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Biobanks represent valuable resources for identifying the genetic components of complex traits, including rare variants, but high computational costs limit the ability of existing methods to account for confounders such as population stratification and sample relatedness. To address this issue, scientists from the University of Michigan School of Public Health have developed a method — called SAIGE-GENE — for region-based association analysis that is capable of handling very large samples while inferring and accounting for sample relatedness.

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