Researchers in the Department of Genetics at the Harvard University Medical School have been awarded $500,000 by the National Institutes of Health for the first year of a four-year project to study multi-allelic copy number variation in the human genome.
As part of the research, the Harvard team is using a Bio-Rad QX100 Droplet Digital PCR system as one of two methods to analyze multi-allelic CNVs in human cohorts. The researchers are also using a computational method that compares available whole-genome sequencing data.
Steven McCarroll, a professor of genetics at Harvard Med and director of genetics at the Stanley Center for Psychiatric Research at the Broad Institute, is principal investigator on the grant, which is being administered by NIH's National Human Genome Research Institute.
According to a recently published grant abstract, McCarroll and colleagues seek to analyze multi-allelic CNVs, which involve genes and other functional elements for which three or more segregating alleles give rise to a wide range of copy numbers — between two and 10 — per diploid human genome.
These multi-allelic CNVs have been "refractory to widely used analysis methods and are not assessed in the genome-scale molecular or statistical approaches used to study genetically complex phenotypes in humans," the researchers wrote.
The project builds on research that McCarroll's group previously conducted on characterizing multi-allelic duplication CNVs of a megabase-long inversion polymorphism in a particular locus of chromosome 17 called 17q21.31, which contains markers previously associated with female fertility, female meiotic recombination, and neurological disease.
As part of that research, published in the August 2012 issue of Nature Genetics, the group analyzed read depth in the locus by applying an algorithm called Genome Structure in Populations, or Genome STRiP, to whole-genome sequencing data from 946 unrelated individuals sampled as part of the 1000 Genomes Project; and used droplet-based digital PCR to analyze 120 parent-offspring trios from HapMap.
They found that their measurements of integer copy number varied from two to eight, and were 99.1 percent concordant across 234 genotypes in overlapping samples, thus validating both the computational and digital PCR methods.
More specifically, for the digital PCR assay, the group designed a pair of PCR primers and a dual-labeled fluorescence-FRET oligonucleotide probe to both the CNV locus and a two-copy control locus. Then they used a droplet generator from QuantaLife to compartmentalize the PCR reaction into uniform 1-nanoliter emulsion-based droplets containing zero, one, or very few template molecules for each locus; and a droplet reader from QuantaLife to count the number of positive and negative droplets, comparing the droplet counts of the CNV locus to the control locus to determine absolute copy number.
QuantaLife originally developed the droplet-based digital PCR system, but was acquired in October by Bio-Rad, which rebranded the platform as the QX100 Droplet Digital PCR system (PCR Insider, 10/6/2011).
Annette Tumolo, director of the digital biology center at Bio-Rad, told PCR Insider this week that McCarroll has access to two such platforms, one of which is in use at Harvard and was obtained from QuantaLife, and one of which Bio-Rad sold to the Broad Institute.
Tumolo said that Bio-Rad maintains "an active and positive relationship" with the McCarroll lab. "They've gotten great results [with the QX100], and were able to rapidly publish the Nature Genetics paper," Tumolo said.
Under the new NHGRI grant, McCarroll and colleagues plan to "accurately analyze mCNVs in reference populations" using both the computational and digital PCR approach, the researchers wrote in their grant abstract.
"By analyzing these data in a statistical framework that incorporates information about genotypes, allele frequencies, inheritance, and haplotypes, we will place mCNV alleles onto the haplotype maps created by HapMap and 1000 Genomes, and render mCNVs accessible to genotype imputation to the fullest extent possible," the grant abstract states.
In addition, McCarroll's group hopes to "deeply characterize mCNVs at 10 biomedically important loci, to understand these polymorphisms at the levels of population genetics, mutational rates and histories, and relationships to clinical phenotypes. Finally, we will pilot inexpensive in silico genome-wide association studies for mCNVs based on statistical imputation into existing GWAS data sets."
The end goal of the project is to discover relationships between disease risk and gene dosage, which will help reveal the molecular etiology of human disease, the researchers wrote.