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PubMed2004

Haplotype sharing correlation analysis using family data: a comparison with family-based association test in the presence of allelic heterogeneity.

The haplotype-sharing correlation (HSC) method for association analysis using family data is revisited by introducing a permutation procedure for estimating region-wise significance at each marker on a study segment. In simulation studies, the HSC method has a correct type 1 error rate in both unstructured and structured populations. The HSC signals on disease segments occur in the vicinity of a true disease locus on a restricted region without recombination hotspots. However, the peak signal may not pinpoint the true disease location in a small region with dense markers. The HSC method is shown to have higher power than single- and multilocus family-based association test (FBAT) methods when the true disease locus is unobserved among the study markers, and especially under conditions of weak linkage disequilibrium and multiple ancestral disease alleles. These simulation results suggest that the HSC method has the capacity to identify true disease-associated segments under allelic heterogeneity that go undetected by the FBAT method that compares allelic or haplotypic frequencies.

Alleles↗
PubMed2005

Testing association and linkage using affected-sib-parent study designs.

We have developed a method for jointly testing linkage and association using data from affected sib pairs and their parents. We specify a conditional logistic regression model with two covariates, one that quantifies association (either direct association or indirect association via linkage disequilibrium), and a second that quantifies linkage. The latter covariate is computed based on expected identity-by-descend (ibd) sharing of marker alleles between siblings. In addition to a joint test of linkage and association, our general framework can be used to obtain a linkage test comparable to the mean test (Blackwelder and Elston [1985] Genet. Epidemiol. 2:85-97), and an association test comparable to the Family-Based Association Test (FBAT; Rabinowitz and Laird [2000] Hum. Hered. 50:211-223). We present simulation results demonstrating that our joint test can be more powerful than some standard tests of linkage or association. For example, with a relative risk of 2.7 per variant allele at a disease locus, the estimated power to detect a nearby marker with a modest level of LD was 58.1% by the mean test (linkage only), 69.8% by FBAT, and 82.5% by our joint test of linkage and association. Our model can also be used to obtain tests of linkage conditional on association and association conditional on linkage, which can be helpful in fine mapping.

Alleles↗
PubMed2002

What does the implicit association test measure? A test of the convergent and discriminant validity of prejudice-related IATs.

Drawing on recent criticism of the Implicit Association Test (IAT), the present study tested the convergent and discriminant validity of two prejudice-related IATs to corresponding explicit prejudice measures in a German student sample (N = 61). Confirming convergent validity, (a) an IAT designed to assess negative associations related to Turkish people was significantly related to the explicit endorsement of prejudiced beliefs about Turkish people, and (b) an IAT designed to assess negative associations related to East Asians was significantly related to explicit prejudice against East Asians. Moreover, confirming discriminant validity, (c) the Asian IAT was unrelated to the explicit endorsement of prejudiced beliefs about Turkish people, and (d) the Turkish IAT was unrelated to explicit prejudice against Asian people. These results further corroborate the assumption that the IAT is a valid method to assess the strength of evaluative associations in the domain of prejudice and stereotypes.

Adult↗
PubMed2003

The reliability of cluster and switch scores for the Controlled Oral Word Association Test.

This study examined the interrater reliability and temporal stability of a scoring system developed by Troyer, Moscovich, and Winocur [Neuropsychologia 11 (1997) 138] to measure clustering and switching on verbal fluency (VF) tasks such as the Controlled Oral Word Association Test (COWAT) [Benton, A.L., Hamsher, K., & Sivan, A.B. (1983). Multilingual aphasia examination (3rd ed.). Iowa City, IA: AJA Associates]. Seven independent raters scored COWAT protocols of 125 healthy participants in accordance with the rules proposed by Troyer et al. Intraclass coefficients were near perfect, ranging from.96 for total number of clusters to.99 for total number of switches. Test-retest reliability coefficients (n=55) were poor to modest (r=.47 for clusters, r=.58 for switches, and r=.70 for total words). Significant improvement in performance was observed across most COWAT indices, suggesting a practice effect. Modifications to test administration are suggested to improve the stability of cluster and switch scores, as well as other variables for further study.

Adult↗
PubMed2002

Using the implicit association test to measure age differences in implicit social cognitions.

Two studies investigated the use of the Implicit Association Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998) to study age differences in implicit social cognitions. Study I collected IAT (implicit) and explicit (self-report) measures of age attitudes, age identity, and self-esteem from young, young-old, and old-old participants. Study 2 collected IAT and explicit measures of attitudes toward flowers versus insects from young and old participants. Results show that the IAT provided theoretically meaningful insights into age differences in social cognitions that the explicit measures did not, supporting the value of the IAT in aging research. Results also illustrate that age-related slowing must be considered in analysis and interpretation of IAT measures.

Adolescent↗
PubMed2001

Mood-induced changes on the Implicit Association Test in recovered depressed patients.

A mood induction paradigm was used to examine dysphoria-related changes in two types of cognitive processing in individuals who had previously experienced depression. Formerly depressed patients (n = 23) and never-depressed controls (n = 27) completed the Dysfunctional Attitudes Scale, a self-report measure of effortful processing, and performed the Implicit Association Test, an automatic-reaction time task that measures evaluative bias, before and after a negative-mood induction. The formerly depressed group showed both an increase in endorsement of dysfunctional attitudes and a more negative evaluative bias for self-relevant information after the induction, relative to controls--however, there was no association between the mood-linked changes observed on these two measures. The shift in evaluative bias shown by the formerly depressed group was similar to that seen in a group of 32 currently depressed individuals. These findings suggest that even a mild negative mood in formerly depressed individuals can reinstate some of the cognitive features observed in depression itself.

Adult↗
PubMed2004

New approach to association testing in case-parent designs under informative parental missingness.

The transmission/disequilibrium test (TDT) and related methods using genotype data on diseased probands and their both parents (triads) have been popular for testing linkage or association between a disease and a candidate gene. The usefulness of the TDT-type approaches lies mainly in their robustness, in the sense that they are valid under population stratification, arbitrary parental genotype distribution, and "informative" parental missingness where the parental missingness may depend on parental genotypes. Recently, a variety of extended TDTs were developed to accommodate parental missingness and to allow for using incomplete triads, including single-offspring-single-parent families (dyads) and single offspring with no parents (monads). However, these methods usually do not preserve the full robustness of the original TDT. In this paper, we propose a new TDT-type approach based on the conditional likelihood of the proband's genotype given the number and, if any, genotypes of the available parents, as well as the proband's phenotype. This new proposal keeps the full robust property of the original TDT. In addition, the new method is very easy to implement, without the need to specify models on parental mating-type probabilities and on parental missingness.

Genetic Linkage↗
PubMed2004

Mapping genes for resistance to Verticillium albo-atrum in tetraploid and diploid potato populations using haplotype association tests and genetic linkage analysis.

Verticillium wilt disease of potato is caused predominantly by Verticillium albo-atrum and V. dahliae. StVe1 -a putative QTL for resistance against V. dahliae -was previously mapped to potato chromosome 9. To develop allele-specific, SNP-based markers within the locus, the StVe1 fragment from a set of 30 North American potato cultivars was analyzed. Three distinct and highly diverse haplotypes can be distinguished at the StVe1 locus. These were detected in 97%, 33%, and 10% of the cultivars analyzed. We tested for haplotype association and for genetic linkage between the StVe1 haplotypes and resistance of tetraploid potato to V. albo-atrum. Moreover, field resistance was assessed in diploid populations with known molecular linkage maps in order to identify novel QTLs. Resistance QTLs against V. albo-atrum were detected on four chromosomes (2, 6, 9, and 12) at the diploid level, with one QTL on chromosome 2 contributing over 40% to the total phenotypic variation of the trait. At the tetraploid level, a significant association between the StVe1-839-C haplotype and susceptibility to the disease was detected, suggesting that resistance-related genes directed against V. albo-atrum and V. dahliae are located in the same genomic region of chromosome 9. However, on the basis of the present analysis, we cannot determine whether these genes are closely linked or if a single gene provides resistance against both Verticillium species. To assess the usefulness of the StVe1-839-C haplotype for marker-assisted selection, we subjected the resistance data to Bayesian analysis, and calculated positive (0.65) and negative (0.75) predictive values, and overall predictive accuracy (0.72). Our results indicate that tagging of additional genes for resistance to Verticillium with molecular markers will be required for efficient marker-assisted selection.

Base Sequence↗
PubMed2005

Family-based association tests for different family structures using pooled DNA.

DNA pooling is a cost-effective strategy for genomewise association studies to identify disease genes. In the context of family-based association studies, Risch & Teng (1998) mainly considered families of identical structures to detect associations between genetic markers and disease, and suggested possible approaches to incorporating different family types without a thorough study of their properties. However, families collected in real genetic studies often have different structures and, more importantly, the informativeness of each family structure depends on the disease model which is generally unknown. So there is a need to develop and investigate statistical methods to combine information from diverse family types. In this article, we propose a general strategy to incorporate different family types by assigning each family an "optimal" weight in association tests. In addition, we consider measurement errors in our analysis. When we evaluate our approach under different disease models and measurement errors, we find that our weighting scheme may lead to a substantial reduction in sample size required over the approach suggested by Risch & Teng (1998), and measurement errors may have significant impact on the required sample size when the error rates are not negligible.

Computer Simulation↗
PubMed2006

Single-attribute implicit association tests (SA-IAT) for the assessment of unipolar constructs. The case of sociosexuality.

A major problem with Implicit Association Tests (IATs) is that they require bipolar attributes (e.g., good-bad). Thus, IAT effects for an attribute category can be interpreted only relative to an opposite category. Problems arise if there is no clear opposite category; in this case, a neutral category can be used, although it induces systematic error variance and thus reduces validity. The present study suggests that this problem can be solved using single-attribute IATs (SA-IATs). Sociosexuality (the tendency to engage in uncommitted sex) was expected to be related at the implicit level to stronger stranger-sex associations relative to partner-sex associations. An IAT was constructed that used conversation as a neutral attribute; it showed satisfactory reliability but only low correlations with explicit sociosexuality. An alternative SA-IAT with sex as the only attribute showed a similar reliability but higher correlations with explicit sociosexuality.

Adolescent↗
PubMed2003

Implicit Association Test: separating transsituationally stable and variable components of attitudes toward gay men.

Implicit attitudes are conceived of as formed in childhood, suggesting extreme stability. At the same time, it has been shown that implicit attitudes are influenced by situational factors, suggesting variability by the moment. In the present article, using structural equation modeling, we decomposed implicit attitudes towards gay men into a person factor and a situational factor. The Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998), introduced as an instrument with which individual differences in implicit attitudes can be measured, was used. Measurement was repeated after one week (Experiment 1) or immediately (Experiment 2). Explicit attitudes towards gay men as assessed by way of questionnaires were positive and stable across situations. Implicit attitudes were relatively negative instead. Internal consistency of the implicit attitude assessment was exemplary. However, the within-situation consistency was accompanied by considerable unexplained between-situation variability. Consequently, it may not be adequate to interpret an individual implicit attitude measured at a given point in time as a person-related, trait-like factor.

Adult↗
PubMed2004

A graph-theoretic approach to testing associations between disparate sources of functional genomics data.

MOTIVATION: The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. RESULTS: We present a graph-theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived predictome and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph-theoretic framework to recast a surprising discrepancy presented elsewhere between gene expression and knockout phenotype, using expression data from a different set of experiments. AVAILABILITY: An R software package, GraphAT, containing the data and statistical procedures is available from Bioconductor: http://www.bioconductor.org.

Algorithms↗
PubMed2004

A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects.

We propose a family-based association test, FBAT-PC, for studies with quantitative traits that are measured repeatedly. The traits may be influenced by partially or completely unknown factors that may vary for each measurement. Using generalized principal component analysis, FBAT-PC amplifies the genetic effects of each measurement by constructing an overall phenotype with maximal heritability. Analytically, and in the simulation studies, we compare FBAT-PC with standard methodology and assess both the heritability of the overall phenotype and the power of FBAT-PC. Compared to univariate analysis, FBAT-PC achieves power gains of up to 200%. Applications of FBAT-PC to an osteoporosis study and to an asthma study show the practical relevance of FBAT-PC. FBAT-PC has been implemented in the software package PBAT and is freely available at http://www.biostat.harvard.edu/~clange/default.htm.

Journal Article↗
PubMed2006

Consequential validity of the implicit association test: comment on Blanton and Jaccard (2006).

Numeric values of psychological measures often have an arbitrary character before research has grounded their meanings, thereby providing what S. J. Messick (1995) called consequential validity (part of which H. Blanton and J. Jaccard now identify as metric meaningfulness). Some measures are predisposed by their design to acquire meanings easily, an example being the sensitivity measure of signal detection theory. Others are less well prepared, illustrated by most self-report measures of self-esteem. Counter to Blanton and Jaccard's characterization, the Implicit Association Test (IAT) has properties that predispose it to acquire consequential validity rapidly. With the IAT as the subject of over 250 publications since 1998, there is now much evidence for its consequential validity. The IAT has attracted more scholarly criticism than have other measures designed for similar purposes. The authors speculate as to why the IAT is an attractive target.

Association↗
PubMed2002

Power calculations for a general class of family-based association tests: dichotomous traits.

Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.

Chi-Square Distribution↗
PubMed2003

A multivariate family-based association test using generalized estimating equations: FBAT-GEE.

In this paper we propose a multivariate extension of family-based association tests based on generalized estimating equations. The test can be applied to multiple phenotypes and to phenotypic data obtained in longitudinal studies without making any distributional assumptions for the phenotypic observations. Methods for handling missing phenotypic information are discussed. Further, we compare the power of the multivariate test with permutation tests and with using separate tests for each outcome which are adjusted for multiple testing. Application of the proposed test to an asthma study illustrates the power of the approach.

Adult↗
PubMed2006

Case-control association tests correcting for population stratification.

In case-control association studies unobserved population stratification may act as a confounder, leading to an increased number of false positive results. Methods accounting for population structure by using additional genetic markers broadly follow one of two concepts: Genomic Control (GC) and Structured Association (SA). While extending existing methods of Structured Association we show that it is necessary to incorporate phenotypic information when inferring population structure, otherwise a systematic bias is introduced. Moreover, for moderate population stratification a Wald test statistic should be preferred as a Structured Association test statistic in comparison to a likelihood ratio test. The introduced extensions are compared to existing methods of Structured Association, as well as to Genomic Control, in a simulation study which is based on realistic situations of large case-control studies with moderate population stratification. A disadvantage of Genomic Control turns out to be the large variation in estimating the variance inflation factor, as well as the power loss if population structure increases. We come to the overall conclusion that Structured Association, if applied correctly, is superior to Genomic Control, at least in the case of simple population structure as simulated here.

Case-Control Studies↗
PubMed2025

Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family-Based Association Testing.

Genotype imputation is a widely-used data augmentation approach that is applied to samples of related and/or unrelated individuals. Association testing may then be carried out on the complete data with commonly-used methods. This approach has typically not accounted for the mix of observed and imputed data, although recent work has noted the potential for introduction of confounding in case-control studies. In the Alzheimer's Disease Sequencing Project family sample we found severe inflation of the test statistics in logistic regression analysis following genotype imputation, even after standard covariate adjustments. Here we dissect sources of this inflation, which is driven by three factors: frequency-dependent bias in imputation-induced allele frequencies, differential measurement error, and differential genotyping rates in cases versus controls that introduces confounding. To address the problem, we propose a statistic, imputation deviance (), which can be easily computed from the observed and imputed genotype probabilities. We show that, as an additional fixed-effect covariate, controls the genome-wide inflation in analysis of this family-based sample, and we speculate that use of imputation deviance may also provide a practical approach to correct for genotype imputation effects in other settings, particularly when a data set is unbalanced and includes related individuals.

Humans↗