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A new algorithm makes it possible to perform genetic analyses on as many as 500,000 individuals—and many traits—at the same time, according to an article in Nature Methods.
The authors noted that determining how genetic variations affect health and disease involves analyzing associations between many different variants and multiple traits and making the best use of data from large cohorts that include hundreds of thousands of individuals.
“It is very challenging to identify genetic variants that underlie phenotypes, or traits, and, usually, we do this by analyzing each phenotype and each variant one by one,” explained Oliver Stegle, PhD, of the European Molecular Biology Laboratory-European Bioinformatics Institute in Cambridge, UK.
“But the simple models we use to do this are too simplistic to uncover the complex dependencies between sets of genetic variants and disease phenotypes.”
On the other hand, complex models that reveal the combined action of many different variants have involved so much computation that it would take a year to run a single complex query. But Dr Stegle and his colleagues said their algorithm can change that.
“The breakthrough here is that we’ve made it possible to perform an integrative analysis involving many variants and phenotypes at the same speed as current approaches,” Dr Stegle said.
He and his colleagues tested their new method, called mtSet, on data from 2 studies and compared the results with existing tools used for genetic analysis. The researchers found that mtSet was substantially faster than existing methods and could explain a larger proportion of traits in terms of the genetics that drive them.
The team said mtSet will allow researchers to explore several variants of a gene at once, while comparing them with several related phenotypes. This makes it easier to pinpoint which genes—or locations on genes—are involved in a particular function.
“What’s important about this work is that it improves statistical power and provides the tools people need to analyze multiple traits in very large cohorts,” Dr Stegle said. “Our algorithm can be used to study up to half a million individuals. That hasn’t been possible until now.” 
Image by Spencer Phillips
A new algorithm makes it possible to perform genetic analyses on as many as 500,000 individuals—and many traits—at the same time, according to an article in Nature Methods.
The authors noted that determining how genetic variations affect health and disease involves analyzing associations between many different variants and multiple traits and making the best use of data from large cohorts that include hundreds of thousands of individuals.
“It is very challenging to identify genetic variants that underlie phenotypes, or traits, and, usually, we do this by analyzing each phenotype and each variant one by one,” explained Oliver Stegle, PhD, of the European Molecular Biology Laboratory-European Bioinformatics Institute in Cambridge, UK.
“But the simple models we use to do this are too simplistic to uncover the complex dependencies between sets of genetic variants and disease phenotypes.”
On the other hand, complex models that reveal the combined action of many different variants have involved so much computation that it would take a year to run a single complex query. But Dr Stegle and his colleagues said their algorithm can change that.
“The breakthrough here is that we’ve made it possible to perform an integrative analysis involving many variants and phenotypes at the same speed as current approaches,” Dr Stegle said.
He and his colleagues tested their new method, called mtSet, on data from 2 studies and compared the results with existing tools used for genetic analysis. The researchers found that mtSet was substantially faster than existing methods and could explain a larger proportion of traits in terms of the genetics that drive them.
The team said mtSet will allow researchers to explore several variants of a gene at once, while comparing them with several related phenotypes. This makes it easier to pinpoint which genes—or locations on genes—are involved in a particular function.
“What’s important about this work is that it improves statistical power and provides the tools people need to analyze multiple traits in very large cohorts,” Dr Stegle said. “Our algorithm can be used to study up to half a million individuals. That hasn’t been possible until now.”
Image by Spencer Phillips
A new algorithm makes it possible to perform genetic analyses on as many as 500,000 individuals—and many traits—at the same time, according to an article in Nature Methods.
The authors noted that determining how genetic variations affect health and disease involves analyzing associations between many different variants and multiple traits and making the best use of data from large cohorts that include hundreds of thousands of individuals.
“It is very challenging to identify genetic variants that underlie phenotypes, or traits, and, usually, we do this by analyzing each phenotype and each variant one by one,” explained Oliver Stegle, PhD, of the European Molecular Biology Laboratory-European Bioinformatics Institute in Cambridge, UK.
“But the simple models we use to do this are too simplistic to uncover the complex dependencies between sets of genetic variants and disease phenotypes.”
On the other hand, complex models that reveal the combined action of many different variants have involved so much computation that it would take a year to run a single complex query. But Dr Stegle and his colleagues said their algorithm can change that.
“The breakthrough here is that we’ve made it possible to perform an integrative analysis involving many variants and phenotypes at the same speed as current approaches,” Dr Stegle said.
He and his colleagues tested their new method, called mtSet, on data from 2 studies and compared the results with existing tools used for genetic analysis. The researchers found that mtSet was substantially faster than existing methods and could explain a larger proportion of traits in terms of the genetics that drive them.
The team said mtSet will allow researchers to explore several variants of a gene at once, while comparing them with several related phenotypes. This makes it easier to pinpoint which genes—or locations on genes—are involved in a particular function.
“What’s important about this work is that it improves statistical power and provides the tools people need to analyze multiple traits in very large cohorts,” Dr Stegle said. “Our algorithm can be used to study up to half a million individuals. That hasn’t been possible until now.”