Genetic association mapping under founder heterogeneity via weighted haplotype similarity analysis in candidate genes

Taking advantage of increasingly available high-density single nucleotide polymorphism (SNP) markers within genes and across genomes, more and more genetic association studies began to use multiple closely linked markers in candidate genes. A practical analytical challenge arising in such studies is the possibility that not all case chromosomes have inherited disease-causing mutations from a common ancestral chromosome (founder heterogeneity). To alleviate the problem, we propose a method that applies a clustering algorithm to haplotype similarity analysis. The method identifies a sequence of nested subsets of case chromosomes by a peeling procedure, where each subset is relatively homogeneous. The average similarity score estimated from each subset in the sequence is compared to that estimated in controls, and a raw (unadjusted for multiple comparisons) P value is obtained. The test for the association between the trait and the candidate gene is based on the minimum raw P value observed in the comparison sequence, with its significance level estimated by a permutation procedure. The method can be applied to both haplotype and genotype data. Simulation studies suggest that our method has the correct type I error rate, and is generally more powerful than existing methods of haplotype similarity analysis.     

Association mapping by generalized linear regression with density-based haplotype clustering

Haplotypes of closely linked single-nucleotide polymorphisms (SNPs) potentially offer greater power than individual SNPs to detect association between genetic variants and disease. We present a novel approach for association mapping in which density-based clustering of haplotypes reduces the dimensionality of the general linear model (GLM)-based score test of association implemented in the HaploStats software (Schaid et al. [2002] Am. J. Hum. Genet. 70:425-434). A flexible haplotype similarity score, a generalization of previously used measures, forms the basis, for grouping haplotypes of probable recent common ancestry. All haplotypes within a cluster are assigned the same regression coefficient within the GLM, and evidence for association is assessed with a score statistic. The approach is applicable to both binary and continuous trait data, and does not require prior phase information. Results of simulation studies demonstrated that clustering enhanced the power of the score test to detect association, under a variety of conditions, while preserving valid Type-I error. Improvement in performance was most dramatic in the presence of extreme haplotype diversity, while a slight improvement was observed even at low diversity. Our method also offers, for binary traits, a slight advantage in power over a similar approach based on an evolutionary model (Tzeng et al. [2006] Am. J. Hum. Genet. 78:231-242).     

Examining the statistical properties of fine-scale mapping in large-scale association studies

Interpretation of dense single nucleotide polymorphism (SNP) follow-up of genome-wide association or linkage scan signals can be facilitated by establishing expectation for the behaviour of primary mapping signals upon fine-mapping, under both null and alternative hypotheses. We examined the inferences that can be made regarding the posterior probability of a real genetic effect and considered different disease-mapping strategies and prior probabilities of association. We investigated the impact of the extent of linkage disequilibrium between the disease SNP and the primary analysis signal and the extent to which the disease gene can be physically localised under these scenarios. We found that large increases in significance (>2 orders of magnitude) appear in the exclusive domain of genuine genetic effects, especially in the follow-up of genome-wide association scans or consensus regions from multiple linkage scans. Fine-mapping significant association signals that reside directly under linkage peaks yield little improvement in an already high posterior probability of a real effect. Following fine-mapping, those signals that increase in significance also demonstrate improved localisation. We found local linkage disequiliptium patterns around the primary analysis signal(s) and tagging efficacy of typed markers to play an important role in determining a suitable interval for fine-mapping. Our findings help inform the interpretation and design of dense SNP-mapping follow-up studies, thus facilitating discrimination between a genuine genetic effect and chance fluctuation (false positive).     

Regression B-spline smoothing in Bayesian disease mapping: with an application to patient safety surveillance

In the context of Bayesian disease mapping, recent literature presents generalized linear mixed models that engender spatial smoothing. The methods assume spatially varying random effects as a route to partially pooling data and 'borrowing strength' in small-area estimation. When spatiotemporal disease rates are available for sequential risk mapping of several time periods, the 'smoothing' issue may be explored by considering spatial smoothing, temporal smoothing and spatiotemporal interaction. In this paper, these considerations are motivated and explored through development of a Bayesian semiparametric disease mapping model framework which facilitates temporal smoothing of rates and relative risks via regression B-splines with mixed-effect representation of coefficients. Specifically, we develop spatial priors such as multivariate Gaussian Markov random fields and non-spatial priors such as unstructured multivariate Gaussian distributions and illustrate how time trends in small-area relative risks may be explored by splines which vary in either a spatially structured or unstructured manner. In particular, we show that with suitable prior specifications for the random effects ensemble, small-area relative risk trends may be fit by 'spatially varying' or randomly varying B-splines. A recently developed Bayesian hierarchical model selection criterion, the deviance information criterion, is used to assess the trade-off between goodness-of-fit and smoothness and to select the number of knots. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risks and to quantify uncertainty. The study offers a disease and health outcome surveillance methodology for flexible and efficient exploration and assessment of emerging risk trends and clustering. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (also known as iatrogenic injuries) among hospitalized elderly patients in British Columbia, Canada.     

上海市长宁区手足口病与气象因素相关性研究

目的分析2012—2015年上海市浦东新区全年的手足口病发病特征,探讨手足口发病气象因素的关系,为制定针对性防控策略提供科学依据。方法收集2012—2015年浦东新区手足口病疫情资料及当地同期气象资料,利用描述性流行病学方法对资料进行描述,并探索二者的相关性。结果2012—2015年浦东新区手足口病累计发病数38 707人,年均发病率为179.21/10万;就全年发病情况看存在明显峰值,发病人群均集中在散居儿童、幼拖儿童和学生,周发病数与平均气温、平均气压等气象因素有较强相关性;“梅雨时节”与非梅雨时节相比,周发病数、平均气温、平均气压等的差异有统计学意义。结论2012—2015年浦东新区手足口病相邻两年呈“一高一低”的发病趋势。“梅雨时节”的手足口病发病特征与全年比较差异有统计学意义,气象因素间的差异可能为重要的影响因素。