Analyzing temporally correlated dolphin sightings data using generalized estimating equations

Many of the statistical techniques commonly used in ecology assume independence among responses. However, there are many marine mammal survey techniques, such as those involving time series or subgroups, which result in correlations within the data. Generalized estimating equations (GEEs) take such correlations into account and are an extension of generalized linear models. This study demonstrates the application of GEEs by modeling temporal variation in bottlenose dolphin presence from sightings data. Since dolphins could remain in the study area for several hours resulting in temporal autocorrelation, an autoregressive correlation structure was used within the GEE, each cluster representing hours within a day of survey effort. The results of the GEE model showed that there was significant diel, tidal, and interannual variation in the presence of dolphins. Dolphins were most likely to be seen in the early morning and during the summer months. Dolphin presence generally peaked during low tide, but this varied among years. There was a significantly lower probability of dolphins being present in 2003 than 2004, but not between 2004 and the other years (1991, 1992, and 2002). GEE‐model fitting packages are now readily available, making this a valuable, versatile tool for marine mammal biologists.     

A genetic association test through combining two independent tests

Gene- and pathway-based variant association tests are important tools in finding genetic variants that are associated with phenotypes of interest. Although some methods have been proposed in the literature, powerful and robust statistical tests are still desirable in this area. In this study, we propose a statistical test based on decomposing the genotype data into orthogonal parts from which powerful and robust independent p-value combination approaches can be utilized. Through a comprehensive simulation study, we compare the proposed test with some existing popular ones. Our simulation results show that the new test has great performance in terms of controlling type I error rate and statistical power. Real data applications are also conducted to illustrate the performance and usefulness of the proposed test.     

A simple test of association for contingency tables with multiple column responses

Loughin and Scherer (1998, Biometrics 54, 630-637) investigated tests of association in two-way tables when one of the categorical variables allows for multiple-category responses from individual respondents. Standard chi-squared tests are invalid in this case, and they developed a bootstrap test procedure that provides good control of test levels under the null hypothesis. This procedure and some others that have been proposed are computationally involved and are based on techniques that are relatively unfamiliar to many practitioners. In this paper, the methods introduced by Rao and Scott (1981, Journal of the American Statistical Association 76, 221-230) for analyzing complex survey data are used to develop a simple test based on a corrected chi-squared statistic.     

Likelihood approach for detecting imprinting and in utero maternal effects using general pedigrees from prospective family-based association studies

Genetic imprinting and in utero maternal effects are causes of parent-of-origin effect but they are confounded with each other. Tests attempting to detect only one of these effects would have a severely inflated type I error rate if the assumption of the absence of the other effect is violated. Some existing methods avoid the potential confounding by modeling imprinting and in utero maternal effect simultaneously. However, these methods are not amendable to extended families, which are commonly recruited in family-based studies. In this article, we propose a likelihood approach for detecting imprinting and maternal effects (LIME) using general pedigrees from prospective family-based association studies. LIME formulates the probability of familial genotypes without the Hardy-Weinberg equilibrium assumption by introducing a novel concept called conditional mating type between marry-in founders and their nonfounder spouses. Further, a logit link is used to model the penetrance. To deal with the issue of incomplete pedigree genotypic data, LIME imputes the unobserved genotypes implicitly by considering all compatible ones conditional on the observed genotypes. We carried out a simulation study to evaluate the relative power and type I error of LIME and two existing methods. The results show that the use of extended pedigree data, even with incomplete information, can achieve much greater power than using nuclear families for detecting imprinting and in utero maternal effects without leading to inflated type I error rates.     

A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing

Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distribution for discrete multivariate SNP data. We propose a composite likelihood approach for parameter estimation. A direct application of this latent variable model is to association testing with multiple SNPs in a candidate gene or region. In contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete SNP data, we can exclusively focus on testing the mean and covariance parameters of the latent Gaussian distributions for cases and controls. Our simulation results demonstrate potential power gains of the proposed approach over some existing methods.     

Personality endophenotypes for bipolar affective disorder: a family-based genetic association analysis

Genetic analyses of complex conditions such as bipolar disorder (BD) may be facilitated by the use of intermediate phenotypes. Various personality traits are overrepresented in people with BD and their unaffected relatives, and may constitute genetically transmitted risk factors or endophenotypes of the illness. In this study, we administered a battery of seven different personality questionnaires comprising 19 subscales to 31 Caucasian BD families (n = 241). Ten of these personality traits showed significant evidence of heritability and were therefore selected as candidate endophenotypes. In addition, a principal components analysis produced two heritable components (negative affect and appetitive drive), which accounted for a considerable proportion of the variance (29% + 12%) and were also used in the analysis. A family-based quantitative association study was carried out using the orthogonal model from the quantitative transmission disequilibrium tests (QTDT) program. Monte Carlo permutations (M = 500), which allow for non-normal data and provide a global P value, corrected for multiple testing, were used to calculate empirical P values for the within-family component of association. The 3' untranslated region repeat polymorphism of the dopamine transporter gene (SLC6A3) was associated with self-directedness (P < 0.0001) and negative affect (P = 0.010). The short allele of the serotonin transporter gene (SLC6A4) promoter polymorphism showed a trend toward association with higher harm avoidance (P = 0.016) and negative affect (P = 0.028). The catechol-o-methyltransferase val158met polymorphism was weakly associated with the personality traits, 'Spirituality' (P = 0.040) and irritable temperament (P = 0.022). Furthermore, the met allele of the brain-derived neurotrophic factor val66met polymorphism was associated with higher hyperthymic temperament scores. We raise the possibility that the 10R allele of the SLC6A3 repeat polymorphism and the short allele of the SLC6A4 promoter variant constitute risk factors for irritable-aggressive and anxious-dysthymic subtypes of BD, respectively.     

An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: A model approach for replication

Prenatal maternal stress exposure has been associated with neonatal differential DNA methylation. However, the available evidence in humans is largely based on candidate gene methylation studies, where only a few CpG sites were evaluated. The aim of this study was to examine the association between prenatal exposure to maternal stress and offspring genome-wide cord blood methylation using different methods. First, we conducted a meta-analysis and follow-up pathway analyses. Second, we used novel region discovery methods [i.e., differentially methylated regions (DMRs) analyses]. To this end, we used data from two independent population-based studies, the Generation R Study (n = 912) and the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 828), to (i) measure genome-wide DNA methylation in cord blood and (ii) extract a prenatal maternal stress composite. The meta-analysis (n_total = 1,740) revealed no epigenome-wide (meta P <1.00e-07) associations of prenatal maternal stress exposure with neonatal differential DNA methylation. Follow-up analyses of the top hits derived from our epigenome-wide meta-analysis (meta P <1.00e-04) indicated an over-representation of the methyltransferase activity pathway. We identified no Bonferroni-corrected (P <1.00e-06) DMRs associated with prenatal maternal stress exposure. Combining data from two independent population-based samples in an epigenome-wide meta-analysis, the current study indicates that there are no large effects of prenatal maternal stress exposure on neonatal DNA methylation. Such replication efforts are essential in the search for robust associations, whether derived from candidate gene methylation or epigenome-wide studies.     

Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm

The narrow genetic base of cultivated cotton germplasm is hindering the cotton productivity worldwide. Although potential genetic diversity exists in Gossypium genus, it is largely ‘underutilized’ due to photoperiodism and the lack of innovative tools to overcome such challenges. The application of linkage disequilibrium (LD)-based association mapping is an alternative powerful molecular tool to dissect and exploit the natural genetic diversity conserved within cotton germplasm collections, greatly accelerating still ‘lagging’ cotton marker-assisted selection (MAS) programs. However, the extent of genome-wide linkage disequilibrium (LD) has not been determined in cotton. We report the extent of genome-wide LD and association mapping of fiber quality traits by using a 95 core set of microsatellite markers in a total of 285 exotic Gossypium hirsutum accessions, comprising of 208 landrace stocks and 77 photoperiodic variety accessions. We demonstrated the existence of useful genetic diversity within exotic cotton germplasm. In this germplasm set, 11–12% of SSR loci pairs revealed a significant LD. At the significance threshold (r² ≥ 0.1), a genome-wide average of LD declines within the genetic distance at < 10 cM in the landrace stocks germplasm and > 30 cM in variety germplasm. Genome wide LD at r² ≥ 0.2 was reduced on average to  1–2 cM in the landrace stock germplasm and 6–8 cM in variety germplasm, providing evidence of the potential for association mapping of agronomically important traits in cotton. We observed significant population structure and relatedness in assayed germplasm. Consequently, the application of the mixed liner model (MLM), considering both kinship (K) and population structure (Q) detected between 6% and 13% of SSR markers associated with the main fiber quality traits in cotton. Our results highlight for the first time the feasibility and potential of association mapping, with consideration of the population structure and stratification existing in cotton germplasm resources. The number of SSR markers associated with fiber quality traits in diverse cotton germplasm, which broadly covered many historical meiotic events, should be useful to effectively exploit potentially new genetic variation by using MAS programs.     

Linkage disequilibrium and association studies in higher plants: Present status and future prospects

During the last two decades, DNA-based molecular markers have been extensively utilized for a variety of studies in both plant and animal systems. One of the major uses of these markers is the construction of genome-wide molecular maps and the genetic analysis of simple and complex traits. However, these studies are generally based on linkage analysis in mapping populations, thus placing serious limitations in using molecular markers for genetic analysis in a variety of plant systems. Therefore, alternative approaches have been suggested, and one of these approaches makes use of linkage disequilibrium (LD)-based association analysis. Although this approach of association analysis has already been used for studies on genetics of complex traits (including different diseases) in humans, its use in plants has just started. In the present review, we first define and distinguish between LD and association mapping, and then briefly describe various measures of LD and the two methods of its depiction. We then give a list of different factors that affect LD without discussing them, and also discuss the current issues of LD research in plants. Later, we also describe the various uses of LD in plant genomics research and summarize the present status of LD research in different plant genomes. In the end, we discuss briefly the future prospects of LD research in plants, and give a list of softwares that are useful in LD research, which is available as electronic supplementary material (ESM) Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

A study of the possible association between adenosine A2A receptor gene polymorphisms and attention‐deficit hyperactivity disorder traits

The adenosine A_2A receptor (ADORA2A) is linked to the dopamine neurotransmitter system and is also implicated in the regulation of alertness, suggesting a potential association with attention‐deficit hyperactivity disorder (ADHD) traits. Furthermore, animal studies suggest that the ADORA2A may influence ADHD‐like behavior. For that reason, the ADORA2A gene emerges as a promising candidate for studying the etiology of ADHD traits. The aim of this study was to examine the relationship between ADORA2A gene polymorphisms and ADHD traits in a large population‐based sample. This study was based on the Child and Adolescent Twin Study in Sweden (CATSS), and included 1747 twins. Attention‐deficit hyperactivity disorder traits were assessed through parental reports, and samples of DNA were collected. Associations between six single nucleotide polymorphisms (SNPs) and ADHD traits were examined, and results suggested a nominal association between ADHD traits and three of these SNPs: rs3761422, rs5751876 and rs35320474. For one of the SNPs, rs35320474, results remained significant after correction for multiple comparisons. These results indicate the possibility that the ADORA2A gene may be involved in ADHD traits. However, more studies replicating the present results are warranted before this association can be confirmed .     

A comparative study of cells in inflammation,EAE and MS using biomedical literature data mining

Summary Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average γ = 2.1) that are commonly found in biological networks.     

A test for preference of association in a color polymorphic poeciliid fish: laboratory study

Synopsis The ecological and evolutionary forces maintaining genetic polymorphism within populations is of continuing interest to evolutionary biologists. Male pygmy swordtails,Xiphophorus pygmaeus, are polymorphic at a Y-linked locus controlling body color. Fish with the + and cp alleles have blue bodies; those with the con allele have bright gold bodies. Male and female fish were tested in the laboratory to determine if there were any preferences for association with groups of male fish based on color. Single test fish were presented with two groups of males of different phenotypes, and times in which the test fish spent in proximity to each group were recorded. Males of different phenotypes showed no differences in preference, nor did phenotype of the males in the groups affect the behavior of the test fish. Females also showed no preference for males of a particular phenotype. Finally, female test fish showed no preference for groups containing a male with the rare phenotype compared to a group containing all males of the same phenotype.     

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.     

Linkage disequilibrium based association mapping of fiber quality traits in <Emphasis Type="Italic">G. hirsutum</Emphasis> L. variety germplasm

Cotton is the world’s leading cash crop, but it lags behind other major crops for marker-assisted breeding due to limited polymorphisms and a genetic bottleneck through historic domestication. This underlies a need for characterization, tagging, and utilization of existing natural polymorphisms in cotton germplasm collections. Here we report genetic diversity, population characteristics, the extent of linkage disequilibrium (LD), and association mapping of fiber quality traits using 202 microsatellite marker primer pairs in 335 G. hirsutum germplasm grown in two diverse environments, Uzbekistan and Mexico. At the significance threshold (r ² ≥ 0.1), a genome-wide average of LD extended up to genetic distance of 25 cM in assayed cotton variety accessions. Genome wide LD at r ² ≥ 0.2 was reduced to ~5–6 cM, providing evidence of the potential for association mapping of agronomically important traits in cotton. Results suggest linkage, selection, inbreeding, population stratification, and genetic drift as the potential LD-generating factors in cotton. In two environments, an average of ~20 SSR markers was associated with each main fiber quality traits using a unified mixed liner model (MLM) incorporating population structure and kinship. These MLM-derived significant associations were confirmed in general linear model and structured association test, accounting for population structure and permutation-based multiple testing. Several common markers, showing the significant associations in both Uzbekistan and Mexican environments, were determined. Between 7 and 43% of the MLM-derived significant associations were supported by a minimum Bayes factor at ‘moderate to strong’ and ‘strong to very strong’ evidence levels, suggesting their usefulness for marker-assisted breeding programs and overall effectiveness of association mapping using cotton germplasm resources. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.