Patient-derived xenotransplantation models of human myeloid diseases including acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms are essential for studying the biology of the diseases in pre-clinical studies. However, few studies have used these models for comparative purposes. Previous work has shown that acute myeloid leukemia blasts respond to human hematopoietic cytokines whereas myelodysplastic syndrome cells do not. We compared the engraftment of acute myeloid leukemia cells and myelodysplastic syndrome cells in NSG mice to that in NSG-S mice, which have transgene expression of human cytokines. We observed that only 50% of all primary acute myeloid leukemia samples (n=77) transplanted in NSG mice provided useful levels of engraftment (>0.5% human blasts in bone marrow). In contrast, 82% of primary acute myeloid leukemia samples engrafted in NSG-S mice with higher leukemic burden and shortened survival. Additionally, all of 5 injected samples from patients with myelodysplastic syndrome showed persistent engraftment on week 6; however, engraftment was mostly low (<2%), did not increase over time, and was only transiently affected by the use of NSG-S mice. Co-injection of mesenchymal stem cells did not enhance human myelodysplastic syndrome cell engraftment. Overall, we conclude that engraftment of acute myeloid leukemia samples is more robust compared to that of myelodysplastic syndrome samples and unlike those, acute myeloid leukemia cells respond positively to human cytokines, whereas myelodysplastic syndrome cells demonstrate a general unresponsiveness to them.

With cost-effective high-throughput Single Nucleotide Polymorphism (SNP) arrays now becoming widely available, it is highly anticipated that SNPs will soon become the choice of markers in whole genome screens. This optimism raises a great deal of interest in assessing whether dense SNP maps offer at least as much information as their microsatellite (MS) counterparts. Factors considered to date include information content, strength of linkage signals, and effect of linkage disequilibrium. In the current report, we focus on investigating the relative merits of SNPs vs. MS markers for disease gene localization. For our comparisons, we consider three novel confidence interval estimation procedures based on confidence set inference (CSI) using affected sib-pair data. Two of these procedures are multipoint in nature, enabling them to capitalize on dense SNPs with limited heterozygosity. The other procedure makes use of markers one at a time (two-point), but is much more computationally efficient. In addition to marker type, we also assess the effects of a number of other factors, including map density and marker heterozygosity, on disease gene localization through an extensive simulation study. Our results clearly show that confidence intervals derived based on the CSI multipoint procedures can place the trait locus in much shorter chromosomal segments using densely saturated SNP maps as opposed to using sparse MS maps. Finally, it is interesting (although not surprising) to note that, should one wish to perform a quick preliminary genome screening, then the two-point CSI procedure would be a preferred, computationally cost-effective choice. Genet. Epidemiol.

The goal of this study is to evaluate, compare, and contrast several standard and new linkage analysis methods. First, we compare a recently proposed confidence set approach with MAPMAKER/SIBS. Then, we evaluate a new Bayesian approach that accounts for heterogeneity. Finally, the newly developed software SIMPLE is compared with GENEHUNTER. We apply these methods to several replicates of the Genetic Analysis Workshop 13 simulated data to assess their ability to detect the high blood pressure genes on chromosome 21, whose positions were known to us prior to the analyses. In contrast to the standard methods, most of the new approaches are able to identify at least one of the disease genes in all the replicates considered.

It has been hypothesized that rare variants may hold the key to unraveling the genetic transmission mechanism of many common complex traits. Currently, there is a dearth of statistical methods that are powerful enough to detect association with rare haplotypes. One of the recently proposed methods is logistic Bayesian LASSO for case-control data. By penalizing the regression coefficients through appropriate priors, logistic Bayesian LASSO weeds out the unassociated haplotypes, making it possible for the associated rare haplotypes to be detected with higher powers. We used the Genetic Analysis Workshop 18 simulated data to evaluate the behavior of logistic Bayesian LASSO in terms of its power and type I error under a complex disease model. We obtained knowledge of the simulation model, including the locations of the functional variants, and we chose to focus on two genomic regions in the MAP4 gene on chromosome 3. The sample size was 142 individuals and there were 200 replicates.Despite the small sample size, logistic Bayesian LASSO showed high power to detect two haplotypes containing functional variants in these regions while maintaining low type I errors. At the same time, a commonly used approach for haplotype association implemented in the software hapassoc failed to converge because of the presence of rare haplotypes. Thus, we conclude that logistic Bayesian LASSO can play an important role in the search for rare haplotypes.

Preliminary genome screens are usually succeeded by fine mapping analyses focusing on the regions that signal linkage. It is advantageous to reduce the size of the regions where follow-up studies are performed, since this will help better tackle, among other things, the multiplicity adjustment issue associated with them. We describe a two-step approach that uses a confidence set inference procedure as a tool for intermediate mapping (between preliminary genome screening and fine mapping) to further localize disease loci. Apart from the usual Hardy-Weiberg and linkage equilibrium assumptions, the only other assumption of the proposed approach is that each region of interest houses at most one of the diseasecontributing loci. Through a simulation study with several two-locus disease models, we demonstrate that our method can isolate the position of trait loci with high accuracy. Application of this two-step procedure to the data from the Arthritis Research Campaign National Repository also led to highly encouraging results. The method not only successfully localized a well-characterized trait contributing locus on chromosome 6, but also placed its position to narrower regions when compared to their LOD support interval counterparts based on the same data.

Background Asthma prevalence is increasing worldwide in most populations, likely due to a combination of heritable factors and environmental changes. Curiously, however, some European farming populations are protected from asthma, which has been attributed to their traditional lifestyles and farming practices. Objective We conducted population-based studies of asthma and atopy in the Hutterites of South Dakota, a communal farming population, to assess temporal trends in asthma and atopy prevalence and describe risk factors for asthma. Methods We studied 1325 Hutterites (ages 6–91 years) at two time points from 1996 to 1997 and from 2006 to 2009 using asthma questionnaires, pulmonary function and methacholine bronchoprovocation tests, and measures of atopy. Results The overall prevalence of asthma increased over the 10 to 13 year study period (7.5% to 11.1%, P = 0.049), whereas the overall prevalence of atopy did not change (45.0% to 44.8%, P = 0.95). Surprisingly, the rise in asthma was only among females (5.8% to 11.2%, P = 0.02); the prevalence among males remained largely unchanged (9.4% to 10.9%, P = 0.57). Atopy, which was not associated with asthma risk in 1996 to 1997, was the strongest risk factor for asthma among Hutterites studied in 2006 to 2009 (P = 0.003). Conclusions Asthma has increased over a 10 to 13 year period among Hutterite females and atopy has become a significant risk factor for asthma, suggesting a change in environmental exposures that are either sex-limited or that elicit a sex-specific response.

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective. In this paper, we compared and contrasted five methods that can be used for performing this intermediate step. These methods are: the lod support approach, the generalized estimating equations (GEE) method, the confidence set inference (CSI) procedure, and two bootstrap methods. We compared these methods in terms of the coverage probability and precision of localization of the resulting intervals. Results from a simulation study considering several two-locus models demonstrated that the two bootstrap methods yield intervals with approximately correct coverage. On the other hand, the 1-lod support intervals, and those produced by the GEE method, tend to significantly undercover the trait locus, while the regions obtained by the CSI incline to overcover the gene position. When the observed coverage of the confidence intervals produced by all the methods was held to be the same, those obtained through the CSI procedure displayed a higher ability to localize loci, especially when these loci have a minor contribution to the trait and when the amount of data available for the analysis is relatively small. However, with very large sample sizes, lod support intervals emerged as a winner. Application of the methods to the data from the Arthritis Research Campaign National Repository led to intervals containing the position of a known trait locus for all methods, with the greatest precision achieved by the CSI.

We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants.