Phenomenal advances in DNA sequencing technologies have enabled systematic identification of genetic variants in human individuals, and the recent FDA marketing authorization of the first next-generation genome sequencer signals the arrival of a new era of pharmacogenomics and personalized medicine. Nevertheless, DNA sequencing alone fails to provide complete information on the genetic makeup of an individual, as two homologous sets of chromosomes are present in the human genome. Delineation of both maternal and paternal copies, or haplotypes, is critical for determining an individual's genetic composition, and for understanding the structure and function of the human genome and its role in health and disease. Yet genome- scale haplotyping, or phasing of DNA variants, has long remained an elusive goal. Existing approaches are prohibitively expensive, technically challenging, require specialized instrumentation, or fall far short of reconstructing chromosome-spanning haplotypes. Arima Genomics has recently developed an innovative new approach for whole-genome haplotyping, combining proximity-ligation and DNA sequencing with a probabilistic algorithm for haplotype assembly. This new method, known as HaploSeq, achieves chromosome-spanning haplotypes with high completeness, resolution, and accuracy in mammalian genomes. As a cost-effective, streamlined technology, HaploSeq is poised to underpin a new standard in genome sequencing in biomedical applications and other markets from pharmacogenomics to agricultural biotechnology. The objectives of Arima Genomics' proposed R&D efforts involve improvement of HaploSeq's ability to phase rare variants in human cells by adapting the protocol to achieve more uniform genome coverage, extension of the HaploSeq algorithm's capabilities to provide genotypes concurrently with haplotypes from the same source sequencing data by developing a new smart-mapping computational module, and demonstration and benchmarking of HaploSeq's utility in ongoing next-generation genetic association studies in partnership with clinical research collaborators at UC San Diego. Successful completion of our research aims will contribute invaluable new knowledge to ongoing investigations of how human genetic variation influences the gene regulatory networks involved in cardiac biology and disease, and will substantially advance the capabilities of HaploSeq toward commercial viability in diverse research, biomedical, and clinical sequencing applications. HaploSeq promises to greatly enhance our understanding of human genetics in health and contribute to the realization of personalized medicine.

Public Health Relevance

Delineating both maternal and paternal genetic copies, or haplotypes, is critical for determining an individual's genetic composition and for understanding the structure and function of the human genome and its role in health and disease, yet genome-scale haplotyping, or 'phasing' of DNA variants, has long remained an elusive goal. Arima Genomics has recently developed an innovative, cost-effective, and streamlined technology for whole-genome haplotyping in humans, known as HaploSeq. This new method will have broad impacts in diverse research, biomedical, and clinical sequencing applications, and promises to improve our understanding of human genetics in health and contribute to the realization of personalized medicine.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Small Business Technology Transfer (STTR) Grants - Phase I (R41)
Project #
3R41HG008118-01A1S1
Application #
9143606
Study Section
Program Officer
Smith, Michael
Project Start
2015-03-01
Project End
2017-01-31
Budget Start
2015-11-24
Budget End
2016-01-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Arima Genomics, LLC
Department
Type
DUNS #
079163557
City
San Diego
State
CA
Country
United States
Zip Code
92122
Nariai, Naoki; Greenwald, William W; DeBoever, Christopher et al. (2017) Efficient Prioritization of Multiple Causal eQTL Variants via Sparse Polygenic Modeling. Genetics 207:1301-1312
Greenwald, William W; Li, He; Smith, Erin N et al. (2017) Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data. BMC Bioinformatics 18:207
Panopoulos, Athanasia D; D'Antonio, Matteo; Benaglio, Paola et al. (2017) iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types. Stem Cell Reports 8:1086-1100
DeBoever, Christopher; Li, He; Jakubosky, David et al. (2017) Large-Scale Profiling Reveals the Influence of Genetic Variation on Gene Expression in Human Induced Pluripotent Stem Cells. Cell Stem Cell 20:533-546.e7