Flexible Designs for Genomewide Association Studies

Summary .  Genomewide association studies attempting to unravel the genetic etiology of complex traits have recently gained attention. Frequently, these studies employ a sequential genotyping strategy: A large panel of markers is examined in a subsample of subjects, and the most promising markers are genotyped in the remaining subjects. In this article, we introduce a novel method for such designs enabling investigators to, for example, modify marker densities and sample proportions while strongly controlling the family-wise type I error rate. Loss of efficiency is avoided by redistributing conditional type I error rates of discarded markers. Our approach can be combined with cost optimal designs and entails a greater flexibility than all previously suggested designs. Among other features, it allows for marker selections based upon biological criteria instead of statistical criteria alone, or the option to modify the sample size at any time during the course of the project. For practical applicability, we develop a new algorithm, subsequently evaluate it by simulations, and illustrate it using a real data set.     

A new method for motion capture of the scapula using an optoelectronic tracking device: a feasibility study

Optoelectronic tracking systems are rarely used in 3D studies examining shoulder movements including the scapula. Among the reasons is the important slippage of skin markers with respect to scapula. Methods using electromagnetic tracking devices are validated and frequently applied. Thus, the aim of this study was to develop a new method for in vivo optoelectronic scapular capture dealing with the accepted accuracy issues of validated methods.

Eleven arm positions in three anatomical planes were examined using five subjects in static mode. The method was based on local optimisation, and recalculation procedures were made using a set of five scapular surface markers.

The scapular rotations derived from the recalculation-based method yielded RMS errors comparable with the frequently used electromagnetic scapular methods (RMS up to 12.6° for 150° arm elevation). The results indicate that the present method can be used under careful considerations for 3D kinematical studies examining different shoulder movements.     

A simple and accurate method for determination of microsatellite total allele content differences between DNA pools

DNA pooling is a potential tool for the efficient analysis of the large numbers of samples and DNA markers that are necessary for genome-wide association studies. A simple accurate method for measuring total allele differences in comparisons between two pools containing large numbers of DNA samples is presented. This method compares relative peak height differences between electrophoretograms for each allele of a microsatellite. The method was evaluated by the analysis of 11 microsatellite markers and DNA pooled sample sizes of 50, 100, and 200 individual DNA samples from the same number of different subjects. Pools were created from previously individually genotyped subjects and constructed so that the pool comparisons would provide real total allele differences varying from 0% to 55%. Calculated pool differences were then compared with the real total allele differences determined by individual genotyping results. Together over 200 comparisons demonstrated a correlation coefficient of 0.96, which compared favorably with other previous methods of analysis. This method could provide a rapid screen for total allele differences of greater than 10%, a threshold that should be applicable to detecting low relative risk genes in common diseases. Therefore, these studies suggest that DNA pooling could be a useful tool in association studies for the determination of candidate regions for a range of complex genetic diseases.     

Retrospective versus prospective score tests for genetic association with case‐control data

Since the seminal work of Prentice and Pyke, the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case‐control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case‐control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common‐random‐effect assumption, which assumes a common random effect for all subjects. We develop the locally most powerful aggregation test based on the retrospective likelihood under an independent‐random‐effect assumption, which allows the genetic effect to vary among subjects. In contrast to the fact that disease prevalence information cannot be used to improve efficiency for the estimation of odds ratio parameters in logistic regression models, we show that it can be utilized to enhance the testing power in genetic association studies. Extensive simulations demonstrate the advantages of the proposed method over the existing ones. A real genome‐wide association study is analyzed for illustration.     

Argentine population genetic structure: large variance in Amerindian contribution

Argentine population genetic structure was examined using a set of 78 ancestry informative markers (AIMs) to assess the contributions of European, Amerindian, and African ancestry in 94 individuals members of this population. Using the Bayesian clustering algorithm STRUCTURE, the mean European contribution was 78%, the Amerindian contribution was 19.4%, and the African contribution was 2.5%. Similar results were found using weighted least mean square method: European, 80.2%; Amerindian, 18.1%; and African, 1.7%. Consistent with previous studies the current results showed very few individuals (four of 94) with greater than 10% African admixture. Notably, when individual admixture was examined, the Amerindian and European admixture showed a very large variance and individual Amerindian contribution ranged from 1.5 to 84.5% in the 94 individual Argentine subjects. These results indicate that admixture must be considered when clinical epidemiology or case control genetic analyses are studied in this population. Moreover, the current study provides a set of informative SNPs that can be used to ascertain or control for this potentially hidden stratification. In addition, the large variance in admixture proportions in individual Argentine subjects shown by this study suggests that this population is appropriate for future admixture mapping studies.     

An analysis of linkage disequilibrium in the interleukin-1 gene cluster, using a novel grouping method for multiallelic markers.

In population- and family-based association studies, it is useful to have some knowledge of the patterns of linkage disequilibrium that exist between markers in candidate regions. When such studies are carried out with multiallelic markers, it is often convenient to group the alleles into a biallelic system, for analysis. In this study, we specifically examined the interleukin-1 (IL-1) gene cluster on chromosome 2, a region containing candidates for many inflammatory and autoimmune disorders. Data were collected on eight markers, four of which were multiallelic. Using these data, we investigated the effect of three allele-grouping strategies, including a novel method, on the detection of linkage disequilibrium. The novel approach, termed the "delta method," measures the deviation from the expected haplotype frequencies under linkage equilibrium, for each allelic combination. This information is then used to group the alleles, in an attempt to avoid the grouping together of alleles at one locus that are in opposite disequilibrium with the same allele at the second locus. The estimate haplotype frequencies (EH) program was used to estimate haplotype frequencies and the disequilibrium measure. In our data it was found that the delta method compared well with the other two strategies. Using this method, we found that there was a reasonable correlation between disequilibrium and physical distance in the region (r=-.540, P=.001, one-tailed). We also identified a common, eight-locus haplotype of the IL-1 gene cluster.     

Two-stage designs for gene-disease association studies with sample size constraints

Gene-disease association studies based on case-control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two-stage method to identify disease-susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Using simulations we show that, when the markers are independent and when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. The power of the two-stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease-susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations.     

Cost-effective designs for linkage disequilibrium mapping of complex traits

The current development of densely spaced collections of single nucleotide polymorphisms (SNPs) will lead to genomewide association studies for a wide range of diseases in many different populations. Determinations of the appropriate number of SNPs to genotype involve a balancing of power and cost. Several variables are important in these determinations. We show that there are different combinations of sample size and marker density that can be expected to achieve the same power. Within certain bounds, investigators can choose between designs with more subjects and fewer markers or those with more markers and fewer subjects. Which designs are more cost-effective depends on the cost of phenotyping versus the cost of genotyping. We show that, under the assumption of a set cost for genotyping, one can calculate a "threshold cost" for phenotyping; when phenotyping costs per subject are less than this threshold, designs with more subjects will be more cost-effective than designs with more markers. This framework for determining a cost-effective study will aid in the planning of studies, especially if there are choices to be made with respect to phenotyping methods or study populations.     

Metabolic syndrome in rheumatic diseases: epidemiology,pathophysiology, and clinical implications

Subjects with metabolic syndrome–a constellation of cardiovascular risk factors of which central obesity and insulin resistance are the most characteristic–are at increased risk for developing diabetes mellitus and cardiovascular disease. In these subjects, abdominal adipose tissue is a source of inflammatory cytokines such as tumor necrosis factor-alpha, known to promote insulin resistance. The presence of inflammatory cytokines together with the well-documented increased risk for cardiovascular diseases in patients with inflammatory arthritides and systemic lupus erythematosus has prompted studies to examine the prevalence of the metabolic syndrome in an effort to identify subjects at risk in addition to that conferred by traditional cardiovascular risk factors. These studies have documented a high prevalence of metabolic syndrome which correlates with disease activity and markers of atherosclerosis. The correlation of inflammatory disease activity with metabolic syndrome provides additional evidence for a link between inflammation and metabolic disturbances/vascular morbidity.     

Focus: The Science of Stress: Virtual Reality Based Active Shooter Training Drill Increases Salivary and Subjective Markers of Stress

Law enforcement personnel are required to respond to a variety of dangerous, potentially life-threatening high stress scenarios. Virtual reality (VR)-based training has been shown to attenuate stress responses; however, little is known about the acute stress response from VR exposure. This study examined the impact of participating in a VR-based active shooter training drill (ASD) on markers of physiological stress as well as potential differences in men and women. To examine the impact of participation in a ~50 sec VR-based ASD, 29 subjects (n = 29; 17 males, 12 females) participated in a quasi-experimental single group design. Saliva samples were collected and analyzed from 27 of the 29 subjects a total of four times 1) 30-min prior to, 2) 5-min prior to, 3) 5-min after, and 4) 30-min after the ASD and analyzed for α-amylase (AA) activity and concentrations of secretory immunoglobulin-A (SIgA), cortisol (CORT), and uric acid (UA). Participation in the ASD resulted in a significant (p < 0.05) increase in salivary stress markers AA and SIgA. In addition, lower concentrations of CORT and UA were found in women compared to men. These findings have implications for law enforcement and/or military personnel that may seek to implement a VR-based training into their training regimen. Future studies should investigate the impact of longitudinal participation in ASD interventions to determine if this is an effective training method to reduce stress responses to real life active shooter training drills.     

Dissection of complex traits in forest trees — opportunities for marker-assisted selection

Due to their long reproductive cycles and the time to expression of mature traits, marker-assisted selection is particularly attractive for tree breeding. In this review, we discuss different approaches used for developing markers and propose a method for application of markers in low linkage disequilibrium (LD) populations. Identification of useful markers for application in tree breeding is mainly based on two approaches, quantitative trait locus (QTL) mapping and association genetic studies. While several studies have identified significant markers, effect of the individual markers is low making it difficult to utilize them in breeding programs. Recently, genomic selection (GS) was proposed for overcoming some of these difficulties. In GS, high density markers are used for predicting phenotypes from genotypes. Currently small effective populations with high LD are being tested for GS in tree breeding. For wider application, GS needs to be applied in low LD populations which are found in many tree breeding programs. Here we propose an approach in which the significant markers from association studies may be used for developing prediction models in low LD populations using the same methods as in GS. Preliminary analyses indicate that a modest numbers of markers may be sufficient for developing prediction models in low LD populations. GS based on large numbers of random markers or small numbers of associated markers is poised to make marker-assisted selection a reality in forest tree breeding.     

Individual DNA identification from ancient human remains.

Individual identification of ancient human remains is one of the most fundamental requisites for studies of paleo-population genetics, including kinship among ancient people, intra- and interpopulation structures in ancient times, and the origin of human populations. However, knowledge of these subjects has been based mainly on circumstantial archaeological evidence for kinship and intrapopulation structure and on genetic studies of modern human populations. Here we describe individual identification of ancient humans by using short-nucleotide tandem repeats and mtDNAs as genetic markers. The application of this approach to kinship analysis shows clearly the presence or absence of kinship among the ancient remains examined.     

Semiparametric methods for evaluating risk prediction markers in case-control studies

The performance of a well-calibrated risk model for a binary disease outcome can be characterized by the population distribution of risk and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of risk, since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the disease outcome. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric methods that accommodate case-control data. The semiparametric methods are flexible, and naturally generalize methods previously developed for cohort data. Applications to prostate cancer risk prediction markers illustrate the methods.     

Genetic mapping based on radiation hybrid data.

Radiation hybrid mapping is a recently developed technique for constructing high-resolution maps of mammalian chromosomes. The chromosome of interest is exposed to X rays, which cause it to break into numerous fragments. The fragments are randomly recovered into hamster cells. The resulting hybrids are then analyzed for the presence or absence of DNA markers of interest. A simple method for using this information to order the markers on the chromosome is developed here. For any particular ordering of the loci, the least number of breaks consistent with the data can be determined. The best order is taken to be that which minimizes this number of obligatory breaks. Some statistical properties of the method are examined, both theoretically and by use of a simulation study. Except for some extreme cases, the method gives good results, which compare well with results obtained by a full maximum likelihood analysis. The method is used to order a set of 14 loci on chromosome 21 with encouraging results.     

Nonparametric tests of association of multiple genes with human disease

The genetic basis of many common human diseases is expected to be highly heterogeneous, with multiple causative loci and multiple alleles at some of the causative loci. Analyzing the association of disease with one genetic marker at a time can have weak power, because of relatively small genetic effects and the need to correct for multiple testing. Testing the simultaneous effects of multiple markers by multivariate statistics might improve power, but they too will not be very powerful when there are many markers, because of the many degrees of freedom. To overcome some of the limitations of current statistical methods for case-control studies of candidate genes, we develop a new class of nonparametric statistics that can simultaneously test the association of multiple markers with disease, with only a single degree of freedom. Our approach, which is based on U-statistics, first measures a score over all markers for pairs of subjects and then compares the averages of these scores between cases and controls. Genetic scoring for a pair of subjects is measured by a "kernel" function, which we allow to be fairly general. However, we provide guidelines on how to choose a kernel for different types of genetic effects. Our global statistic has the advantage of having only one degree of freedom and achieves its greatest power advantage when the contrasts of average genotype scores between cases and controls are in the same direction across multiple markers. Simulations illustrate that our proposed methods have the anticipated type I-error rate and that they can be more powerful than standard methods. Application of our methods to a study of candidate genes for prostate cancer illustrates their potential merits, and offers guidelines for interpretation.     

A joint modeling approach for analyzing marker data in the presence of a terminal event

In many medical studies, markers are contingent on recurrent events and the cumulative markers are usually of interest. However, the recurrent event process is often interrupted by a dependent terminal event, such as death. In this article, we propose a joint modeling approach for analyzing marker data with informative recurrent and terminal events. This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent, and terminal events. Estimation procedures are developed for the model parameters and the degree of dependence, and a prediction of the covariate‐specific cumulative markers is provided. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is illustrated.     

Genetic markers in population studies of atlantic salmon Salmo salar L: analysis of DNA sequences

The review, which consist of two parts, summarizes literature data on all genetic markers used in population studies of Atlantic salmon. the second part of the review concerns analysis of DNA sequences: fragments of known genes, anonymous genome sequences, mini- and microsatellites, mitochondrial DNA. The main results of studies of the Atlantic salmon gene poll using DNA markers are discussed. Most of the markers examined in certain conditions may be under selection. The resolution power of various methods of DNA analysis and the fields of their use are considered in reference to Atlantic salmon.     

Admixture studies in Latin America: from the 20th to the 21st century

The present study is a review of admixture studies in Latin America, an interesting subject because of the unique history of the area, in which populations from 3 different origins had contact and intercrossed. The most often used methods of analysis of admixture in Latin America and some problems related to them, such as the determination of the parental populations and selection of genetic markers, are briefly reviewed. Several sources of data for admixture studies (surnames, quantitative traits, proteins, and molecular information) are summarized. The results obtained using protein systems and blood groups, the most often used markers in Latin America, are considered. They are classified according to their application in 3 groups of populations: urban centers, native Americans, and African-descended subjects. The data show that almost every population is dihybrid or trihybrid, and when African influence is not detected, it is probably due more to the method than to an absence of that contribution. A special section is dedicated to the direction of gene flow, and results about directional mating based on mtDNA, Y-chromosome, and nuclear DNA or proteins are also given. From these studies it is possible to conclude that Amerindian admixture came mainly from female lineages, but it is difficult to establish what happened with the African contribution. A last subject considered is the relation between interethnic crosses and diseases; it is easy to analyze that relation when the pathological condition is related to a unique allele, but when complex diseases are considered, the results are not as clear because of the influence of nongenetic factors. Finally, the perspectives for admixture studies in the 21st century are considered, and some attempts to predict their future in Latin America are made.     

Genetic markers in population studies of Atlantic salmon <Emphasis Type="Italic">Salmo salar</Emphasis> L.: Analysis of DNA sequences

The review, which consist of two parts, summarizes literature data on all genetic markers used in population studies of Atlantic salmon. The second part of the review concerns analysis of DNA sequences; fragments of known genes, anonymous genome sequences, mini-and microsatellites, mitochondrial DNA. The main results of studies of the Atlantic salmon gene poll using DNA markers are discussed. Most of the markers examined in certain conditions may be under selection. The resolution power of various methods of DNA analysis and the fields of their use are considered in reference to Atlantic salmon.     

Isolation of CD45RO+, memory T cells recognizing proteolipid protein from neurologically normal subjects.

Previous studies provide evidence for in vivo activation of MBP-reactive T cells in subjects with multiple sclerosis. In general, in vivo activation occurs less frequently in healthy control subjects. In the current study we examined the T cell response to proteolipid protein in PBMC isolated from 9 control subjects. We used CD45 isotypes as markers for memory and na?ve T cells to assess in vivo activation of CD4+ T cells reactive with PLP. In contrast to the results obtained using MBP, we found that approximately 50% of PLP-reactive T cells were derived from the CD45RO+ memory subpopulation of T cells isolated from these control subjects. These results indicate that some myelin-reactive T cells have undergone activation in vivo in neurologically intact individuals. This suggests that immunoregulatory mechanisms may be present that prevent overt disease in spite of in vivo activation of PLP-reactive T cells.