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Large-scale biobanks are being collected around the world in efforts tobetter understand human health and risk factors for disease. They often surveyhundreds of thousands of individuals, combining questionnaires with clinical,genetic, demographic, and imaging assessments; some of this data may becollected longitudinally. Genetic associations analysis of such datasetsrequires methods to properly handle relatedness, population structure and othertypes of biases introduced by confounders. Most popular and accurate approachesrely on linear mixed model (LMM) algorithms, which are iterative andcomputational complexity of each iteration scales by the square of the samplesize, slowing the pace of discoveries (up to several days for single traitanalysis), and, furthermore, limiting the use of repeat phenotypicmeasurements. Here, we describe our new, non-iterative, much faster andaccurate Two-Step Linear Mixed Model (Two-Step LMM) approach, that has acomputational complexity that scales linearly with sample size. We show thatthe first step retains accurate estimates of the heritability (the proportionof the trait variance explained by additive genetic factors), even whenincreasingly complex genetic relationships between individuals are modeled.Second step provides a faster framework to obtain the effect sizes ofcovariates in regression model. We applied Two-Step LMM to real data from theUK Biobank, which recently released genotyping information and processed MRIdata from 9,725 individuals. We used the left and right hippocampus volume (HV)as repeated measures, and observed increased and more accurate heritabilityestimation, consistent with simulations.

A Fast, Accurate Two-Step Linear Mixed Model for Genetic Analysis Applied to Repeat MRI Measurements