For all introductory genetics courses Informed by many years of genetics teaching and research expertise, authors Mark Sanders and John Bowman use an integrated approach that helps contextualise three core challenges of learning genetics: solving problems, understanding evolution, and understanding the connection between traditional genetics models and more modern approaches.
The development of simple, sensitive and rapid methods for the detection and identification of Toxoplasma gondii is important for the diagnosis and epidemiological studies of the zoonotic disease toxoplasmosis. In the past 2 decades, molecular methods based on a variety of genetic markers have been developed, each with its advantages and limitations. The application of these methods has generated invaluable information to enhance our understanding of the epidemiology, population genetics and phylogeny of T. gondii. However, since most studies focused solely on the detection but not genetic characterization of T. gondii, the information obtained was limited. In this review, we discuss some widely used molecular methods and propose an integrated approach for the detection and identification of T. gondii, in order to generate maximum information for epidemiological, population and phylogenetic studies of this key pathogen.
Results: Thirty of the 324 patients with early-onset parkinsonism (9.3%) were found to carry mutations in Parkin, PINK1, or PLA2G6 or had increased trinucleotide repeats in SCA8. Twenty-nine of 109 probands with autosomal-recessive inheritance of parkinsonism (26.6%) were found to carry mutations in Parkin, PINK1, GBA, or HTRA2. The genetic causes for the 138 probands with an autosomal-dominant inheritance pattern of parkinsonism were more heterogeneous. Seventeen probands (12.3%) carried pathogenic mutations in LRRK2, VPS35, MAPT, GBA, DNAJC13, C9orf72, SCA3, or SCA17. A novel missense mutation in the UQCRC1 gene was found in a family with autosomal-dominant inheritance parkinsonism via whole-exome sequencing analysis.
Multiomics (multiple omics) provides an integrated perspective to power discovery across multiple levels of biology. This biological analysis approach combines genomic data with data from other modalities such as transcriptomics, epigenetics, and proteomics, to measure gene expression, gene activation, and protein levels.
Incorporating RNA-Seq can help researchers annotate and prioritize variants for functional analysis to understand mechanisms of disease. A multiomics approach to functional genomics can help power drug target identification and biomarker discovery.
Comprehensive epigenetic profiling can reveal patterns of gene regulation to help find the function of variants identified by GWAS. Multiomics approaches that combine methylation or other epigenetic profiling with genetic information can connect functional layers to decipher complex pathways and disease mechanisms.
The integration of genomics plus epigenetics and RNA-Seq can help researchers identify candidate genes and understand the mechanisms controlling interesting phenotypes. This holistic, non-biased multiomics approach can uncover new regulatory elements for biomarkers and therapeutic targets.
This multiomic approach directly connects genotype to phenotype for more informed research on disease and therapeutics development. Linking genetic variation to protein expression at the single-cell level can reveal the functional impact of somatic mutations on human cancers to better understand tumor evolution and disease progression.
Multiomics leverages three main experimental approaches: bulk-cell analysis, single-cell analysis, and spatial analysis. Explore the specific reagents and equipment needed for some of the most frequently used multiomics workflows.
At Illumina, our goal is to apply innovative technologies to the analysis of genetic variation and function, making studies possible that were not even imaginable just a few years ago. It is mission critical for us to deliver innovative, flexible, and scalable solutions to meet the needs of our customers. As a global company that places high value on collaborative interactions, rapid delivery of solutions, and providing the highest level of quality, we strive to meet this challenge. Illumina innovative sequencing and array technologies are fueling groundbreaking advancements in life science research, translational and consumer genomics, and molecular diagnostics.
Genomic applications are being integrated into a broad range of clinical and research activities at health care systems across the United States. This trend can be attributed to a variety of factors, including the declining cost of genome sequencing and the potential for improving health outcomes and cutting the costs of care. The goals of these genomics-based programs may be to identify individuals with clinically actionable variants as a way of preventing disease, providing diagnoses for patients with rare diseases, and advancing research on genetic contributions to health and disease. Of particular interest are genomics- based screening programs, which will, in this publication, be clinical screening programs that examine genes or variants in unselected populations in order to identify individuals who are at an increased risk for a particular health concern (e.g., diseases, adverse drug outcomes) and who might benefit from clinical interventions.
To make our integrated platform accessible to all users regardless of computational background, we derived a simple user interface for automated analysis of swimming movies (Figure 3). After defining the location of a family of sequentially named .tif images (multiple independent recordings can be analyzed and summary statistics are reported), the acquisition parameters (i.e., spatial resolution (µ/pixel) and recording speed (frames per second) are entered (Figure 3a). Users can adjust body and skeleton threshold values to obtain the best fit to their recording conditions (Figure 3b). After definition, users see the progress of analysis, including summary kinematics, biomechanics, skeletonization, and curvature plots (Figure 3c). Upon completion of the analysis, summary statistics for each of the 18 parameters are shown (Figure 3d) and users can review each individual recording for accuracy using trajectory plots, curvature analysis, and/or a skeleton browser. Data are output as .txt files for further analysis.
We have developed a free, user-friendly, and quantitative solution for integrative analysis of C. elegans locomotion. Our method uses equipment commonly found in C. elegans research labs, making it easy and inexpensive to implement. The ability to quantitatively analyze phenotypes regardless of image acquisition methods should allow comparison of data between laboratories. Full manuscript, BMP MATLAB scripts as well as detailed instructions and sample files (Krajacic et al., 2012) are available for download at www.genetics.org.
Crowgey EL, Stabley DL, Chen C, Huang H, Robbins KM, Polson SW, Sol-Church K, Wu CH. An integrated approach for analyzing clinical genomic variant data from next-generation sequencing. J Biomol Tech 2015 Jan 29 [Epub ahead of print].
Li YR, Li J, Zhao SD, Bradfield JP, Mentch FD, Maggadottir SM, Hou C, Abrams DJ, Chang D, Gao F, Guo Y, Wei Z, Connolly JJ, Cardinale CJ, Bakay M, Glessner JT, Li D, Kao C, Thomas KA, Qiu H, Chiavacci RM, Kim CE, Wang F, Snyder J, Richie MD, Flatø B, Førre Ø, Denson LA, Thompson SD, Becker ML, Guthery SL, Latiano A, Perez E, Resnick E, Russell RK, Wilson DC, Silverberg MS, Annese V, Lie BA, Punaro M, Dubinsky MC, Monos DS, Strisciuglio C, Staiano A, Miele E, Kugathasan S, Ellis JA, Munro JE, Sullivan KE, Wise CA, Chapel H, Cunningham-Rundles C, Grant SF, Orange JS, Sleiman PM, Behrens EM, Griffiths AM, Satsangi J, Finkel TH, Keinan A, Prak ET, Polychronakos C, Baldassano RN, Li H, Keating BJ, Hakonarson H. Meta-analysis of shared genetic architecture across 10 pediatric autoimmune diseases. Nat Med 2015;21:1018-1027.
Research on less-studied histological types of ovarian cancer that uses integrated analysis of multiple layers of information, including relationships between risk factors and genomic and prognostic associations, to provide powerful biological and mechanistic insight into ovarian cancer biology with potential to point to novel targeted therapeutic options (R01 CA248288; Ellen L. Goode, Ph.D., principal investigator).
Clinical trials, comparative studies, data analysis, disease complications, epidemiology, genetics, health outcome assessment, medical policy, mental health services, psychology, public health, research methodology, risk factors, statistics, surgery, therapeutics, and more. 2b1af7f3a8