Design Considerations for Massively Parallel Sequencing Studies of Complex Human Disease

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few “true” disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumes the cost of sequencing the whole exome is 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design

Trend-TDT – a transmission/disequilibrium based association test on functional mini/microsatellites

<p>Abstract</p> <p>Background</p> <p>Minisatellites and microsatellites are associated with human disease, not only as markers of risk but also involved directly in disease pathogenesis. They may play significant roles in replication, repair and mutation of DNA, regulation of gene transcription and protein structure alteration. Phenotypes can thus be affected by mini/microsatellites in a manner proportional to the length of the allele. Here we propose a new method to assess the linear trend toward transmission of shorter or longer alleles from heterozygote parents to affected child.</p> <p>Results</p> <p>This test (trend-TDT) performs better than other TDT (Transmission/Disequilibrium Test) type tests, such as TDT_maxand TDT_L/S, under most marker-disease association models.</p> <p>Conclusion</p> <p>The trend-TDT test is a more powerful association test when there is a biological basis to suspect a relationship between allele length and disease risk.</p

The Effects of Seed Size on Hybrids Formed between Oilseed Rape (Brassica napus) and Wild Brown Mustard (B. juncea)

Background : Seed size has significant implications in ecology, because of its effects on plant fitness. The hybrid seeds that result from crosses between crops and their wild relatives are often small, and the consequences of this have been poorly investigated. Here we report on plant performance of hybrid and its parental transgenic oilseed rape (Brassica napus) and wild B. juncea, all grown from seeds sorted into three seed-size categories.[br/] Methodology/Principal Findings : Three seed-size categories were sorted by seed diameter for transgenic B. napus, wild B. juncea and their transgenic and non-transgenic hybrids. The seeds were sown in a field at various plant densities. Globally, small-seeded plants had delayed flowering, lower biomass, fewer flowers and seeds, and a lower thousand-seed weight. The seed-size effect varied among plant types but was not affected by plant density. There was no negative effect of seed size in hybrids, but it was correlated with reduced growth for both parents.[br/] Conclusions : Our results imply that the risk of further gene flow would probably not be mitigated by the small size of transgenic hybrid seeds. No fitness cost was detected to be associated with the Bt-transgene in this study

The effects of environmental disturbances on tumor growth

In this study, the analytic expressions of the steady probability distribution of tumor cells were established based on the steady state solution to the corresponding Fokker-Planck equation. Then, the effects of two uncorrelated white noises on tumor cell growth were investigated. It was found that the predation rate plays the main role in determining whether or not the noise is favorable for tumor growth.Comment: 14 pages, 11 figures. Note: The paper will be published on volume 42 of the Brazilian Journal of Physic

Transmission of Schistosoma japonicum in Marshland and Hilly Regions of China: Parasite Population Genetic and Sibship Structure

The transmission dynamics of Schistosoma japonicum remain poorly understood, as over forty species of mammals are suspected of serving as reservoir hosts. However, knowledge of the population genetic structure and of the full-sibship structuring of parasites at two larval stages will be useful in defining and tracking the transmission pattern between intermediate and definitive hosts. S. japonicum larvae were therefore collected in three marshland and three hilly villages in Anhui Province of China across three time points: April and September-October 2006, and April 2007, and then genotyped with six microsatellite markers. Results from the population genetic and sibling relationship analyses of the parasites across two larval stages demonstrated that, within the marshland, parasites from cattle showed higher genetic diversity than from other species; whereas within the hilly region, parasites from dogs and humans displayed higher genetic diversity than those from rodents. Both the extent of gene flow and the estimated proportion of full-sib relationships of parasites between two larval stages indicated that the cercariae identified within intermediate hosts in the marshlands mostly came from cattle, whereas in the hilly areas, they were varied between villages, coming primarily from rodents, dogs or humans. Such results suggest a different transmission process within the hilly region from within the marshlands. Moreover, this is the first time that the sibling relationship analysis was applied to the transmission dynamics for S. japonicum

Asymptomatic neurocognitive disorders in patients infected by HIV: fact or fiction?

Neurocognitive disorders are emerging as a possible complication in patients infected with HIV. Even if asymptomatic, neurocognitive abnormalities are frequently detected using a battery of tests. This supported the creation of asymptomatic neurocognitive impairment (ANI) as a new entity. In a recent article published in BMC Infectious Diseases, Magnus Gisslén and colleagues applied a statistical approach, concluding that there is an overestimation of the actual problem. In fact, about 20% of patients are classified as neurocognitively impaired without a clear impact on daily activities. In the present commentary, we discuss the clinical implications of their findings. Although a cautious approach would indicate a stricter follow-up of patients affected by this disorder, it is premature to consider it as a proper disease. Based on a review of the data in the current literature we conclude that it is urgent to conduct more studies to estimate the overall risk of progression of the asymptomatic neurocognitive impairment. Moreover, it is important to understand whether new biomarkers or neuroimaging tools can help to identify better the most at risk population

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Rapamycin induces glucose intolerance in mice by reducing islet mass, insulin content, and insulin sensitivity

Rapamycin, a specific inhibitor for mTOR complex 1, is an FDA-approved immunosuppressant for organ transplant. Recent developments have raised the prospect of using rapamycin to treat cancer or diabetes and to delay aging. It is therefore important to assess how rapamycin treatment affects glucose homeostasis. Here, we show that the same rapamycin treatment reported to extend mouse life span significantly impaired glucose homeostasis of aged mice. Moreover, rapamycin treatment of lean C57B/L6 mice reduced glucose-stimulated insulin secretion in vivo and ex vivo as well as the insulin content and beta cell mass of pancreatic islets. Confounding the diminished capacity for insulin release, rapamycin decreased insulin sensitivity. The multitude of rapamycin effects thus all lead to glucose intolerance. As our findings reveal that chronic rapamycin treatment could be diabetogenic, monitoring glucose homeostasis is crucial when using rapamycin as a therapeutic as well as experimental reagent