Covariate effects on linkage and association using a general pair method

The "general pair method" (GPM) is a nonparametric, identity-by-state (IBS) method of assessing linkage between a chromosomal marker and a binary phenotype. It is applicable to any pedigree structure, and uses marker information from affected as well as unaffected individuals. Results obtained here from nuclear families (Problem 2A) are contrasted with those from extended pedigrees (Problem 2B). Test statistics for chromosomal linkage between each marker and disease status are contrasted with tests for "direct association" which test the hypothesis that a particular allele is associated disease status across all pedigrees. A novel extension of the GPM is presented here for testing whether the strength of linkage (and/or association) depends on the levels of a covariate (i.e., dependency on gender, age, the levels of the "environmental factor," or the levels of the "quantitative phenotypes" supplied). The GPM is seen to have some power to detect major gene 1 on chromosome 5, and major gene 3 on chromosome 4. The gender interaction effects proved too small to detect. No direct associations are found.     

Simple, robust linkage tests for affected sibs

Parametric-linkage analysis applied to large pedigrees with many affected individuals has helped in the identification of highly penetrant genes; but, for diseases lacking a clear Mendelian inheritance pattern or caused by several genes of low to moderate penetrance, a more robust strategy is nonparametric analysis applied to small sets of affected relatives, such as affected sib pairs. Here we show that the robustness of affected-sib-pair tests is related to the shape of the constraint set for the sibs' identity-by-descent (IBD) probabilities. We also derive a set of constraints for the IBD probabilities of affected sib triples and use common features of the shapes of the two constrain sets to introduce new nonparametric tests (called "minmax" tests) that are more robust than those in current use. Asymptotic-power computations support the robustness of the proposed minmax tests.     

Genetic analysis of inflammatory bowel disease in a large European cohort supports linkage to chromosomes 12 and 16

BACKGROUND & AIMS: Inflammatory bowel disease (IBD) is a complex disorder of unknown etiology. Epidemiological investigations suggest a genetic basis for IBD. Recent genetic studies have identified several IBD linkages. The significance of these linkages will be determined by studies in large patient collections. The aim of this study was to replicate IBD linkages on chromosomes 12 and 16 in a large European cohort. METHODS: Three hundred fifty-nine affected sibling pairs from 274 kindreds were genotyped using microsatellite markers spanning chromosomes 12 and 16. Affection status of the sibling pairs was defined as Crohn's disease (CD) or ulcerative colitis (UC). RESULTS: Nonparametric statistical analyses showed linkage for both chromosomes. Two-point results for chromosome 12 peaked at D12S303 (logarithm of odds [LOD], 2.15; P = 0.003) for CD and at D12S75 (LOD, 0.92; P = 0.03) for UC. Multipoint analyses produced a peak LOD of 1.8 for CD. Chromosome 16 showed linkage for CD at marker D16S415 (LOD, 1.52; P = 0.007). Multipoint support peaked above markers D16S409 and D16S411 (LOD, 1.7). CONCLUSIONS: These data are consistent with linkage of IBD to chromosomes 12 and 16. The replication of genetic risk loci in a large independent family collection indicates important and common susceptibility genes in these regions and will facilitate identification of genes involved in IBD.     

Multipoint linkage analysis using affected relative pairs and partially informative markers

Linkage analysis is a method of identifying regions of the human genome harboring genes affecting the risk for a particular disease. It works by finding chromosomal segments inherited by affected relatives from a common ancestor (i.e., identical by descent or IBD) in excess of that expected by chance. Two complicating factors are that only a relatively small number of genomic locations (marker loci) are examined and the number of distinct realizations (alleles) at each marker is not large. Hence, unambiguous determination of IBD is impossible for any genomic location without additional information. Assuming data from a set of mapped, partially informative markers, we evaluate the effectiveness of a method that analyzes the array of markers on each chromosome jointly (multipoint methods) as a function of the informativeness and density of the markers. For the special case of pairs of half siblings whose parents are also typed, a combination of analysis and simulation is used to obtain insight into the problem of setting thresholds to control the false-positive error rate. Approximations are given for the power, and guidelines are developed to help describe the trade-offs between marker density and informativeness.     

Haplotype sharing analysis in affected individuals from nuclear families with at least one affected offspring

In diseases with a complex mode of inheritance, families with multiple affected individuals are difficult to ascertain. The haplotype sharing statistic (HSS) uses (hidden) co-ancestry between affected individuals from a founder population. These affected individuals will likely not only share the same mutation(s), but also the surrounding haplotype. We show that this method gives a low false positive rate, but does not detect genes in the nuclear families of Problem 2A of the GAW data. We also give evidence based on simulations and empirical studies in real population based data that the HSS method has statistical power.     

Is a standard regime for anticoagulation with heparin in unstable angina adequate?

We combined the five chromosome 18 bipolar affective disorder data sets provided by GAW10, totaling 185 families with 3,394 individuals, and performed analysis of differential parental transmission and chromosome 18 marker allele sharing in families with transmission through fathers vs those through mothers. Results indicated a significant excess of maternal transmission of bipolar disorder. All pedigrees were then broken into nuclear families and affected sib-pair linkage analyses performed on the marker, D18S37. There was significant linkage in the data overall, as well as in each subgroup of paternal, maternal and unknown parental transmission nuclear families. There were no significant differences in identical-by-descent (IBD) scores among the three transmission subgroups. These findings support an excess of maternal transmission, and linkage between bipolar disorder and marker D18S37. However, our results do not support the previous suggestion that there are differences in chromosome 18 marker allele sharing depending on the transmitting parent.     

Global, Local, and Graphical Person-Fit Analysis Using Person-Response Functions.

Person-fit statistics test whether the likelihood of a respondent's complete vector of item scores on a test is low given the hypothesized item response theory model. This binary information may be insufficient for diagnosing the cause of a misfitting item-score vector. The authors propose a comprehensive methodology for person-fit analysis in the context of nonparametric item response theory. The methodology (a) includes H. Van der Flier's (1982) global person-fit statistic U3 to make the binary decision about fit or misfit of a person's item-score vector, (b) uses kernel smoothing (J. O. Ramsay, 1991) to estimate the person-response function for the misfitting item-score vectors, and (c) evaluates unexpected trends in the person-response function using a new local person-fit statistic (W. H. M. Emons, 2003). An empirical data example shows how to use the methodology for practical person-fit analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Differences in adult excretory-secretory products between geographical isolates of Echinostoma caproni

BACKGROUND & AIMS: Linkage data derived from genome-wide scans of inflammatory bowel disease (IBD) sibling-pair families have identified 4 loci on chromosomes 3, 7, 12, and 16 as potential sites for IBD susceptibility genes. The aim of this study was to investigate whether linkage analysis of another independently collected set of sibling pairs with IBD would provide further evidence of linkage between these previously reported loci and IBD. METHODS: Using the MAPMAKER/SIBS program, the segregation of 21 microsatellite marker loci spanning the 4 putative IBD gene loci was analyzed in a study population comprising 161 families with 114 Crohn's disease, 36 ulcerative colitis, and 50 mixed IBD sibling pairs from the Greater Toronto area. RESULTS: The results of multipoint linkage analysis showed no evidence for linkage between IBD and each of the 21 marker loci studied; the logarithm of odds scores in all instances were less than 0.8. These linkage data were found, by exclusion mapping analysis, to exclude values of lambdas ranging from 1.5 to 3.0, depending on the locus evaluated. CONCLUSIONS: The loci previously suggested as representing IBD susceptibility loci are not linked to IBD in the Toronto population examined in this analysis.     

The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling

We consider statistics for analyzing a variety of family-based and nonfamily-based designs for detecting linkage disequilibrium of a marker with a disease susceptibility locus. These designs include sibships with parents, sibships without parents, and use of unrelated controls. We also provide formulas for and evaluate the relative power of different study designs using these statistics. In this first paper in the series, we derive statistical tests based on data derived from DNA pooling experiments and describe their characteristics. Although designs based on affected and unaffected sibs without parents are usually robust to population stratification, they suffer a loss of power compared with designs using parents or unrelateds as controls. Although increasing the number of unaffected sibs improves power, the increase is generally not substantial. Designs including sibships with multiple affected sibs are typically the most powerful, with any of these control groups, when the disease allele frequency is low. When the allele frequency is high, however, designs with unaffected sibs as controls do not retain this advantage. In designs with parents, having an affected parent has little impact on the power, except for rare dominant alleles, where the power is increased compared with families with no affected parents. Finally, we also demonstrate that for sibships with parents, only the parents require individual genotyping to derive the TDT statistic, whereas all the offspring can be pooled. This can potentially lead to considerable savings in genotyping, especially for multiplex sibships. The formulas and tables we derive should provide some guidance to investigators designing nuclear family-based linkage disequilibrium studies for complex diseases.     

An empirical study of a proposed test of nonparametric analysis of covariance.

Puri and Sen (1969b) introduced a nonparametric test statistic that, because of its relationship to the general linear model, subsumes many commonly performed hypothesis tests. Following the work of Puri and Sen, Harwell and Serlin (1985) proposed a test of the nonparametric analysis of covariance (ANCOVA) hypothesis. In order to use this statistic, ranks (or some other transformation) are substituted for the original scores. Standard statistical packages can then be used to perform the analysis, and the results of the test are referred to the appropriate reference distribution. The similarity of the rank transformation of Conover and Iman (1981) to this procedure is noted, and the results of a Monte Carlo study investigating the distributional properties (i.e., Type I error rate and power) of the proposed test and other nonparametric analyses of covariance models are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The prion diseases: Creutzfeldt-Jakob, Gerstmann-Str?ussler-Scheinker, and related disorders

Conventional statistical approaches rely heavily on the properties of the central limit theorem to bridge the gap between the characteristics of a sample and some theoretical sampling distribution. Problems associated with nonrandom sampling, unknown population distributions, heterogeneous variances, small sample sizes, and missing data jeopardize the assumptions of such approaches and cast skepticism on conclusions. Conventional nonparametric alternatives offer freedom from distribution assumptions, but design limitations and loss of power can be serious drawbacks. With the data-processing capacity of today's computers, a new dimension of distribution-free statistical methods has evolved that addresses many of the limitations of conventional parametric and nonparametric methods. Computer-intensive statistical methods involve reshuffling, resampling, or simulating a data set thousands of times to empirically define a sampling distribution for a chosen test statistic. The only assumption necessary for valid results is the random assignment of experimental units to the test groups or treatments. Application to a real data set illustrates the advantages of these methods, including freedom from distribution assumptions without loss of power, complete choice over test statistics, easy adaptation to design complexities and missing data, and considerable intuitive appeal. The illustrations also reveal that computer-intensive methods can be more time consuming than conventional methods and the amount of computer code required to orchestrate reshuffling, resampling, or simulation procedures can be appreciable.     

Allelic association between marker loci

Allelic association has proven useful to refine the location of major genes prior to positional cloning, but it is of uncertain value for genome scans in complex inheritance. We have extended kinship theory to give information content for linkage and allelic association. Application to pairs of closely linked markers as a surrogate for marker x oligogene pairs indicates that association is largely determined by regional founders, with little effect of subsequent demography. Sub-Saharan Africa has the least allelic association, consistent with settlement of other regions by small numbers of founders. Recent speculation about substantial advantages of isolates over large populations, of constant size over expansion, and of F1 hybrids over incrosses is not supported by theory or data. On the contrary, fewer affected cases, less opportunity for replication, and more stochastic variation tend to make isolates less informative for allelic association, as they are for linkage.     

Using loss of heterozygosity data in affected pedigree member linkage tests

Linkage analysis can be used to test the hypothesis that a marker locus of known location segregates independently from a presumed disease gene. One way to test this hypothesis is to measure the similarity of marker alleles among pairs of relatives affected with the disease. When the disease under consideration is cancer, it is possible to take advantage of the marker alleles in tumors to revise the similarity measure obtained from the observations made in constitutional tissue. Only cancers that arise through the model of recessive oncogenesis are amenable to this revised analysis. This model postulates that cancer is caused by somatic genetic changes which result in the loss of one or both copies of a normal allele at a tumor suppressor locus. If an individual's inherited genotype is heterozygous at the marker locus, the model of recessive oncogenesis suggests that we may observe loss of constitutional heterozygosity at the marker locus in the tumor. In this report, we how how to incorporate this loss of heterozygosity data into affected pedigree member linkage tests. The revised procedure is illustrated using data obtained from relatives with breast cancer. Substantial improvement in the power to reject the different chromosome hypothesis is obtained when loss of heterozygosity is observed in multiple relatives with the same marker alleles retained in the tumors.     

Computerized documentation for a rural nursing intervention project

Complex traits are generally taken to be under the influence of multiple genes, which may interact with each other to confer susceptibility to disease. Statistical methods in current use for localizing such genes essentially work under single-gene models, either implicitly or explicitly. In genomic screens for complex disease genes, some of the marker loci must be in tight linkage with disease susceptibility genes. We developed a general multi-locus approach to identify sets of such marker loci. Our approach focuses on affected sib pair data and employs a nonparametric pattern recognition technique using artificial neural networks. This technique analyzes all markers simultaneously in order to detect patterns of locus interactions. When applied to previously published sib pair data on type I diabetes, our approach finds the same genes as in the published report in addition to some new loci. For a specific two-locus model of inheritance, the power of our approach is higher than that of the currently used analysis standard.     

Nonparametric one-way multivariate analysis of variance: A computational approach based on the Pillai-Bartlett trace.

Demonstrates a method for computing the omnibus test statistic, which illustrates the relation of nonparametric methods to parametric MANOVA, based on K. Pillai's (1955) and M. Bartlett's (1939) trace statistic. The required test statistic is equal to (N?–?1)V, where V is the trace statistic computed on the transformed data. The test statistic can be obtained by submitting the data, already converted to ranks or normal scores, to a packaged MANOVA program. (25 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hepatic stem cells

Despite advances in the understanding of monogenic hypertensive disorders, the genetic contribution to essential hypertension has yet to be elucidated. The position of tyrosine hydroxylase (TH) as the rate-limiting enzyme in catecholamine biosynthesis renders it a candidate gene for the etiology of hypertension. The TH gene contains an internal, informative microsatellite marker (TCAT)9. We undertook (1) an association study in a group of well-characterized hypertensive subjects (HT) and control subjects (NT) and (2) an affected sibling pair (ASP) study using sibships from our local family practices. Two hundred twenty-seven hypertensive patients (pretreatment systolic/diastolic blood pressure [BP] range, 139/94 to 237/133 mm Hg; age range [SD], 30 to 71 [8.5] years) were age- and gender-matched with 206 control subjects (BP range, 96/62 to 153/86 mm Hg; age range, 40 to 70 [7.6] years). One hundred thirty-six affected sibling pairs were recruited for our linkage study; 73 young borderline hypertensive subjects (YHT) (pretreatment BP range, 123/76 to 197/107 mm Hg; age range, 20 to 51 [9.4] years) were also recruited in whom recent pretreatment norepinephrine and epinephrine levels were available. All subjects were white. The TH short tandem repeat (STR) was amplified using specific polymerase chain reaction cycling conditions in all subjects, and products were run on an ABI 373A sequencer. TH alleles were assigned using Genescan and Genotyper software. Five TH alleles were present and designated A through E. Allele frequencies in the NT population (A, B, C, D, and E: 0.24, 0.17, 0.13, 0.20, and 0.26, respectively) were significantly different from the HT cohort (A, B, C, D, and E: 0.24, 0.19, 0.11, 0.11, and 0.35, respectively), P<0. 0005 (Pearson's test chi2=19.94; 4 df). The E allele appears overrepresented in the HT group, whereas the D allele appears to be overrepresented in the NT group. TH genotype frequencies were also significantly different between cases and controls (P<0.001; chi2=36. 57; 14 df). Both groups were in Hardy-Weinberg proportion. There was a trend (NS) for the D allele to be associated with a lower BP when BP was analyzed as a quantitative trait. ASP linkage data was analyzed using Splink, a nonparametric program. Expected values for sharing 0, 1, and 2 alleles (Z0, Z1, and Z2, respectively) may be expected to be 25%, 50%, and 25%, respectively, by chance (assuming identity by descent). These probabilities were calculated by Splink as 34, 68, and 34, respectively, and compared with observed values of 36.8, 67.9, and 31.3, respectively; thus, there was no excess sharing of TH alleles among affected sibling pairs (P=0.59; logarithm of odds ratio score, 0.0). TH allele frequencies in our YHT group (A, B, C, D, and E: 0.24, 0.20, 0.12, 0.15, and 0.29, respectively) were similar to those of our NT cohort (P>0.05). There was a trend for lower pretreatment plasma norepinephrine levels with the D allele in this YHT cohort. A common and potentially functional variant at codon 81(Val-->Met) within exon 2 of the TH gene (which we show to be in linkage disequilibrium with TH-STR) was also typed in our YHT but did not associate with catecholamine levels and is therefore unlikely to account for our findings with D and E TH-STR. In conclusion, the TH locus strongly associates with essential hypertension in a case-control model using well-characterized hypertensive and control groups. An ASP linkage model was negative, presumably because of lack of power. This study suggests that the TH gene, or a nearby gene, may be involved in the etiology of essential hypertension.     

A simulation study of the effects of assignment of prior identity-by-descent probabilities to unselected sib pairs, in covariance-structure modeling of a quantitative-trait locus

Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.     

Scales and statistics: Parametric and nonparametric.

A comparison of parametric and nonparametric statistics is presented in terms of practical problems and measurement theoretical considerations. Though there is little to choose between the 2 in terms of significance level or power it is stated that parametric procedures, being more versatile, best meet the needs of psychological research. Type of metric scale, ordinal vs. interval, has little relevance to the question of whether to use parametric or nonparametric statistics. From Psyc Abstracts 36:01:3AE05A. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes.

Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of freedom heteroscedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. The authors describe a nonparametric bootstrap methodology that can provide improved Type I error control. In addition, the authors indicate how researchers can set robust confidence intervals around a robust effect size parameter estimate. In an online supplement, the authors use several examples to illustrate the application of an SAS program to implement these statistical methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Seismic Response Control of Building Structures with Superelastic Shape Memory Alloy Wire Dampers

This paper presents a simulation-based benchmark control study in which shape memory alloy (SMA) wire dampers are utilized to control the seismic response of a three-story nonlinear steel frame building. The SMA wire damper uses superelastic Nitinol wires for energy dissipation because of its high fatigue life and large recoverable strain. An analytical model which considers the training effect of SMA wires is used to describe the stress-strain relationship of superelastic SMA wires. The performance of SMA wire dampers is investigated in the framework of the third-generation benchmark problem on structural control. A comparative study of the seismic response control performance of SMA wire dampers with either unprestrained or prestrained SMA wires was also conducted. The results of this simulation-based benchmark control study show that SMA wire dampers, as a passive structure control measure, can effectively reduce the seismic responses of the three-story nonlinear benchmark building structure and has the potential to withstand several design earthquakes without the need for repair.