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Cutting edge computational molecular biology research featured in Genome Research

03/18/2008

TIP SHEET: Highlights from papers coordinated with the Research in Computational Molecular Biology (RECOMB) 2008 Conference

Cutting-edge computational molecular biology research featured in Genome Research

Genome Research is publishing several papers in coordination with the Research in Computational Molecular Biology (RECOMB) 2008 Conference, March 30, 2008 – April 2, 2008, at the National University of Singapore. Genome Research has partnered with RECOMB to publish a select number of high-quality contributions to the meeting, presenting the latest theoretical advances in computational biology and their applications in molecular biology and medicine. The papers will appear online Wednesday, March 19, 2008, and in print as a special section of the Genome Research April 2008 issue.

1. Inferring ancestral genomes from admixed populations

Understanding the origins, migrations, adaptations, and admixtures of human populations is often severely complicated by a lack of documentation or archaeological evidence. However, the genomic structure of an individual or a population can serve as a biological record of ancestry. Endeavors such as the International HapMap Project have made considerable progress in describing common patterns of human genetic variation, yet analyzing this data to inform upon ancestry remains a formidable task. Two papers published in Genome Research present new mathematical models directly addressing this challenge.

Led by Dr. Serafim Batzoglou of Stanford University, researchers have designed a model that significantly improves scientists’ ability to determine ancestry based upon genomic features. “We created a new model that improved accuracy to such an extent that distinguishing between the continental populations in HapMap became possible up to 20 generations back,” describes co-first author Andreas Sundquist. Though genetic variation data from closely related populations is lacking, Sundquist and co-first author Eugene Fratkin overcame this obstacle by constructing simulated population sets to test their model. “We were then able to conduct tests on these populations and analyze the accuracy of our method as a function of both the population divergence and the number of generations of admixture,” explains Sundquist. “Our results show that challenges still remain in distinguishing between closely related populations, but that we have vastly improved the state-of-the-art.”

In the second paper, researchers from the International Computer Science Institute and the University of California, Berkeley, have taken another approach to inferring ancestry. Sankararaman et al. present a new computational model that improves on previous methods for deriving ancestry information from admixed populations, by more accurately modeling linkage disequilibrium and predicting historical recombination events. The authors utilize their algorithm to tackle problems such as inferring locus-specific ancestry in a population derived from unknown ancestral populations.

Reference:
Sundquist, A., Fratkin, E., Do, C.B., Batzoglou, S. Effect of genetic divergence in identifying ancestral origin using HAPAA. Genome Res. doi:10.1101/gr.072850.107 .

Contact:
Serafim Batzoglou, Ph.D., Stanford University, Stanford, CA, USA serafim@cs.stanford.edu

Reference:
Sankararaman, S., Kimmel, G., Halperin, E., and Jordan, M.I. On the inference of ancestries in admixed populations. Genome Res. doi:10.1101/gr.072751.107 .

Contact:
Eran Halperin, Ph.D., International Computer Science Institute, Berkeley, CA, USA
heran@icsi.berkeley.edu

2. Ancestry helps map disease genes

Mapping by admixture linkage disequilibrium (MALD) is a powerful approach for identifying regions of the genome that contain genes associated with disease. This method takes advantage of differences in disease prevalence between populations to look for patterns of variation that are over-represented in population groups with increased susceptibility to a particular disorder. In this research study, scientists from Technion - Israel Institute of Technology and Washington University have developed a new technique for MALD, called Expected Mutual Information. “Our novel approach extends previous methods by incorporating knowledge on population admixture, drawing a more precise picture of the mosaic of ancestries along an individual's genome,” explains primary author Sivan Bercovici.

The authors anticipate that this new method may lead to significant advances in the ability of researchers to isolate the genetic determinants of common diseases. "The convergence of the novel computational technologies provided in this manuscript with analysis and knowledge of the population genetic architecture of special populations of interest, should greatly facilitate design and implementation of genome-wide mapping of susceptibility loci for clinical phenotypes of general importance in human health and disease,” explains co-author Dr. Karl Skorecki. “Such an implementation is already underway in a collaboration between computer scientists, population geneticists, and clinicians."

Reference:
Bercovici, S., Geiger, D., Shlush, L., Skorecki, K., and Templeton, A. Panel construction for mapping in admixed populations via expected mutual information. Genome Res. doi:10.1101/gr.073148.107.

Contact:
Sivan Bercovici, Technion - Israel Institute of Technology, Technion City, Haifa, Israel
sberco@cs.technion.ac.il

3. Applying prior knowledge to genome-wide association studies

Genome-wide association studies are an important early step in the identification of genetic markers associated with disease and disorders. A limitation of current analysis methods is that all candidate markers are considered equal in their potential to be involved in the disease, when in actuality, some markers might be more likely to play a role in disease than others. By taking into consideration preexisting information about allele association and gene function, the statistical power of association studies could be significantly improved.

In this work, University of California, Los Angeles researcher Dr. Eleazar Eskin has derived a new method, Multi-threshold Association Study Analysis, which makes use of prior knowledge about markers and gene function that has been underutilized in association studies. "This paper presents a method for incorporating this information into genetic association studies which can increase their ability to discover genes involved in disease,” describes Eskin. “As efforts such as the ENCODE project mature, which attempt to predict the function of every region of the human genome, a tremendous amount of this type of information is becoming available.”

Reference:
Eskin, E. Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information. Genome Res. doi:10.1101/gr.072785.107.

Contact:
Eleazar Eskin, Ph.D., University of California Los Angeles, Los Angeles, CA, USA
eeskin@cs.ucla.edu

Please direct requests for pre-print copies of the manuscripts to Peggy Calicchia, Genome Research Editorial Secretary ( calicchi@cshl.edu ;+1-516-422-4012).

ABOUT GENOME RESEARCH:
Genome Research (www.genome.org) is an international, monthly, peer-reviewed journal published by Cold Spring Harbor Laboratory Press. Launched in 1995, it is one of the five most highly cited primary research journals in genetics and genomics.

ABOUT COLD SPRING HARBOR LABORATORY PRESS:
Cold Spring Harbor Laboratory Press is an internationally renowned publisher of books, journals, and electronic media located on Long Island, New York. It is a division of Cold Spring Harbor Laboratory, an innovator in life science research and the education of scientists, students, and the public. For more information, visit www.cshlpress.com.

Genome Research issues press releases to highlight significant research studies that are published in the journal.



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