The illustration above depicts with an example of four genes, how knowing how variable genes are in the normal population helps to find candidate disease genes in a patient. Above, top: Tuuli Lappalainen, PhD; bottom: Pejman Mohammadi, PhD.

For individuals with rare diseases, getting a diagnosis is often a long and complicated odyssey. Over the past few years, this has been greatly improved by genome sequencing that can pinpoint the mutation that breaks a gene and leads to a severe disease. However, this approach is still unsuccessful in the majority of patients, largely because of our inability to read the genome to identify all mutations that disrupt gene function.

In a new study published on October 10 in Science , researchers from New York Genome Center , Columbia University , and Scripps Research Institute propose a solution to this problem. Building a new computational method for analyzing genomes together with transcriptome data from RNA-sequencing, they can now identify genes where genetic variants disrupt gene expression in patients and improve the diagnosis of rare genetic disease.

The new method introduced in this study, Analysis of Expression Variation or ANEVA, first takes allele-specific expression data from a large reference sample of healthy individuals to understand how much genetic regulatory variation each gene harbors in the normal population. Then, using the ANEVA Dosage Outlier Test, researchers can analyze the transcriptome of any individual – such as a patient – to find a handful of genes where he or she carries a genetic variant with an unusually large effect compared to what healthy individuals have. By applying this test to a cohort of muscle dystrophy and myopathy patients, the researchers demonstrated  the performance of their method and diagnosed additional patients where previous methods of genome and RNA analysis had failed to find the broken genes.

The epigenomic profile of RBFOX2, a haploinsufficient gene recently identified as a risk gene of congenital heart disease. Each small box represents 100 bp region around transcription start sites (TSSs) and the shade of the color reflect the strength of the histone mark signal in tissues under normal conditions. RBFOX2 has large expansion of active histone marks (H3K4me3 and H3K9ac), especially in heart and epithelial tissues (purple and gray rows), and tissue-specific suppression mark (H3K27me3) in blood samples.(Credit: Shen lab)

The genetics of developmental disorders, such as congenital heart disease and autism, are highly complex. There are roughly 500 to 1,000 risk genes that can lead to each of these diseases, and to date, only about a few dozen have been identified. Scientists have ramped up efforts to develop computational approaches to address challenges in accurately identifying genetic risk factors in ongoing genetic studies, and the availability of such tools would greatly assist researchers in gaining a deeper understanding of the root causes of these diseases. 

Focusing on haploinsufficiency, a key biological mechanism of genetic risk in developmental disorders, Yufeng Shen , PhD, and his lab have developed a novel computational method that enables researchers to find new risk genes in these diseases. Their key idea is that the expression of haploinsufficient genes must be precisely regulated during normal development, and such regulation can be manifested in distinct patterns of genomic regulatory elements. Using data from the NIH Roadmap Epigenomics Project, they showed there is a strong correlation of certain histone marks and known haploinsufficient genes. Then based on supervised machine learning algorithms, they developed a new method, which they call Episcore , to predict haploinsufficiency from epigenomic data representing a broad range of tissue and cell types. Finally, they demonstrate the utility of Episcore in identification of novel risk variants in studies of congenital heart disease and intellectual disability.  

Organoids created from the bladder cancers of patients mimic the characteristics of each patient’s tumor and may be used in the future to identify the best treatment for each patient. Images: Michael Shen

Columbia University Irving Medical Center (CUIMC) and NewYork-Presbyterian researchers have created patient-specific bladder cancer organoids that mimic many of the characteristics of actual tumors. As reported by CUIMC, the use of organoids, tiny 3-D spheres derived from a patient’s own tumor, may be useful in the future to guide treatment of patients.

The study was published April 5 in the online edition of Cell.

Clinical and molecular data are currently stored in many different databases using different semantics and different formats. A new project called DeepLink aims to develop a framework that would make it possible to compare and analyze data across platforms not originally intended to intersect. (Image courtesy of Nicholas Tatonetti.)

Medical doctors and basic biological scientists tend to speak about human health in different languages. Whereas doctors in the clinic focus on phenomena such as symptoms, drug effects, and treatment outcomes, basic scientists often concentrate on activity at the molecular and cellular levels such as genetic alterations, gene expression changes, or protein profiles. Although these various layers are all related physiologically, there is no standard terminology or framework for storing and organizing the different kinds of data that describe them, making it difficult for scientists to systematically integrate and analyze data across different biological scales. Being able to do so, many investigators now believe, could provide a more efficient and comprehensive way to understand and fight disease.

A new project recently launched by Nicholas Tatonetti (Assistant Professor in the Columbia University Departments of Systems Biology and Biomedical Informatics) along with co-principal investigators Chunhua Weng (Department of Biomedical Informatics) and Michel Dumontier (Stanford University), aims to bridge this divide. With the support of a $1.1 million grant from the National Center for Advancing Translational Science (NCATS) the scientists have begun to develop a tool they call DeepLink, a data translator that will integrate health-related findings at multiple scales.

As Dr. Tatonetti explains, “We want to close what we call the interoperability gap, a fundamental difference in the language and semantics used to describe the models and knowledge between the clinical and molecular domains. Our goal is to develop a scalable electronic architecture for integrating the enormous multiscale knowledge that is now available.”

Yufeng Shen's lab is interested in developing better computational methods for identifying rare genetic variants that increase disease risk.

On the surface, birth defects and cancer might not seem to have much in common. For some time, however, scientists have observed increased cancer risk among patients with certain developmental syndromes. One well-known example is seen in children with Noonan syndrome, who have an eightfold increased risk of developing leukemia. Recently, researchers studying the genetics of autism also observed mutations in PTEN, an important tumor suppressor gene. Although such findings have been largely isolated and anecdotal, they raise the tantalizing question of whether cancer and developmental disorders might be fundamentally linked.

According to a paper recently published in the journal Human Mutation, many of these similarities might not be just coincidental, but the result of shared genetic mutations. The study, led by Yufeng Shen, an Assistant Professor in the Columbia University Departments of Systems Biology and Biomedical Informatics, together with Wendy Chung, Kennedy Family Associate Professor of Pediatrics at Columbia University Medical Center, found that cancer-driving genes also make up more than a third of the risk genes for developmental disorders. Moreover, many of these genes appear to function through similar modes of action. The scientists suggest that this could make tumors “natural laboratories” for pinpointing and predicting the damaging effects of rare genetic alterations that cause developmental disorders.

“In comparison with cancer, there are relatively few patients with developmental disorders,” Shen explains, “For geneticists, this makes it hard to identify the risk genes solely based on statistical evidence of mutations from these patients. This study indicates that we should be able to use what we learn from cancer genetics — where much more data are available — to help in the interpretation of genetic data in developmental disorders.”

By using statistical methods to compare genomic data across species, such as chimpanzees and humans, the Przeworski Lab is gaining insights into the origins of genetic variation and adaptation. (Photo: Common chimpanzee at the Leipzig Zoo. Thomas Lersch, Wikimedia Commons.)

Launched approximately 100 years ago, population genetics is a subfield within evolutionary biology that seeks to explain how processes such as mutation, natural selection, and random genetic drift lead to genetic variation within and between species. Population genetics was originally born from the convergence of Mendelian genetics and biostatistics, but with the recent availability of genome sequencing data and high-performance computing technologies, it has bloomed into a mature computational science that is providing increasingly high-resolution models of the processes that drive evolution.

Molly Przeworski, a professor in the Columbia University Departments of Biological Sciences and Systems Biology, majored in mathematics at Princeton before beginning her PhD in evolutionary biology at the University of Chicago in the mid-1990s. While there, she realized that the availability of increasingly large data sets was changing population genetics, and has since been interested in using statistical approaches to investigate questions such as how genetic variation drives adaptation and why mutation rate and recombination rate differ among species. In the following interview, she describes how population genetics is itself evolving, as well as some of her laboratory’s contributions to the field.

The Department of Systems Biology and Center for Computational Biology and Bioinformatics are pleased to announce that three Columbia University faculty members have recently joined our community. Kam Leong, the Samuel Y. Sheng Professor of Biomedical Engineering at Columbia University, is now an interdisciplinary faculty member in the Department of Systems Biology. In addition, Yaniv Erlich and Guy Sella are now members of the Center for Computational Biology and Bioinformatics (C2B2). Their addition to the Department and to C2B2 will bring new expertise that will benefit our research and education activities, incorporating perspectives from fields such as nanotechnology, bioinformatics, and evolutionary genomics.

Researchers in the Rabadan Lab have found that comorbidity between Mendelian diseases and cancer may result from shared genetic factors.

Genetic diseases can arise in a variety of ways. Mendelian disorders, for example, occur when specific mutations in single genes — called germline mutations — are inherited from either of one’s two parents. Well-known examples of Mendelian diseases include cystic fibrosis, sickle cell disease, and Duchenne muscular dystrophy. Other genetic diseases, including cancer, result from somatic mutations, which occur in individual cells during a person’s lifetime. Because the genetic origins of Mendelian diseases and cancer are so different, they are typically understood to be distinct phenomena. However, scientists in the Columbia University Department of Systems Biology have found evidence that there might be interesting genetic connections between them. 

In a paper just published in Nature Communications, postdoctoral research scientist Rachel Melamed and colleagues in the laboratory of Associate Professor Raul Rabadan report on a new method that uses knowledge about Mendelian diseases to suggest mutations involved in cancer. The study takes advantage of an enormous collection of electronic health records representing over 110 million patients, a substantial percentage of US residents. The authors show that clinical co-occurrence of Mendelian diseases and cancer, known as comorbidity, can be tied to genetic changes that play roles in both diseases. The paper also identifies several specific relationships between Mendelian diseases and the cancers melanoma and glioblastoma.

Network of autism-associated genes. (Credit: Dennis Vitkup)

The following article is reposted with permission from the Columbia University Medical Center Newsroom. Find the original here.

People with autism have a wide range of symptoms, with no two people sharing the exact type and severity of behaviors. Now a large-scale analysis of hundreds of patients and nearly 1000 genes has started to uncover how diversity among traits can be traced to differences in patients’ genetic mutations. The study, conducted by researchers at Columbia University Medical Center, was published Dec. 22 in the journal Nature Neuroscience.

Autism researchers have identified hundreds of genes that, when mutated, likely increase the risk of developing autism spectrum disorder (ASD). Much of the variability among people with ASD is thought to stem from the diversity of underlying genetic changes, including the specific genes mutated and the severity of the mutation.

“If we can understand how different mutations lead to different features of ASD, we may be able to use patients’ genetic profiles to develop accurate diagnostic and prognostic tools and perhaps personalize treatment,” said senior author Dennis Vitkup, PhD, associate professor of systems biology and biomedical informatics at Columbia University’s College of Physicians & Surgeons.