28 Rare variants are thought to play an important role in the etiology of complex diseases and may 29 explain a significant fraction of the missing heritability in genetic disease studies. Next-30 generation sequencing facilitates the association of rare variants in coding or regulatory regions 31 with complex diseases in large cohorts at genome-wide scale. However, rare variant association 32 studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation 33 explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant 34 Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS 35 tests, BATI allows integration of individual or variant-specific features as covariates, while 36 efficiently performing inference based on full model estimation. We demonstrate that BATI 37 outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing 38 cohorts, especially when using meaningful biological context, such as functional annotation. We 39 show that BATI achieves power above 75% in scenarios in which competing tests fail to identify 40 risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We 41 have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide 42 Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome 43 sequencing. rvGWAS supports rare variant association for genes or any other biological unit 44 such as promoters, while allowing the analysis of essential functionalities like quality control or 45 filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight 46 candidate predisposition genes, including EHMT2 and COPS7A.47 Data availability and implementation 48 All relevant data are within the manuscript and pipeline implementation on49 https://github.com/hanasusak/rvGWAS 3 50 Author summary 51 Complex diseases are characterized by being related to genetic factors and environmental52 factors such as air pollution, diet etc. that together define the susceptibility of each individual to 53 develop a given disease. Much effort has been applied to advance the knowledge of the genetic 54 bases of such diseases, specially in the discovery of frequent genetic variants in the population 55 increasing disease risk. However, these variants usually explain a little part of the etiology of 56 such diseases. Previous studies have shown that rare variants, i.e. variants present in less than 57 1% of the population, may explain the rest of the variability related to genetic aspects of the 58 disease.59 Genome sequencing offers the opportunity to discover rare variants, but powerful statistical 60 methods are needed to discriminate those variants that induce susceptibility to the disease. 61 Here we have developed a powerful and flexible statistical approach for the detection of rare 62 variants associated with a disease and we have integrated it into a computer tool that is eas...