prevalence and molecular epidemiology of DENV in Yunnan, China.

A powerful association test for qualitative traits incorporating imprinting effects using general pedigree data.

For qualitative traits and diallelic marker loci, the pedigree disequilibrium test (PDT) based on general pedigrees and its extension (Monte Carlo PDT (MCPDT)) for dealing with missing genotypes are simple and powerful tests for association. There is an increasing interest of incorporating imprinting into association analysis. However, PDT and MCPDT do not take account of the information on imprinting effects in the analysis, which may reduce their test powers when the effects are present. On the other hand, the transmission disequilibrium test with imprinting (TDTI*) combines imprinting into the mapping of association variants. However, TDTI* only accommodates two-generation nuclear families and thus is not suitable for extended pedigrees. In this article, we first extend PDT to incorporate imprinting and propose PDTI for complete pedigrees (no missing genotypes). To fully utilize pedigrees with missing genotypes, we further develop the Monte Carlo PDTI (MCPDTI) statistic based on Monte Carlo sampling and estimation. Both PDTI and MCPDTI are derived in a two-stage framework. Simulation study shows that PDTI and MCPDTI control the size well under the null hypothesis of no association and are more powerful than PDT and TDTI* (based on a sample of nuclear families randomly selecting from pedigrees) when imprinting effects exist.

Detection of parent-of-origin effects for quantitative traits using general pedigree data.

Genomic imprinting is a genetic phenomenon in which certain alleles are differentially expressed in a parent-of-origin-specific manner, and plays an important role in the study of complex traits. For a diallelic marker locus in human, the parentalasymmetry tests Q-PAT(c) with any constant c were developed to detect parent-of-origin effects for quantitative traits. However, these methods can only be applied to deal with nuclear families and thus are not suitable for extended pedigrees. In this study, by making no assumption about the distribution of the quantitative trait, we first propose the pedigree parentalasymmetry tests Q-PPAT(c) with any constant c for quantitative traits to test for parent-of-origin effects based on nuclear families with complete information from general pedigree data, in the presence of association between marker alleles under study and quantitative traits. When there are any genotypes missing in pedigrees, we utilize Monte Carlo (MC) sampling and estimation and develop the Q-MCPPAT(c) statistics to test for parent-of-origin effects. Various simulation studies are conducted to assess the performance of the proposed methods, for different sample sizes, genotype missing rates, degrees of imprinting effects and population models. Simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects and Q-PPAT(c) are robust to population stratification. In addition, the power comparison demonstrates that Q-PPAT(c) and Q-MCPPAT(c) for pedigree data are much more powerful than Q-PAT(c) only using two-generation nuclear families selected from extended pedigrees.

Powerful haplotype-based Hardy-Weinberg equilibrium tests for tightly linked loci.

Recently, there have been many case-control studies proposed to test for association between haplotypes and disease, which require the Hardy-Weinberg equilibrium (HWE) assumption of haplotype frequencies. As such, haplotype inference of unphased genotypes and development of haplotype-based HWE tests are crucial prior to fine mapping. The goodness-of-fit test is a frequently-used method to test for HWE for multiple tightly-linked loci. However, its degrees of freedom dramatically increase with the increase of the number of loci, which may lack the test power. Therefore, in this paper, to improve the test power for haplotype-based HWE, we first write out two likelihood functions of the observed data based on the Niu's model (NM) and inbreeding model (IM), respectively, which can cause the departure from HWE. Then, we use two expectation-maximization algorithms and one expectation-conditional-maximization algorithm to estimate the model parameters under the HWE, IM and NM models, respectively. Finally, we propose the likelihood ratio tests LRT[Formula: see text] and LRT[Formula: see text] for haplotype-based HWE under the NM and IM models, respectively. We simulate the HWE, Niu's, inbreeding and population stratification models to assess the validity and compare the performance of these two LRT tests. The simulation results show that both of the tests control the type I error rates well in testing for haplotype-based HWE. If the NM model is true, then LRT[Formula: see text] is more powerful. While, if the true model is the IM model, then LRT[Formula: see text] has better performance in power. Under the population stratification model, LRT[Formula: see text] is still more powerful. To this end, LRT[Formula: see text] is generally recommended. Application of the proposed methods to a rheumatoid arthritis data set further illustrates their utility for real data analysis.