Akbari A, Vitti JJ, Iranmehr A, Bakhtiari M, Sabeti PC,
Mirarab S, Bafna V. Identifying the favored mutation in a positive selective
sweep. Nat Methods. 2018 Feb 19. doi: 10.1038/nmeth.4606. [Epub ahead of print]
Abstract
Most approaches that capture signatures of selective sweeps
in population genomics data do not identify the specific mutation favored by
selection. We present iSAFE (for "integrated selection of allele favored
by evolution"), a method that enables researchers to accurately pinpoint
the favored mutation in a large region (∼5 Mbp) by using a statistic
derived solely from population genetics signals. iSAFE does not require
knowledge of demography, the phenotype under selection, or functional
annotations of mutations.
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In a new study published in Nature Methods, scientists have
developed a new algorithm that allows for the prediction of mutations favored
by natural selection in large regions of the human genome. What does this new
study mean for treatment options for genetic disorders?
Researchers needed to study the sequenced genome of a
population size of 1000 individuals, so they turned to computational techniques
to help perform this project. Researchers created an algorithm entitled iSAFE,
which is able to analyze a certain region of the genome and determine which
mutation is favored by which selection. Previous studies have been able to
detect which regions of the human genome are evolving under which selection
pressure, but have not been able to shed light on the specific mutation that
responds to that particular selective pressure. This algorithm however, does
not need to know the function of the genomic region it is analyzing nor does it
require any demographic information since it works by reading population
genetic signals imprinted on the genomes of the sampled individuals to identify
the mutation. During nature selection, neighboring mutations can essentially
“hitchhike” with a mutation that is under positive selection causing a loss in
genetic diversity near that mutation. iSAFE is able to exploit the signals of
neighboring mutations in order to pinpoint the location of the favored
mutation. The algorithm is shedding light on the possibility of understand
genetic disorders and possibly pinpoint underlying causes of those disorders;
hopefully, paving the way to potential therapeutic targets.
A team of scientists has developed an algorithm that can
accurately pinpoint, in large regions of the human genome, mutations favored by
natural selection. The finding provides deeper insight into how evolution
works, and ultimately could lead to better treatments for genetic disorders.
For example, adaptation to chronic hypoxia at high altitude can suggest targets
for cardiovascular and other ischemic diseases.
The sequenced genome of a single individual yields about
half a terabyte of data of information—that's about as much information as
you'll find on 106 DVDs. A population sample of size 1000 individuals contains
1000 times as much information. So to examine such a massive amount of data,
researchers turned to computational techniques.
"Computer science and data science are playing a
significant role to better understand the code of life and uncover the hidden
patterns in our genome," said Ali Akbari, the paper's first author and a
Ph.D. student in electrical and computer engineering at the University of
California San Diego. "We are analyzing massively large sets of human
genomic data to ultimately improve our understanding of genetic basis of
diseases."
Researchers detail the algorithm, dubbed iSAFE, in the Feb.
19 issue of Nature Methods.
Many existing genomic analysis approaches can detect which
regions of the human genome are evolving under selection pressure. Often, these
regions are large, covering millions of base-pairs and do not shed light on the
specific mutations that are responding to the selection pressure. iSAFE doesn't
need to know the function of the genomic region it is analyzing or any
demographic information for the human population it belongs to. Instead, the
researchers used population genetic signals imprinted in the genomes of the
sampled individuals and machine learning techniques to reliably identify the
mutation favored by selection.
In natural selection, neighboring mutations 'hitchhike' with
the mutation that is under positive selection, leading to a loss of genetic
diversity near the favored mutation. iSAFE exploits signals in the neighboring
sequences, the so-called "shoulder regions" to pinpoint the favored
mutation.
"Finding the favored mutation among tens of thousands
of other, hitchhiking, mutations was like a needle in a haystack problem,"
said Akbari, who works in the research group of computer science professor
Vineet Bafna at the Jacobs School of Engineering at UC San Diego.
To test the algorithm, researchers ran iSAFE on regions of
the genome that are home to known favored mutations. The algorithm ranked the
correct mutation as the top one out of more than 21,000 possibilities in 69
percent of cases, as opposed to state of the art methods, which only did this
in 10 percent of cases.
The algorithm also identified a host of previously unknown
mutations, including five that involve genes related to pigmentation. In these
cases, iSAFE identified identical mutations in multiple non-African
populations. This suggests an early response to the onset of selection as
humans migrated out of Africa.
https://medicalxpress.com/news/2018-02-algorithm-mutations-favored-natural-large.html
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