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Copy number variants (CNVs) are regions of the genome where certain individuals are copied or deleted and are a common type of
genetic disabling mutation.
The human genome contains hundreds of thousands of CNVs, but typical genomic analysis methods detect only the largest, and scientists aren't sure what most of them do
.
Now, a team of researchers at MIT's Broad Institute, Harvard University, Brigham and Women's Hospital and Harvard Medical School have developed a computational method that detected 15 million CNVs in the UK Biobank — six times
more than previous analyses of the same data.
With their method, the researchers found hundreds of biological connections between these CNVs and dozens of human traits, revealing new links between specific genes and traits, such as height, blood counts, and healthy biomarkers
.
The findings, published today in the journal Cell, are the most thorough analysis to date of the link between CNVs and traits and provide a new way to detect and elucidate the effects of larger structural variations, such as CNVs, that affect the genome
in complex ways.
"Being able to delve into the potential of these variants gives us more opportunities to discover the ways in which genetic variants affect human phenotypes," said
Po-Ru Loh, an associate member of Broad, senior author of the study and an assistant professor at Brigham and Women's Hospital and Harvard Medical School.
"Downstream, it gives us more clues that allow us to explain and understand the complex connections
between genetics and trait variation.
"
Many biobanks contain data on
single nucleotide polymorphisms (SNPs) or single-letter changes in DNA in large populations.
Although SNPs are common, they usually have little
effect on one trait.
CNVs—ranging in length from 5,000 to millions of base pairs—disable some genes and can induce more significant changes in the genome, such as increasing the number
of copies of a gene.
Loh's team hopes to improve the detection
of these structural changes from existing repositories of SNP data, such as the UK Biobank.
"In many large cohorts, genetic variation can only be measured using SNPs-array data, from which small CNVs are difficult to detect using current algorithms
.
We think there may be additional information in the cohort that we can use to improve our ability to detect these CNVs," said
Margaux Hujoel, the study's first author and a postdoctoral researcher in Loh's lab.
Hujoel and his team built an algorithm that grouped UK Biobank SNP probe intensity data based on distantly related
relationships between individuals who shared a haplotype (SNP cluster).
This reduces the noise in the data, allowing the number of detected CNVs to be 6 times
larger than with previous techniques.
They found that the CNVs they detected accounted for half of all gene inactivations, which scientists had previously attributed to structural changes
in the genome.
The team then searched for links between
CNVs and 56 personality traits.
They identified more than 250 associations involving nearly 100 loci or genomic regions, which could be a direct result
of CNVs.
Many studies have revealed new links
between specific genes and traits such as height.
For example, individuals with rare CNVs that disable the UHRF2 gene are, on average, about 7 centimeters
shorter than those without.
Other rare variants with strong effects — only found in large biobank-sized cohorts — may provide valuable insights
into the genome's impact on complex diseases.
Hujoel and Loh, in collaboration with Chikashi Terao, a group leader at the Center for Integrative Medicine at the Institute of Physics and Chemistry in Japan and a postdoc at Broad and Brigham Women's Hospital, worked with Loh to apply their model to data from Japan's Biobank and confirmed many of the same trends
.
Loh hopes other researchers will use their software to analyze genomic data
from other biobanks.
"This tool should be easily adapted to do the same analysis on other ancestral groups, which could uncover very different and interesting genetic associations
," he said.
The team says that even at UK Biobank, the vast majority of CNVs remain to be discovered
.
Since large biobanks primarily generate SNP data using arrays that only look at specific locations in the genome, they miss most CNVs
.
Hujoel is tweaking their method so that researchers can use it to study whole exome sequencing data, which can examine all the protein-coding regions
in the genome.
Loh also envisions that others might apply it to whole genome sequencing data to detect CNVs
throughout the genome.
Hujoel said: "There is interest in exploring hidden parts of the genome that have hitherto been invisible to most gene association studies
.
We see our work as both a methodology and hope that it will continue to be useful and applicable to other data sources, as well as more motivation
for people to continue to delve into the ways in which structural change shapes human characteristics.
”
