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Biomedical subjects

Shili Lin

Publications and source records attributed to Shili Lin.

24 records · Page 2Linked to original sources
PubMed2003

Linkage analysis with sequential imputation.

Multilocus calculations, using all available information on all pedigree members, are important for linkage analysis. Exact calculation methods in linkage analysis are limited in either the number of loci or the number of pedigree members they can handle. In this article, we propose a Monte Carlo method for linkage analysis based on sequential imputation. Unlike exact methods, sequential imputation can handle large pedigrees with a moderate number of loci in its current implementation. This Monte Carlo method is an application of importance sampling, in which we sequentially impute ordered genotypes locus by locus, and then impute inheritance vectors conditioned on these genotypes. The resulting inheritance vectors, together with the importance sampling weights, are used to derive a consistent estimator of any linkage statistic of interest. The linkage statistic can be parametric or nonparametric; we focus on nonparametric linkage statistics. We demonstrate that accurate estimates can be achieved within a reasonable computing time. A simulation study illustrates the potential gain in power using our method for multilocus linkage analysis with large pedigrees. We simulated data at six markers under three models. We analyzed them using both sequential imputation and GENEHUNTER. GENEHUNTER had to drop between 38-54% of pedigree members, whereas our method was able to use all pedigree members. The power gains of using all pedigree members were substantial under 2 of the 3 models. We implemented sequential imputation for multilocus linkage analysis in a user-friendly software package called SIMPLE.

Genetic Linkage↗
PubMed2003

Nonparametric disequilibrium mapping of functional sites using haplotypes of multiple tightly linked single-nucleotide polymorphism markers.

As the speed and efficiency of genotyping single-nucleotide polymorphisms (SNPs) increase, using the SNP map, it becomes possible to evaluate the extent to which a common haplotype contributes to the risk of disease. In this study we propose a new procedure for mapping functional sites or regions of a candidate gene of interest using multiple linked SNPs. Based on a case-parent trio family design, we use expectation-maximization (EM) algorithm-derived haplotype frequency estimates of multiple tightly linked SNPs from both unambiguous and ambiguous families to construct a contingency statistic S for linkage disequilibrium (LD) analysis. In the procedure, a moving-window scan for functional SNP sites or regions can cover an unlimited number of loci except for the limitation of computer storage. Within a window, all possible widths of haplotypes are utilized to find the maximum statistic S* for each site (or locus). Furthermore, this method can be applied to regional or genome-wide scanning for determining linkage disequilibrium using SNPs. The sensitivity of the proposed procedure was examined on the simulated data set from the Genetic Analysis Workshop (GAW) 12. Compared with the conventional and generalized TDT methods, our procedure is more flexible and powerful.

Algorithms↗
PubMed2003

Information gain for genetic parameter estimation with incorporation of marker data.

Genetic marker data has been increasingly incorporated into segregation analysis, as combined segregation and linkage analysis has been performed more frequently. In this article, we study the extent of information gains with incorporation of marker data in segregation analysis, a topic that has not been investigated rigorously. Specifically, the current study is to investigate the influence of marker data on genetic model parameter estimation. A variance matrix criterion (as the inverse of the Fisher information matrix) and a relative entropy criterion (a measure of flatness of expected log-likelihood surface) are used to quantify the information gains. Our results indicate that substantial information gain can be achieved with the incorporation of marker data. The amount of variance reduction increases as the heterozygosity of the linked marker increases and as the trait gets closer to the linked marker(s). Incorporation of marker data in larger pedigrees also yields greater information gains based on both criteria. The effect of pedigree structure is also studied.

Data Interpretation, Statistical↗
PubMed2002

Clinical presentations of naturally occurring canine seizures: similarities to human seizures.

The clinical presentations of 119 canine seizures from 41 Standard Poodles and 11 Dalmatians were classified according to a modified version of the International League Against Epilepsy (ILAE) seizure classification system. Standardized use of the ILAE system with dogs not only should facilitate research in veterinary medicine, which has no standard criteria for seizure classification, but also should facilitate comparisons between canine and human seizures. We found that for more than 80% of both breeds, at least some of their seizures had partial onsets. However, because it was common for partial seizures to secondarily generalize, the majority of Poodles (81%) and Dalmatians (91%) experienced at least some generalized seizures. Among partial seizures, complex partial were more frequent than simple partial. For both breeds, two thirds of those with partial onset seizures had exclusively complex partial. Among dogs with primary or secondarily generalized seizures, 80% of both breeds had tonic-clonic seizures.

Journal Article↗
PubMed2002

Construction of a confidence set of markers for the location of a disease gene using affected sib pair data.

We have previously proposed a confidence set approach for finding tightly linked genomic regions under the setting of parametric linkage analysis. In this article, we extend the confidence set approach to nonparametric linkage analysis of affected sib pair (ASP) data based on their identity-by-descent (IBD) information. Two well-known statistics in nonparametric linkage analysis, the Two-IBD test (proportion of ASPs sharing two alleles IBD), and the Mean test (average number of alleles shared IBD in the ASPs), are used for constructing confidence sets. Some numerical analyses as well as a simulation study were carried out to demonstrate the utility of the methods. Our results show that the fundamental advantages of the confidence set approach in parametric linkage analysis are retained when the method is generalized to nonparametric analysis. Our study on the accuracy of confidence sets, in terms of choice of tests, underlying disease incidence data, and amount of data available, leads us to conclude, among other things, that the Mean test outperforms the Two-IBD test in most situations, with the reverse being true only for traits with small additive variance. Although we describe how to construct confidence sets based on only two familiar tests, one can construct confidence sets similarly using other allele sharing statistics.

Confidence Intervals↗