The Design and Use of Variance Component Models in the Analysis of Human Quantitative Pedigree Data

The use of variance components and multivariate linear models in genetics applications has a long history that dates back to (at least) Fisher's seminal 1918 paper “The correlation between relatives on the supposition of Mendelian inheritance” [Phil. Trans. 52: 399–433]. Although extensions and elaborations of Fisher's insights have been offered in recent times, relatively few studies exist which examine the theoretical and operational properties variance component models possess in complicated genetic analysis settings. In this paper variance component models, as well as some of their properties (e.g., power, efficiency, and sample size considerations) are discussed in the context of each of the following genetic analysis settings: 1. the detection of general polygenic additive and dominance effects; 2. the detection of genetic effects in the presence of environmental effects (and vice versa); 3. the detection of pleiotropic gene action; 4. aspects of the detection of genotype by environment interaction; and 5. sequential tests for general hypotheses framed in the context of settings 1 through 4. Exposition of the proposed methods and results are facilitated through a special emphasis placed on pedigree covariance structure modeling.     

Combining predictors for classification using the area under the receiver operating characteristic curve

No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function-the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case-control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood-based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.     

Evolution of the RNA-dependent RNA polymerase (RdRP) genes: Duplications and possible losses before and after the divergence of major eukaryotic groups

Eukaryotic RNA-dependent RNA polymerases (RdRPs, encoded by RDR genes) play critical roles in developmental regulation, maintenance of genome integrity, and defense against foreign nucleic acids. However, the phylogenetic relationship of RDRs remains unclear. From available genome sequences, we identified 161 putative RDR genes from 56 eukaryotes, ranging from protists to multicellular organisms, including plants, fungi and invertebrate animals, such as nematodes, lancelet and sea anemone. On the other hand, we did not detect RDR homologs in vertebrates and insects, even though RNA interference functions in these organisms. Our phylogenetic analysis of the RDR genes suggests that the eukaryotic ancestor might have had three copies, i.e. RDRα, RDRβ and RDRγ. These three ancient copies were also supported by the patterns of protein sequence motifs. Further duplication events after the divergence of major eukaryotic groups were supported by the phylogenetic analyses, including some that likely occurred before the separation of subgroups within each kingdom. We present a model for a possible evolutionary history of RDR genes in eukaryotes.     

相关个体基因型联合概率分布及在身份鉴定中的应用

从联合父系基因概率出发,得出处在同一代的多个个体的联合基因型概率,讨论两种符合我国国情的家谱图,得到同一家族内第m代独生子女之间的联合基因型概率,相应的方法可用来求多个家族、多代独生子女之间的联合基因型概率．列举了两个案例来说明相关个体的联合基因型概率在身份鉴定中的应用．     

A Likelihood-Based Trait-Model-Free Approach for Linkage Detection of Binary Trait

Summary .  Trait-model-free (or "allele-sharing") approach to linkage analysis is a popular tool in genetic mapping of complex traits, because of the absence of explicit assumptions about the underlying mode of inheritance of the trait. The likelihood framework introduced by Kong and Cox (1997,  American Journal of Human Genetics   61, 1179–1188) allows calculation of accurate p-values and LOD scores to test for linkage between a genomic region and a trait. Their method relies on the specification of a model for the trait-dependent segregation of marker alleles at a genomic region linked to the trait. Here we propose a new such model that is motivated by the desire to extract as much information as possible from extended pedigrees containing data from individuals related over several generations. However, our model is also applicable to smaller pedigrees, and has some attractive features compared with existing models ( Kong and Cox, 1997 ), including the fact that it incorporates information on both affected and unaffected individuals. We illustrate the proposed model on simulated and real data, and compare its performance with the existing approach ( Kong and Cox, 1997 ). The proposed approach is implemented in the program lm_ibdtests within the framework of MORGAN 2.8 ( http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml ).     

Estimating equations for removal data analysis

We consider the problem of estimating a population size from successive catches taken during a removal experiment and propose two estimating functions approaches, the traditional quasi-likelihood (TQL) approach for dependent observations and the conditional quasi-likelihood (CQL) approach using the conditional mean and conditional variance of the catch given previous catches. Asymptotic covariance of the estimates and the relationship between the two methods are derived. Simulation results and application to the catch data from smallmouth bass show that the proposed estimating functions perform better than other existing methods, especially in the presence of overdispersion.     

Estimation of the correlation between nutrient intake measures under restricted sampling

Food frequency questionnaires (FFQs) are commonly used to assess dietary intake in epidemiologic research. To evaluate the FFQ reliability, the commonly used approach is to estimate the correlation coefficient between the data given in FFQ and those in food records (for example, 4-day food records [4DFR]) for nutrients of interest. However, in a dietary intervention study, a criterion for eligibility may be to select participants who have baseline FFQ-measured dietary intake of percent energy from fat above a prespecified quantity. Other instruments, such as the 4DFR, may be subsequently administrated only to eligible participants. Under these circumstances, analysis without adjusting for the restricted population will usually lead to biased estimation of correlation coefficients and other parameters of interest. In this paper, we apply likelihood-based and multiple imputation (MI) methods to accommodate such incomplete data obtained as a result of the study design. A simulation study is conducted to examine finite sample performance of various estimators. We note that both the MI estimate and the maximum likelihood (ML) estimate based on a bivariate-normal model are not sensitive to departures from this normality assumption. This led us to investigate robustness properties of the ML estimator analytically. We present some data analyses from a dietary assessment study from the Women's Health Initiative to illustrate the methods.