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New Statistical Method Could Cut Computation Time for Genetic Studies


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UCLA Associate Professor of Computer Science Eleazar Eskin

"We made a few simplifying assumptions that allowed us to dramatically increase the speed of computations," says UCLA Associate Professor of Computer Science Eleazar Eskin.

Credit: University of California, San Diego

University of California, Los Angeles (UCLA) researchers have developed a new computational strategy for genome-wide association studies, which enables the scanning of up to a million genetic markers in thousands of individuals and corrects for population structure. The new strategy, called Efficient Mixed Model Association Expedited, captured the complex mixture of both population structure and hidden relatedness, and corrected these relationships when creating a genetic map.

"Capitalizing on the characteristics of complex traits in humans, we made a few simplifying assumptions that allowed us to dramatically increase the speed of computations, making our approach readily applicable to genome-wide association studies with tens of thousands of samples," says UCLA professor Eleazar Eskin.

Eskin says the method also could have a large impact on admixed populations, which are samples of individuals who have ancestry from multiple regions of the world.

From UCLA Newsroom
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