HFCC Data Analysis
HFCC is a redefining how whole genome association analysis (WGAS) are conducted. HFCC applies case-to-case and case-to-control methodologies to perform genome association studies. HFCC's analysis engine also performs multi-locus analysis, integrates proprietary noise filtering and statistical methods to set new standards for data interpretation. With HFCC researchers can complete studies more quickly, effectively and economically than previously thought possible.
Based on patented technology aptly-named "hypothesis-free clinical cloning", HFCC is the only whole genome analysis tool that systematically generates quality genetic leads directly from the pan-genome analysis itself, thus enabling rapid, less expensive marker identification and confident decision-making about which markers to pursue further.
"Hypothesis-Free Clinical Cloning [HFCC] promises to be a highly valuable tool for whole genome association studies. The recent availability of relatively inexpensive, high-throughput genotyping has made whole genome association studies extremely appealing, but most researchers still lack the very large sample numbers required to achieve statistical certainty in their findings. HFCC offers a new statistical method that can utilize the information from relatively few patients and markers, providing much greater power to both sample-limited WGAS and pharmacogenomic studies. It will enable rapid, less expensive marker identification, allowing for confident go or no-go decisions on what markers to pursue further."
-- Kristin Ardlie, Director, Biological Samples Platform at the Broad Institute
HOW IS HFCC DIFFERENT FROM OTHER ANALYSIS METHODS?
WHAT IS A "WGA STUDY", AND WHY IS A NEW METHOD NECESSARY?
HOW IS HFCC DIFFERENT FROM OTHER ANALYSIS METHODS?
HFCC is designed to specifically address the inherent flaws in traditional approaches to conducting WGA studies.
HFCC based genomic studies can confidently be initiated without an 'a priori' dissemination of the pathways and associations believed to be involved. Because of this the scientific burden of any human bias is drastically reduced.
HFCC studies employ a distinctive method of screening and dividing control pools. It is through this method that a continual cross-referencing between group populations becomes possible. By using patients in controls pools that include patients displaying either a full presentation of the trait-of-interest or alternately, those who harbor an undiscovered genetic predisposition to later acquiring the trait, HFCC is able to clear away the fog of epistasic-occlusion to provide a clearer picture of the true genetic associations at work.
Because HFCC studies do not require the usual extensive collection of genetic material to perform analyses, researchers make substantial gains by reducing the time and resources necessary to acquire the material needed for filling study case and control populations.
Continuing into the process of HFCC analysis, data construction and subsequent filtering proceed with the exhaustive application of algorithm sets employed to determine and evaluate the entire spectrum of univariate interactions, as is common; but unlike other available analysis methods, HFCC keeps going...
Designed to handle the daunting calculations inherent to theoretical multilocus analysis, the HFCC method has been put to the test by world renowned geneticists; successfully withstanding the critical scrutiny of today's top geneticists. Continually validated through an ongoing process of scientific verification, HFCC methodology now makes available the opportunity to identify previously hidden markers, both through novel experimentation, and through the re-analysis of previously generated data.
WHAT IS A "WGA STUDY", AND WHY IS A NEW METHOD NECESSARY?
In whole genome association studies (WGA studies), researchers compare the genetic makeup (genomes) of individuals with particular illnesses or traits to the genomes of unaffected individuals or "controls" in order to find genetic targets for use in diagnosing, preventing or treating disease.
Until recently, progress using genomic association has been slow for a number of reasons: Conventional studies require very large samples from cases and controls which are expensive and difficult to come by. Researchers must hypothesize or guess which genes are related to particular traits but with thousands of genes to choose from, most guesses are wrong. Conventional studies use a complex process to determine which of some 10 million DNA sequence deviations or SNPs (single nucleotide polymorphisms) are involved in traits. However, increasingly sensitive analysis methods lead to ever-increasing "noise" unnecessary data or information that cannot be interpreted.