bluuebunny/crossref_metadata_2025_split huggingface dataset:
Admixture in Human Populations
Authors: Majumder Partha P. 
Published: September 2014

Abstract
When parental populations give rise to a hybrid population, it is possible to estimate the proportions of admixture from each parental population on the basis of known marker allele frequencies if there has been no genetic drift. Methods include generalized least squares and maximum likelihood. It is also possible to use a measure of genetic similarity. All these methods assume that the admixture is a static, one‐time phenomenon, whereas in reality there is usually a continued, long‐term exchange of genes among populations.
Age‐of‐Onset Estimation
Authors: Siegmund K. 
Published: September 2014

Abstract
Age of onset estimates are crucial to clinical management and designing disease prevention studies. For many diseases, age is the primary risk factor. Age of onset is censored if a person is unaffected. Two different modeling philosophies are possible: to assume a mixture of susceptible and nonsusceptible individuals in the population, or to assume that all individuals are susceptible and would become affected if only they lived long enough. Variability of age of onset as a function of exposures can be modeled through the hazard. Designs possible to estimate age of onset parameters are: case—control family studies, kin—cohort studies, and the study of high‐risk families. Age‐specific penetrances can be estimated using a likelihood, pseudolikelihood, marginal likelihood, weighted score, or the method of moments.
Genetic Markers
Authors: Elston Robert C. 
Published: September 2014

Abstract
Genetic markers are genetic polymorphisms, used for the study of population structure, linkage and association analysis, paternity testing, and forensics, that segregate in a simple Mendelian fashion. They include blood cell antigens, restriction fragment length polymorphisms, minisatellites (variable number of tandem repeat), microsatellites (short tandem repeat), and single nucleotide polymorphisms.
Blood Groups
Authors: Reisner E. 
Published: September 2014

Abstract

The term
blood groups
encompasses the products of genes expressed as molecules on the surface of red cells in humans. They were first investigated for their clinical importance in the transfusion of blood products and then became useful as genetic markers for population genetic studies. Included in the blood groups are the ABO, Rhesus (Rh), MNSs, P, Kell, Duffy, Kidd, Lutheran, and Lewis blood.
Candidate Gene
Authors: Elston Robert C. 
Published: September 2014

Abstract
The “candidate gene” approach presupposes that we have reason to believe that a particular metabolic pathway underlies a disease. We then try to confirm or refute this belief by seeking a disease–marker association or linkage, where the marker is the causative gene, in linkage disequilibrium with the causative gene, or tightly linked but in equilibrium with it.
Case‐Only Gene Mapping
Authors: Weir B. S. , Nielsen D. M. 
Published: September 2014

Abstract
When individuals can be characterized as being affected or unaffected by a disease, marker‐trait association can be addressed by comparing marker frequencies between these two categories. It is also possible to infer linkage or association on the basis of marker data from affected individuals only. When there is linkage disequilibrium a test for Hardy‐Weinberg proportions among marker genotypes in the affected population is a test about marker allele and genotype associations with the disease locus. However, rejection Hardy‐Weinberg at the marker locus in the affected population may simply reflect nonrandom mating in the whole population.
Commingling Analysis
Authors: Mendell Nancy Role , Finch Stephen J. 
Published: September 2014

AbstractCommingling distributions have been proposed as a logical extension to the genetic model for genetic traits that are either polygenic or multifactorial. Mixture models can be used to describe unobserved heterogeneity in many situations. For normal component distributions with equal variance, the EM algorithm can be used to find the maximum likelihood estimates of the parameters. The asymptotic distribution of the likelihood ratio test of the null hypothesis of a single normal distribution is unknown but can be approximated by the parametric bootstrap. A Bayesian approach to this problem can also be taken.
Disease‐Marker Association
Authors: Schaid D. 
Published: September 2014

AbstractDisease–marker association can be due to both genetic and nongenetic causes. The former are either that the marker plays a functional role in the disease process or that it is in close proximity to a disease locus (in linkage disequilibrium with it). Association can also be due to population stratification or admixture. Usual contingency table chi‐square methods assume independence of alleles and/or individuals. In the absence of this independence, it is possible to allow for population heterogeneity by using genomic control (i.e. markers that are unlinked to the disease) or family‐member controls, which provide various degrees of matching on ethnicity. Association studies can also use extended pedigree data provided that allowance is made for the statistical dependence of the members in the same pedigree.
Genetic Heterogeneity
Authors: Olson Jane M. 
Published: September 2014

AbstractThe term genetic heterogeneity refers to the underlying diversity in genetic architecture of different individuals in the population with a common phenotype. Two types of genetic heterogeneity are commonly distinguished: locus heterogeneity in which variants at different loci can predispose to the same phenotype, and allelic heterogeneity in which different variants at the same loci can predispose to the same phenotype. Although allelic heterogeneity is certainly of interest, once a susceptibility locus is identified, the presence of locus heterogeneity so dramatically reduces the power of gene mapping for complex traits that it remains a major statistical challenge. In this article, we primarily discuss models of linkage analysis that allow for locus heterogeneity, including more recent models that allow for heterogeneity‐related covariates.
Genetic Map Functions
Authors: Speed Terry P. 
Published: September 2014

AbstractA genetic map function gives a relationr = M(d) connecting recombination fractionsrand genetic map distancesdbetween pairs of loci along a chromosome. They have been widely used in genetics because (a) genetic map distances are additive, whereas recombination fractions are not; and (b) recombination fractions are much easier to estimate from data. In effect, map functions correct for multiple exchanges in chromosomal intervals during meiosis. The most well‐known map function is that due to Haldane,r = 0.5(1 − exp(−2d)), which arises in the Poisson or no‐ interference model for recombination. Another is associated with the name Kosambi. In general, map functions should be regarded as organism specific, as they embody certain assumptions about recombination, in particular, the nature and extent of genetical interference. However, map functions only consider pairs of loci, and do not necessarily correspond to global models of recombination. While genetical interference has some consequences for map functions, it is not well captured by the associated map function. When multilocus mapping is carried out with a full probability model for recombination, map functions are not needed.
Genetic Risk Ratios
Authors: Kopciuk K. A. , Bull Shelley B. 
Published: September 2014

Abstract
Genetic risk ratios measure the association between known or putative genetic factors and the occurrence of a particular disease. These risk quantifiers are expressed as relative risks (RRs) or odds ratios (ORs). Risk ratios for relatives, such as measures of familial aggregation or recurrence risk in relatives, use family disease history to assess the evidence for an inherited genetic basis for the disease. Allelic, genotype, and haplotype risk ratios evaluate the association of a disease with a measured candidate gene or with one or more genetic markers. Risk ratios estimated under a case–control design may be population‐based or family‐based depending on the type of control group.
Mendel's Laws
Authors: Wijsman Ellen M. 
Published: September 2014

AbstractGregor Mendel's experiments with peas lead to the development of two laws that describe, in quantitative terms, trait distributions in the offspring of a mating. The first, which still forms the fundamental basis behind much of genetics, is the law of segregation. In modern terms, this law states that each individual, as a parent, transmits to an offspring, exactly one randomly chosen allele of the two present at each locus in the individual. The second law, or law of independence, describes the joint behavior of more than one gene. This law states that alleles at one locus segregate independently of alleles at other loci. This proposition is now known to be false for genes on the same chromosome, but still, usually adequately describes the inheritance patterns of multiple genes, other than those on the same chromosome.
Multilocus (gene x gene interaction)
Authors: Cordell Heather J. 
Published: September 2014

AbstractAn important topic in molecular and quantitative genetics is epistasis, or the interaction between genes. There has been much confusion in the literature owing to differing definitions and interpretations of gene–gene interaction. This article reviews some of the most popular definitions used in the literature and notes that the correspondence between statistical and biological interaction effects may be more limited than is sometimes assumed.
Parental Effects
Authors: Fallin Dani , Palmer Lyle J. 
Published: September 2014

Abstract

An individual's risk for a genetic disease or phenotype, conditional on their own genotype, may depend on parental effects such as imprinting, or the genotype or phenotype of a particular parent. For example, under imprinting, a risk‐conferring allele may only be expressed in an individual if it is inherited from a particular parent. Alternatively, a mother's genotype, even if not transmitted to her offspring, may influence
in utero
conditions and increase risk and/or interact with genetic predisposition for particular diseases among those offspring. The existence of such parental effects may lead to incorrect interpretations of the (marginal) effects of particular genes when performing human genetic studies at the individual level, unless such effects are accounted for in the analysis. We discuss potential scenarios of such parental effects, and present an overview of current methods for accommodating these effects when performing segregation analysis, linkage analysis, or genetic association studies.
Pedigrees, Sequential Sampling
Authors: Vieland Veronica 
Published: September 2014

Abstract
Families are frequently selected for genetic epidemiologic study based upon (i) initial ascertainment of (affected) individuals, and (ii) subsequent intrafamilial sampling of (subsets of) relatives of those individuals. Such procedures must be taken into consideration when writing genetic likelihoods, estimating genetic parameters, or performing statistical tests based on ascertained pedigree data.
Penetrance
Authors: Wijsman Ellen M. 
Published: September 2014

Abstract
Penetrance is a probability model that relates, for an individual, the observed phenotype to the underlying genotype. Penetrance functions may be continuous or discrete, may include measured covariates, and may involve more than one locus. All variables not explicitly in the model are assumed to be randomly distributed among individuals, leading to variance in estimates of penetrance, in part because of variability in unmeasured covariates and genetic background among populations.
Segregation Ratios
Authors: Elston Robert C. 
Published: September 2014

Abstract
Whereas genetic transition probabilities refer to the distribution of offspring genotype classes that arise from specific mating types, segregation ratios are the expected ratios of offspring phenotype classes. Typical Mendelian segregation ratios are 1 : 2 : 1 and (in the case of dominance) 3 : 1.
Clustering, Single Linkage
Authors: Massaro Joseph M. 
Published: September 2014

Abstract

Single linkage clustering is used in hierarchical clustering analysis to partition a set of
n
observations into
g
groups or clusters on the basis of
p
variables measured on the
n
observations. The analysis is performed on an
n
×
n
matrix of pairwise distance measures between observations. The procedure starts by combining the two observations with the smallest distance between them. The procedure continues to combine clusters with the smallest distances until only one cluster, including the entire sample, remains. The number of groups,
g
, is determined to represent the final number of clusters to retain.
Biological Standardization
Authors: Gaines Das Rose E. 
Published: September 2014

Abstract
The potency of a biological product usually needs to be assessed in terms of its biological activity rather than in terms of its chemical or physical properties. Biological variability means that this assessment depends on statistical techniques, such as bioassay and on the availability of biological standards, which have known properties. Standards can be local, as might be used within a company for the assessment of a new assay or international standards, which are required for the primary calibration of biological products.
Dilution Method for Bacterial Density Estimation
Authors: Gaines Das Rose E. 
Published: September 2014

Abstract
There are many methods for the measurement of bacterial mass and concentration. Different measures reflect different characteristics of the bacterial mass, and not all measures reflect the number of viable bacteria. If direct counting and characterization of individual bacterial cells is not possible or feasible, indirect methods must be used. Determination of the “most probable number” and serial dilution assays are indirect methods intended to reflect the number of viable bacteria.
Immunotoxicology
Authors: Portier Christopher J. , Germolec Dori 
Published: September 2014

Abstract

Immunotoxicology is the study of the response of the immune system to toxicological insults, which are usually induced by xenobiotics. Experiments are carried out
in vitro
or
in vivo
. These can be
host resistance assays
, which concern the ability of the immune system to protect the host from infectious agents and so on or they can be
immune function assays
, which measure the response of the immune system to a stimulus. The former are largely analyzed using methods for categorical data, whereas the latter usually involve very large counts and these are generally analyzed using methods for continuous data. There is current interest in using immunotoxicity data in risk assessment.
Quality Control in Laboratory Medicine
Authors: Strike P. W. 
Published: September 2014

AbstractAn overview of the principles and practices of Quality Control (QC) in laboratory medicine, encompassing the origins of bias and variability in laboratory measurement, the regulation of same through internal (within‐lab) and external (between‐lab) QC programs, and concluding with an account of the strategies proposed for defining meaningful analytical goals in respect of the “quality” component of QC.
Median Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung 
Published: September 2014

AbstractThe median effective dose (MED) is the median of the tolerance distribution of the dose levels for a stimulus that generates, on average, predefined responses in 50% of experimental units. The median effective dose is usually denoted as ED50. An overview of the design and analysis for inference about ED50is provided. First, two commonly employed tolerance distributions—normal and logistic distributions—are introduced to characterize the relationship between the dose and response. Under a location/scale model for the tolerance distribution, estimation procedures for ED50are reviewed. Interval estimation methods include delta method, the Fieller's theorem, likelihood approach, and Bayesian procedures. The difficulties associated with interval estimation by the Fieller's theorem and likelihood method are addressed. Inference procedures for ED50are also summarized for the situations where (a) the underlying population consists of a mixture of homogeneous subpopulation, (b) the responses of individual experimental units are dependent, and (c) responses are observed at some prespecified time points. Nonparametric methods such as Spearman–Kärber estimators as well as other robust procedures are reviewed. An account of different nonsequential designs for MED under various optimality conditions is given. Finally, a brief summary of sequential procedures and designs is provided.
Minimum Therapeutically Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung 
Published: September 2014

Abstract
The concept and definitions of minimum therapeutic effective dose (MTED) and therapeutic window of a drug are presented. Design and analysis for estimating MTED are overviewed. Design issues such as inclusion of a placebo concurrent control, selection of dose levels, and sample size determination are addressed. Statistical analyses such as the application of analysis of variance and model‐based approach to estimating MTED are summarized. Discussion on internal validity vs. external evidence is also provided.
Slope–Ratio Assay
Authors: Redmond Carol K. 
Published: September 2014

Abstract
Slope‐ratio assays are similar to parallel‐line assays insofar as both are indirect analytical assays, which aim to estimate the potency of a test preparation relative to that of a standard. Slope‐ratio assays are most often encountered in microbiologic applications. As with a parallel‐line assay, the key assumption is that the expected response to a given dose of the test preparation is the same as that which would be obtained from the standard preparation administered at a dose that is ρ times the given dose. As in parallel‐line assays, the parameter ρ is known as the relative potency. Unlike the parallel‐line assay, the expected response, which is almost always continuous, is linear in the dose, not its logarithm. The graphs of the response against dose for test and standard preparation will be straight lines of different slopes and common intercept, as the assumptions of the assay demand the same outcome for standard and test preparations when the dose is zero. The analysis can be accomplished by a multiple regression, with the estimator of ρ being the ratio of the slope estimators. Interval estimates are often obtained using Fieller's theorem.
Stimulus–Response Studies
Authors: T. Morgan Byron J. 
Published: September 2014

AbstractSimple stimulus–response experiments result in estimates of the probability of a hit (getting it right) and probability of a false alarm (getting it wrong), which produce a point on a receiver operating characteristic (ROC) curve. Signal‐detection models convert this information into a single measure of performance. There are several elaborations of the simple experiment. ROC curve methodology has been applied in areas such as ecology, medicine, engineering, and psychology.
Autocorrelation Function
Authors: Rabe‐Hesketh Sophia 
Published: September 2014

Abstract

The correlation between two random variables that are part of a time series is usually known as the
autocorrelation
. A series of such correlations can be found according to the time separation or lag of the two variables. A plot of these correlations against the lag, sometimes known as a correlogram, is often an important tool in identifying a suitable model for the series.
Baseline Adjustment in Longitudinal Studies
Authors: Senn Stephen 
Published: September 2014

Abstract
A baseline variable may be a demographic characteristic such as sex, a pretreatment observation on the variable to be used for measuring outcome, or a pretreatment observation on a variable correlated with the outcome. Adjustment by the analysis of covariance is preferable to the analysis of changes. Topics discussed include testing for baseline balance, adjustment for baselines taken after start of treatment, cut‐off designs, cohort studies, and uncontrolled studies.
Change‐point problem with biostatistical applications
Authors: Pawitan Yudi 
Published: September 2014

AbstractThe change‐point problem is to determine the unknown location τ, based on an ordered sequence of observationsx1, …,xn, such that the two groups of observationsx1, …,xτandxτ+1, …,xnfollow distinct models. In most applications, each model is a regression model, so the overall model is a nonlinear regression model. A commonly used change‐point model is the piecewise linear model. Statistical inference on the change parameter τ is nonstandard, since the likelihood function is not differentiable with respect to τ. The article provides a review of methodology from a general quasi‐likelihood framework, and discusses an application of the change point in breast cancer incidence as a function of age.
D
iggle–
K
enward Model for Dropouts
Authors: Diggle Peter J. 
Published: September 2014

Abstract
Dropouts in longitudinal data analysis occur when some subjects withdraw prematurely from the study. When the processes that govern dropout are related to the measurement process, which is the primary focus of the study, interpretation of the data can be difficult. The Diggle–Kenward model gives a description of the association between the dropout and measurement processes in which completely random, random, or informative dropout models can be obtained by imposing simple and explicit restrictions on the model parameters. Typically, informative dropout parameters are poorly identified, and the associated inferences are therefore sensitive to distributional assumptions.
Distribution‐Free Methods for Longitudinal Data
Authors: Davis Charles S. 
Published: September 2014

AbstractMany of the available methods for analyzing repeated measurements require distributional assumptions on either the joint distribution of a subject's repeated measurements or on the marginal distributions at each occasion. Although such methods are often adequate and appropriate, nonparametric approaches to the analysis of repeated measurements may also be indicated in certain situations. One approach is to use nonparametric summary statistics in order to reduce the vector of responses from each subject to a single measurement. Multivariate nonparametric approaches include generalizations of univariate distribution‐free methods such as the Wilcoxon–Mann–Whitney and Kruskal–Wallis tests, randomization model methods based on the use of Cochran–‐Mantel–Haenszel statistics, asymptotically distribution‐free analogs of parametric procedures for normally distributed outcomes, distribution‐free methods for incomplete repeated measurements based onUstatistics, the rank transform method, and nonparametric regression methods such as kernel estimation, weighted local least squares estimation, and smoothing splines.
Longitudinal Data Analysis, Overview
Authors: Dunn Graham , Pickles Andrew 
Published: September 2014

AbstractThe article reviews several approaches to the analysis of data containing repeated or serial measurements, including data arising from both observational studies and experiments. Examples include fitting growth curves and other forms of trend analysis. Methods for the analysis of quantitative responses include adaptations of analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), and analysis of covariance (ANCOVA) models allowing for autocorrelation. Structural equation models involving latent variables are also explained briefly, as are random effects models. Methods for counts and categorical responses include loglinear models, latent class models, random effects models, and marginal models involving generalized estimating equations.