Nature Methods: Identify rare genetic variants in whole genome sequencing studies

A team of researchers at Indiana University School of Medicine has developed specialized bioinformatics software designed to identify rare genetic variants
in whole-genome sequencing studies.

Dr.
Zilin Li, assistant professor of biostatistics and health data science, is the first author and co-corresponding author recently published in the journal Nature Methods, which details a variant-association testing framework
using STAARpipeline.

"Despite hundreds of millions of rare genetic variants, their research has been challenging because there is no convenient, scalable, and robust pipeline for comprehensive rare variant analysis, which requires evaluation of sets of variants rather than individual variants," Li said
.

STAARpipeline allows researchers to assess a range of rare noncoding genetic variants that will aid genetic research
.
Noncoding genetic variants are parts of the genome that do not code for amino acids, which are molecules
that bind to form proteins.
More than 98 percent of people's DNA is non-coding
.

"Rare variants can be observed in 99% of the human genome, a major source of heritability for complex traits and disease loss"
.

To use STAARpipeline, researchers input genotype (genetic code) and phenotype (complex traits or disease code) data
into the program.
The software analyzes data and identifies rare variants, grouping variants into 8 functional categories in gene center analysis and variants into fixed-size sliding windows and newly proposed data adaptive dynamic windows
in non-gene center analyses.
Gene center analysis focuses on variation within or near genes, while non-gene center analysis focuses on variation in intergenic regions, that is, pieces of
DNA located between genes.
The program then annotates multiple variable functions for each variable collection to further increase the analytical power and summarize the results for the
user.

The research team has tested STAARpipeline on large sample sizes, including 40,000 from the National Heart, Lung, and Blood Institute (NHLBI) Crossomics Precision Medicine Program
.
During the analysis, STAARpipeline found significant associations for 49 gene-centric noncoding analyses, 35 of which were based on 6 new noncoding categories
.
In addition, data adaptive size dynamic window analysis detected 43 non-overlapping significant associations in noncoding genomes, 19.
4%
more than the classical fixed-size sliding window method.

STAARpipeline builds on another program built by Li and his colleagues called STAAR, a genetic variant test
that uses annotated information to find connections and associations.

"We believe that STAARpipeline can scale to analyze variations in hundreds of millions of whole genome sequencing data," Li said
.
"Since rare variants are found in 99% of the human genome, this project addresses an important gap
in information analysis.
"