Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice.

We illustrate, through two case studies, that mean-variance QTL mapping -QTL mapping that models effects on the mean and the variance simultaneously-can discover QTL that traditional interval mapping cannot. Mean-variance QTL mapping is based on the double generalized linear model, which extends the standard linear model used in interval mapping by incorporating not only a set of genetic and covariate effects for mean but also set of such effects for the residual variance. Its potential for use in QTL mapping has been described previously, but it remains underutilized, with certain key advantages undemonstrated until now. In the first case study, a reduced complexity intercross of C57BL/6J and C57BL/6N mice examining circadian behavior, our reanalysis detected a mean-controlling QTL for circadian wheel running activity that interval mapping did not; mean-variance QTL mapping was more powerful than interval mapping at the QTL because it accounted for the fact that mice homozygous for the C57BL/6N allele had less residual variance than other mice. In the second case study, an intercross between C57BL/6J and C58/J mice examining anxiety-like behaviors, our reanalysis detected a variance-controlling QTL for rearing behavior; interval mapping did not identify this QTL because it does not target variance QTL. We believe that the results of these reanalyses, which in other respects largely replicated the original findings, support the use of mean-variance QTL mapping as standard practice

The project data sphere initiative: accelerating cancer research by sharing data

Background. In this paper, we provide background and context regarding the potential for a new data-sharing platform, the Project Data Sphere (PDS) initiative, funded by financial and in-kind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Given the relatively modest decline in cancer death rates over the past several years, a new research paradigm is needed to accelerate therapeutic approaches for oncologic diseases. Phase III clinical trials generate large volumes of potentially usable information, often on hundreds of patients, including patients treated with standard of care therapies (i.e., controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patient-level clinical trial data available to the scientific research community. Potential Benefits and Risks of Data Sharing. For researchers, shared data have the potential to foster a more collaborative environment, to answer research questions in a shorter time frame than traditional randomized control trials, to reduce duplication of effort, and to improve efficiency. For industry participants, use of trial data to answer additional clinical questions could increase research and development efficiency and guide future projects through validation of surrogate end points, development of prognostic or predictive models, selection of patients for phase II trials, stratification in phase III studies, and identification of patient subgroups for development of novel therapies. Data transparency also helps promote a public image of collaboration and altruism among industry participants. For patient participants, data sharing maximizes their contribution to public health and increases access to information that may be used to develop better treatments. Concerns about data-sharing efforts include protection of patient privacy and confidentiality. To alleviate these concerns, data sets are deidentified to maintain anonymity. To address industry concerns about protection of intellectual property and competitiveness, we illustrate several models for data sharing with varying levels of access to the data and varying relationships between trial sponsors and data access sponsors. The Project Data Sphere Initiative. PDS is an independent initiative of the CEO Roundtable on Cancer Life Sciences Consortium, built to voluntarily share, integrate, and analyze comparator arms of historical cancer clinical trial data sets to advance future cancer research. The aim is to provide a neutral, broad-access platform for industry and academia to share raw, deidentified data from late-phase oncology clinical trials using comparator-arm data sets. These data are likely to be hypothesis generating or hypothesis confirming but, notably, do not take the place of performing a well-designed trial to address a specific hypothesis. Prospective providers of data to PDS complete and sign a data sharing agreement that includes a description of the data they propose to upload, and then they follow easy instructions on the website for uploading their deidentified data. The SAS Institute has also collaborated with the initiative to provide intrinsic analytic tools accessible within the website itself. As of October 2014, the PDS website has available data from 14 cancer clinical trials covering 9,000 subjects, with hopes to further expand the database to include more than 25,000 subject accruals within the next year. PDS differentiates itself from other data-sharing initiatives by its degree of openness, requiring submission of only a brief application with background information of the individual requesting access and agreement to terms of use. Data from several different sponsors may be pooled to develop a comprehensive cohort for analysis. In order to protect patient privacy, data providers in the U.S. are responsible for deidentifying data according to standards set forth by the Privacy Rule of the U.S. Health Insurance Portability and Accountability Act of 1996. Using Data Sharing to Improve Outcomes in Cancer: The “Prostate Cancer Challenge.” Control-arm data of several studies among patients with metastatic castration-resistant prostate cancer (mCRPC) are currently available through PDS. These data sets have multiple potential uses. The “Prostate Cancer Challenge” will ask the cancer research community to use clinical trial data deposited in the PDS website to address key research questions regarding mCRPC. General themes that could be explored by the cancer community are described in this article: prognostic models evaluating the influence of pretreatment factors on survival and patient-reported outcomes; comparative effectiveness research evaluating the efficacy of standard of care therapies, as illustrated in our companion article comparing mitoxantrone plus prednisone with prednisone alone; effects of practice variation in dose, frequency, and duration of therapy; level of patient adherence to elements of trial protocols to inform the design of future clinical trials; and age of subjects, regional differences in health care, and other confounding factors that might affect outcomes. Potential Limitations and Methodological Challenges. The number of data sets available and the lack of experimental arm data limit the potential scope of research using the current PDS. The number of trials is expected to grow exponentially over the next year and may include multiple cancer settings, such as breast, colorectal, lung, hematologic malignancy, and bone marrow transplantation. Other potential limitations include the retrospective nature of the data analyses performed using PDS and its generalizability, given that clinical trials are often conducted among younger, healthier, and less racially diverse patient populations. Methodological challenges exist when combining individual patient data from multiple clinical trials; however, advancements in statistical methods for secondary database analysis offer many tools for reanalyzing data arising from disparate trials, such as propensity score matching. Despite these concerns, few if any comparable data sets include this level of detail across multiple clinical trials and populations. Conclusion. Access to large, late-phase, cancer-trial data sets has the potential to transform cancer research by optimizing research efficiency and accelerating progress toward meaningful improvements in cancer care. This type of platform provides opportunities for unique research projects that can examine relatively neglected areas and that can construct models necessitating large amounts of detailed data.The full potential of PDS will be realized only when multiple tumor types and larger numbers of data sets are available through the website

Candidate Risk Factors and Mechanisms for Tolvaptan-Induced Liver Injury Are Identified Using a Collaborative Cross Approach

Clinical trials of tolvaptan showed it to be a promising candidate for the treatment of Autosomal Dominant Polycystic Kidney Disease (ADPKD) but also revealed potential for idiosyncratic drug-induced liver injury (DILI) in this patient population. To identify risk factors and mechanisms underlying tolvaptan DILI, 8 mice in each of 45 strains of the genetically diverse Collaborative Cross (CC) mouse population were treated with a single oral dose of either tolvaptan or vehicle. Significant elevations in plasma alanine aminotransferase (ALT) were observed in tolvaptan-treated animals in 3 of the 45 strains. Genetic mapping coupled with transcriptomic analysis in the liver was used to identify several candidate susceptibility genes including epoxide hydrolase 2, interferon regulatory factor 3, and mitochondrial fission factor. Gene pathway analysis revealed that oxidative stress and immune response pathways were activated in response to tolvaptan treatment across all strains, but genes involved in regulation of bile acid homeostasis were most associated with tolvaptan-induced elevations in ALT. Secretory leukocyte peptidase inhibitor (Slpi) mRNA was also induced in the susceptible strains and was associated with increased plasma levels of Slpi protein, suggesting a potential serum marker for DILI susceptibility. In summary, tolvaptan induced signs of oxidative stress, mitochondrial dysfunction, and innate immune response in all strains, but variation in bile acid homeostasis was most associated with susceptibility to the liver response. This CC study has indicated potential mechanisms underlying tolvaptan DILI and biomarkers of susceptibility that may be useful in managing the risk of DILI in ADPKD patients

QTL Mapping on a Background of Variance Heterogeneity

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such “background variance heterogeneity” (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene’s test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term “mean-variance QTL mapping”, to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight

vqtl: An R Package for Mean-Variance QTL Mapping

We present vqtl, an R package for mean-variance QTL mapping. This QTL mapping approach tests for genetic loci that influence the mean of the phenotype, termed mean QTL, the variance of the phenotype, termed variance QTL, or some combination of the two, termed mean-variance QTL. It is unique in its ability to correct for variance heterogeneity arising not only from the QTL itself but also from nuisance factors, such as sex, batch, or housing. This package provides functions to conduct genome scans, run permutations to assess the statistical significance, and make informative plots to communicate results. Because it is inter-operable with the popular qtl package and uses many of the same data structures and input patterns, it will be straightforward for geneticists to analyze future experiments with vqtl as well as re-analyze past experiments, possibly discovering new QTL

Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice

We illustrate, through two case studies, that “mean-variance QTL mapping”—QTL mapping that models effects on the mean and the variance simultaneously—can discover QTL that traditional interval mapping cannot. Mean-variance QTL mapping is based on the double generalized linear model, which extends the standard linear model used in interval mapping by incorporating not only a set of genetic and covariate effects for mean but also set of such effects for the residual variance. Its potential for use in QTL mapping has been described previously, but it remains underutilized, with certain key advantages undemonstrated until now. In the first case study, a reduced complexity intercross of C57BL/6J and C57BL/6N mice examining circadian behavior, our reanalysis detected a mean-controlling QTL for circadian wheel running activity that interval mapping did not; mean-variance QTL mapping was more powerful than interval mapping at the QTL because it accounted for the fact that mice homozygous for the C57BL/6N allele had less residual variance than other mice. In the second case study, an intercross between C57BL/6J and C58/J mice examining anxiety-like behaviors, our reanalysis detected a variance-controlling QTL for rearing behavior; interval mapping did not identify this QTL because it does not target variance QTL. We believe that the results of these reanalyses, which in other respects largely replicated the original findings, support the use of mean-variance QTL mapping as standard practice

kbroman/qtl: Version 1.62

<h2>Version 1.62, 2023-11-17</h2> <h3>Bug fixes</h3> <ul> <li><p>Fixed bug in <code>summary.scanone()</code> for the case <code>format="onepheno"</code> but <code>threshold</code> has length > 1. (Issue #102.)</p> </li> <li><p>Fixed bug in <code>read.cross()</code> for format <code>"csvs"</code> when the phenotype file contains only the identifiers. (Issue #103.)</p> </li> </ul&gt

Comparative Effectiveness of Mitoxantrone Plus Prednisone Versus Prednisone Alone in Metastatic Castrate‐Resistant Prostate Cancer After Docetaxel Failure

BACKGROUND. Mitoxantrone was approved for use in metastatic castrate-resistant prostate cancer (mCRPC) based on pain palliation without observed survival benefit in a small phase III trial in 1996. To re-evaluate for possible survival benefits in a larger contemporary sample and to demonstrate analytic uses of the newly available Project Data Sphere online resource, we used data from control arms of completed clinical trials to compare survival and toxicity among patients with postdocetaxel mCRPC treated with mitoxantrone and prednisone. PATIENTS AND METHODS. Control arm data from two phase III randomized control trials, SUN 1120 and TROPIC, were used to examine the efficacy of mitoxantrone plus prednisone (n = 305) versus prednisone alone (n = 257) among patients with postdocetaxel mCRPC. Propensity score matching was used to balance patient characteristics between the separate trials, conditioned on age and key prognostic variables of survival. The primary outcome was overall survival. Secondary endpoints evaluated safety. RESULTS. Median survival was similar among patients receiving mitoxantrone plus prednisone versus prednisone alone (385 days vs. 336 days; deceleration factor = 0.04; 95% confidence interval: −0.12 to 0.22). Prevalence of several any-grade toxicity, including fatigue, back pain, and peripheral neuropathy, was increased among patients who received mitoxantrone. CONCLUSION. There was no significant survival benefit for mitoxantrone plus prednisone over prednisone alone among men with mCRPC after docetaxel therapy. This finding is consistent with prior studies showing no survival advantage with mitoxantrone in the predocetaxel setting. Furthermore, our data suggest that mitoxantrone may be associated with increased toxicity compared with prednisone alone

Candidate Risk Factors and Mechanisms for Tolvaptan-Induced Liver Injury Are Identified Using a Collaborative Cross Approach

Clinical trials of tolvaptan showed it to be a promising candidate for the treatment of Autosomal Dominant Polycystic Kidney Disease (ADPKD) but also revealed potential for idiosyncratic drug-induced liver injury (DILI) in this patient population. To identify risk factors and mechanisms underlying tolvaptan DILI, 8 mice in each of 45 strains of the genetically diverse Collaborative Cross (CC) mouse population were treated with a single oral dose of either tolvaptan or vehicle. Significant elevations in plasma alanine aminotransferase (ALT) were observed in tolvaptan-treated animals in 3 of the 45 strains. Genetic mapping coupled with transcriptomic analysis in the liver was used to identify several candidate susceptibility genes including epoxide hydrolase 2, interferon regulatory factor 3, and mitochondrial fission factor. Gene pathway analysis revealed that oxidative stress and immune response pathways were activated in response to tolvaptan treatment across all strains, but genes involved in regulation of bile acid homeostasis were most associated with tolvaptan-induced elevations in ALT. Secretory leukocyte peptidase inhibitor (Slpi) mRNA was also induced in the susceptible strains and was associated with increased plasma levels of Slpi protein, suggesting a potential serum marker for DILI susceptibility. In summary, tolvaptan induced signs of oxidative stress, mitochondrial dysfunction, and innate immune response in all strains, but variation in bile acid homeostasis was most associated with susceptibility to the liver response. This CC study has indicated potential mechanisms underlying tolvaptan DILI and biomarkers of susceptibility that may be useful in managing the risk of DILI in ADPKD patients