Wentian Li

Bio: Wentian Li is an academic researcher from Rockefeller University. The author has contributed to research in topics: Population & Akaike information criterion. The author has an hindex of 14, co-authored 20 publications receiving 1489 citations. Previous affiliations of Wentian Li include Santa Fe Institute & Cold Spring Harbor Laboratory.

A complete enumeration and classification of two-locus disease models.

Abstract: There are 512 two-locus, two-allele, two-phenotype, fully penetrant disease models. Using the permutation between two alleles, between two loci, and between being affected and unaffected, one model can be considered to be equivalent to another model under the corresponding permutation. These permutations greatly reduce the number of two-locus models in the analysis of complex diseases. This paper determines the number of nonredundant two-locus models (which can be 102, 100, 96, 51, 50, or 58, depending on which permutations are used, and depending on whether zero-locus and single-locus models are excluded). Whenever possible, these nonredundant two-locus models are classified by their property. Besides the familiar features of multiplicative models (logical AND), heterogeneity models (logical OR), and threshold models, new classifications are added or expanded: modifying-effect models, logical XOR models, interference and negative interference models (neither dominant nor recessive), conditionally dominant/recessive models, missing lethal genotype models, and highly symmetric models. The following aspects of two-locus models are studied: the marginal penetrance tables at both loci, the expected joint identity-by-descent (IBD) probabilities, and the correlation between marginal IBD probabilities at the two loci. These studies are useful for linkage analyses using single-locus models while the underlying disease model is two-locus, and for correlation analyses using the linkage signals at different locations obtained by a single-locus model.

The Structure of the Elementary Cellular Automata Rule Space

Abstract: The structure of the elementary cellular automata rule space is investigated. The probabilities for a rule to be connected to other rules in the same class (intra-class), as well as rules in di erent classes (inter-class), are determined. The intra-class connection probabilities vary from around 0.3 to 0.5, an indication of the strong tendency for rules with the similar behavior to be next to each other. Rules are also grouped according to the meaneld descriptions. The meaneld clusters are classi ed into three classes (nonlinear, linear, and inversely linear) according to the \\hot bits\" in the rule table. It is shown that such classi cation provides another easy way to describe the rule space.

Vitamin D deficiency.

Abstract: admission. This proportion could already be greater in some parts of the country and may increase if referrals of cases of self-poisoning increase faster than the facilities for their assessment and management. The provision of social work and psychiatric expertise in casualty departments may be one means of preventing unnecessary medical admissions without risk to the patients. Dr Blake's and Dr Bramble's figures do not demonstrate, however, that any advantage would attach to medical teams taking over assessment from psychiatrists except that, by implication, assessments would be completed sooner by staff working on the ward full time. What the figures actually suggest is that if assessment of overdoses were left to house doctors there would be an increase in admissions to psychiatric units (by 19°U), outpatients (by 5O°'), and referrals to social services (by 140o). So for house doctors to assess overdoses would provide no economy for the psychiatric or social services. The study does not tell us what the consequences would have been for the six patients who the psychiatrists would have admitted but to whom the house doctors would have offered outpatient appointments. E J SALTER

Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

Abstract: One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.

Detecting gene-gene interactions that underlie human diseases

Abstract: Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

The human obesity gene map: the 2005 update.

Abstract: This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.

An Introduction to Genetic Statistics

Abstract: A reviewer for the Journal of the Royal Statistical Society of England comments \"This is the first book covering in one volume all important topics in genetical statistics.\" Written to provide basic probability ideas in terms of genetic situations, since the theory of genetics is a probability theory; to give a definitive treatment of applications of these ideas to genetic theory; and to describe statistical methods appropriate to the data models that are developed.