Mendel software ucla

T, Harvard, and Stanford. He has authored four advanced textbooks and published nearly scientific papers. Two themes dominate Dr. Lange's research. One is the development of novel mathematical methods in optimization theory, applied probability, and computational statistics.

The other is a devotion to realistic biological modeling. Although there is bound to be a tension between these two poles, the advancement of the biomedical sciences depends on bridging the gap. His contributions to genetic epidemiology, population genetics, membrane physiology, demography, oncology, and medical imaging highlight some of the connections.

Many of his landmark papers predate by a decade or more the current flood of biological applications of hidden Markov chains, Markov chain Monte Carlo, and high-dimensional optimization. Lange has also made important software contributions to the human genetics community. His program Mendel is the Swiss army knife of statistical genetics packages. He and faculty colleague Eric Sobel are constantly adding new utilities, with a recent emphasis on special handling of the enormous data sets generated by SNP single nucleotide polymorphism association studies.

This list constitutes a Who's Who of statistical genetics. It only requires GWAS summary data, and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium LD and overlapping GWAS samples. Analyzing large-scale GWAS summary data across multiple complex traits, novel genomic regions may be identified that contribute significantly to the genetic correlation among these traits.

Integrating Clinical Data. PLoS Comput Biol 11 8 : e LADR allows investigators to conduct interactive searches across the participating organizations on patient demographics, diagnosis and procedure codes ICD-9 and CPT , labs, and medications and will be available to you and your research team for recruitment purposes for your study.

By creating this linkage, LADR will enable institutions to assemble more data on patient treatments and other exposures along with more data on their outcomes, empowering research that could not be conducted by any individual organization.

The UC Rex Data Explorer is a secure online system designed to enable UC clinical investigators to identify potential research study cohorts spanning the five UC medical centers. The output of each query from the UC ReX Data Explorer is numerics count of patients by site that match the criteria identified in the query. The numeric count helps investigators assess the feasibility of their study idea by identifying whether there are sufficient numbers of prospective subjects within the UC system.

Making Biomed BigData Accessible. Interpreting biomedical BigData. We apply a Bayesian approach to quantify both the convergence and consistency of the empirical evidence, helping the user to identify which new experiments may prove most instructive.

In the graphical structure of a research map, every edge is linked to the article s it references, allowing the user to retrieve additional details of the annotated literature.

While there are many existing packages for this task within the R ecosystem, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting.

Exploring Dynamical Cell Biology. This model allows exploration of the parameters that affect the efficiency of constitutive mRNA processing. Nucleic Acids Res. We infer significantly fewer 0.

Fitness landscapes and evolutionary forecasting To predict the dynamics of the simplest evolutionary systems, only three parameters are required: population size, mutation rate, and the fitness effects of mutations. The first two are relatively easy to obtain for natural populations, but acquiring information about the space of fitness effects, aka the fitness landscape, is a monumental task that has only recently seen significant advances.

With fitness landscapes measured for microbial populations, simulations can be run, and evolutionary forecasts can be made to predict how bacteria and viruses will evolve in the face of environmental stressors, such as antibiotics and antivirals.

Analyzing fitness landscapes is a distinct problem involving visualization of large amounts of data and quantification of epistasis. I will show examples of fitness landscapes and their quantitative properties for a range of organisms facing various environmental challenges, and show how to make evolutionary forecasts based on these data. Bayesian Analysis, 12, Datta, A.

Non-separable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with application to particulate matter analysis. Annals of Applied Statistics, 10, Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, , Quick, H. Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics, 71, Monteiro, J. Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data.

Technometrics, 56, Ren, Q. Hierarchical factor models for large spatially misaligned datasets: A low-rank predictive process approach.

Biometrics, 69, Modeling temporal gradients in regionally aggregated California asthma hospitalization data. Annals of Applied Statistics, 7, A hierarchical model for predicting forest variables over large heterogeneous domains. Journal of the American Statistical Association , Hierarchical spatial process models for multiple traits in large genetic trials.

Zhang, Y. Annals of Applied Statistics 3, Hierarchical spatial models for predicting tree species assemblages across large domains.