Benchmark for detecting large genetic mutations linked to major diseases


Date:
June 15, 2020
Source:
National Institute of Standards and Technology (NIST)
Summary:
Researchers have developed a way for laboratories to determine
how accurately they can detect large mutations. The new method
and the benchmark material enable researchers, clinical labs and
commercial technology developers to better identify large genome
changes they now miss and will help them reduce false detections
of genome changes.



FULL STORY ==========================================================================
Many serious diseases, including autism, schizophrenia and numerous
cardiac disorders, are believed to result from mutation of an individual's
DNA. But some large mutations, which still make up only a small fraction
of the total human genome, have been surprisingly challenging to detect.


==========================================================================
Now, researchers at the National Institute of Standards and Technology
(NIST) have developed a way for laboratories to determine how accurately
they can detect these mutations, which take the form of large insertions
and deletions in the human genome. The new method and the benchmark
material enable researchers, clinical labs and commercial technology
developers to better identify large genome changes they now miss and
will help them reduce false detections of genome changes.

The researchers present their new benchmark in Nature Biotechnology.

Scientists in the Human Genome Project generated the first reference
genome in the late 1990s, pieced together from a collection of genome
sequences from different individuals. When scientists sequence DNA,
they are essentially randomly chopping up the DNA into smaller pieces,
which then need to be pieced back together like a puzzle.

The building blocks of DNA include four types of bases: adenine
(A), cytosine (C), guanine (G) and thymine (T), strung together to
form 23 chromosomes in human cells. These genetic codes contain all
the information of life. To understand the genetic basis for a given
disease, scientists sequence a person's DNA and compare it against a
reference genome. Differences between the individual's DNA sequence
and the reference genome are called variants. Some of these variants,
which can range from insertions and deletions of 50 to tens of thousands
of letters (or bases) of the roughly 6.4 billion bases that make up the
human genome, are found to be linked to a disease.

Over the last eight years, the NIST-led Genome in a Bottle consortium
(GIAB), which includes members from the federal government, academia and industry, developed whole human genome benchmarks for small variants
for seven individuals. For this new paper, NIST worked with GIAB to
develop a new benchmark for large insertions and deletions. To form this benchmark, NIST integrated results from 19 different analysis approaches
by GIAB members, using GIAB's public data from a well-characterized set
of human DNA from a family of Eastern European Ashkenazi Jewish ancestry
(NIST Reference Material 8392).



==========================================================================
The NIST Genome in a Bottle Consortium is a public-private-academic
consortium hosted by NIST to develop the technical infrastructure
(reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. In
this animation, learn more about the genome sequencing process and why standards are such an important part of this process.

"Just like a company making rulers could compare their ruler to a
standard measuring stick to make sure it is measuring the correct
distance, clinical laboratories doing DNA sequencing can measure NIST
reference material DNA and compare their answer to this new benchmark
to help make sure they measure large insertions and deletions well,"
said NIST biomedical engineer Justin Zook.

Laboratories have accurately detected many small insertions and deletions
in the genome for years. One would think detecting larger insertions
and deletions would be easier, but it's actually harder because "the
most widely used sequencing technologies output relatively short strings
of genetic code, making it hard to reconstruct what's happening," says
Zook. With new DNA sequencing technologies, it is now possible to detect
many more large insertions and deletions.

Imagine the genome as a book. The benchmark helps scientists detect
large chapters that are missing (deleted chapters) or not in the original (inserted chapters).

"DNA sequencing is like shredding the book into smaller pieces and then
trying to find any differences between the book that was shredded and
a similar book, perhaps the same book before it went through editorial revisions," said Zook.

Even though the DNA is broken into smaller pieces, the new DNA sequencing technologies make it possible to read the larger pieces, making it easier
to find these larger insertions and deletions.



==========================================================================
The NIST Genome in a Bottle Consortium is a public-private-academic
consortium hosted by NIST to develop the technical infrastructure
(reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. In
this animation, learn more about why developing these reference materials
is so important.

This benchmark for large insertions and deletions will improve the
accuracy of DNA sequencing technologies and analysis methods, reducing
the likelihood of errors such as false positives and negatives. A false positive means detecting an insertion or deletion in the genome that's
not real, while a false negative means not detecting a change in the
genome when it's actually there.

Reducing false positive and negative numbers is critical, especially
in clinical settings where many diseases such as autism, schizophrenia
and cardiovascular disease have been linked to structural variants. For example, if a clinical laboratory is sequencing a patient's DNA, a false negative can result in missing the change in the genome that is causing
the disease, leading to incorrect treatments.

Down the road, applications of the benchmark will help labs detect
disease- associated structural variants by validating their methods.

For NIST researchers, next steps include characterizing difficult regions
of the genome that contain repetitive sequences. DNA sequence technologies
and methods continue to improve, enabling researchers to push into more challenging regions of the genome and identify structural variants that
are harder to detect.

But according to Zook, this is precisely why this area is fun to work
in, as technologies have changed and improved in the past 30 years. He
credits the collaboration with GIAB as being key to these efforts: "All
of this work wouldn't be possible if we weren't able to collaborate with
a group of diverse people with different areas of expertise."

========================================================================== Story Source: Materials provided by National_Institute_of_Standards_and_Technology_(NIST).

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Justin M. Zook, Nancy F. Hansen, Nathan D. Olson, Lesley Chapman,
James
C. Mullikin, Chunlin Xiao, Stephen Sherry, Sergey Koren, Adam M.

Phillippy, Paul C. Boutros, Sayed Mohammad E. Sahraeian, Vincent
Huang, Alexandre Rouette, Noah Alexander, Christopher E. Mason,
Iman Hajirasouliha, Camir Ricketts, Joyce Lee, Rick Tearle, Ian
T. Fiddes, Alvaro Martinez Barrio, Jeremiah Wala, Andrew Carroll,
Noushin Ghaffari, Oscar L. Rodriguez, Ali Bashir, Shaun Jackman,
John J. Farrell, Aaron M.

Wenger, Can Alkan, Arda Soylev, Michael C. Schatz, Shilpa Garg,
George Church, Tobias Marschall, Ken Chen, Xian Fan, Adam
C. English, Jeffrey A.

Rosenfeld, Weichen Zhou, Ryan E. Mills, Jay M. Sage, Jennifer
R. Davis, Michael D. Kaiser, John S. Oliver, Anthony P. Catalano,
Mark J. P.

Chaisson, Noah Spies, Fritz J. Sedlazeck, Marc Salit. A
robust benchmark for detection of germline large
deletions and insertions. Nature Biotechnology, 2020; DOI:
10.1038/s41587-020-0538-8 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200615140819.htm

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