Computational Science students Louis Ehwerhemuepha and Alexandria Smith traveled to Boston to make a poster presentation on October 23rd at the annual meeting of the American Society of Human Genetics (ASHG). They presented their research work titled: “A Novel Exact Case-Control Test for Association.”




Computational Science students Louis Ehwerhemuepha and Alexandria Smith (photo attached) traveled to Boston to make a poster presentation on October 23 at the Annual Meeting of the American Society of Human Genetics (ASHG)


Alexandria Smith and Louis Ehwerhemuepha

The real-world implications for this research is to find a particular gene that is responsible for a complex human disorder.

Louis is a PhD. Student in the inaugural Ph.D. in Computational Sciences program. Alexandria is earning her masters degree in computational sciences. They began working on the research in the spring and submitted their abstract to the conference in June. They developed a general non-parametric test that compared multinomial distributions in small samples, studied its properties, created the software implementation and applied it to genotype/phenotype data. Initially, they applied it to simple settings with a single gene.

Later, they extended their work to encompass multiple genes and even and even scenarios when full genetic information was unknown and needed to be estimated analytically using the Expectation Maximization (EM) algorithm.

“The great thing about their work is creating a statistical approach that enables us to detect association between genes and diseases,” said faculty advisor Cyril Rakovski. “This attempts to answer that question.”

The ASHG conference is the biggest conference on genetics in the world. This year’s – the 63rd annual – was the biggest so far, attracting 10,000 attendees and over 500 poster presentations. Of the presentations, Louis and Alexandria were part of the few comprised only of students – a prestigious honor for them and for Chapman.

Louis called participating in the conference, “a beautiful experience.”

“This is not considered big data but rather, “big calculations” joked Rakovski. “Genotyping data is moving faster than technology and statistical models, making this research all the more important and necessary for the future.”

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