Better statistical methods to understand gene interactions leading to cancer development
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IMAGE: Hui-Yi Lin, PhD view more 

Credit: LSU Health New Orleans

New Orleans, LA – Research led by Hui-Yi Lin, PhD, Associate Professor of Biostatistics at LSU Health New Orleans School of Public Health, has developed another novel statistical method for evaluating gene-to-gene interactions associated with cancer and other complex diseases. The Additive-Additive 9 Interaction (AA9int) method is described in a paper published in Bioinformatics, available online at https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty461/5034431.

“This method can identify combinations of genetic variants for predicting cancer risk and prognosis,” notes Dr. Lin, who is also the paper’s lead author.

AA9int is based upon another method Lin developed, SNP Interaction Pattern Identifier (SIPI), to identify interactions between single nucleotide polymorphisms (SNPs). According to the National Institutes of Health, “Single nucleotide polymorphisms, frequently called SNPs (pronounced “snips”), are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block, called a nucleotide. Most commonly, these variations are found in the DNA between genes. They can act as biological markers, helping scientists locate genes that are associated with disease. When SNPs occur within a gene or in a regulatory region near a gene, they may play a more direct

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