1. 1. Introduction
2. 2. Details
1. 2.1 Command interface
2. 2.2 Application

## 1.  Introduction

This is implementation for the KBAC statistic in Liu and Leal 20101. It carries out case-control association testing for rare variants for whole exome association studies. Briefly, consider a gene of length n which harbors m rare variants. Genotype on the m variant sites & the disease status (case/control) are known for each individual. The program takes as input the m-site genotype and disease status (case/control) data files, and computes a p-value indicating the significance of association. Permutation has to be used to obtain valid p-values.

An R package is also available for use with standalone text dataset.

Note a couple of differences between this implementation and the original version:

• The original paper provides 3 kernel options: hypergeometric, binomial and Gaussian kernels. The hypergeometric kernel generally performs best and is implemented. Other kernels are not implemented.
• The --alternative 2 option implements the spirit of the RBT test2 by performing two KBAC tests under both protective and deleterious assumptions and use the larger of the two statistics thus calculated as the final KBAC statistic.

## 2.  Details

### 2.1  Command interface

vtools show test KBAC

Name:          KBAC
Description:   Kernel Based Adaptive Clustering method, Liu & Leal 2010
usage: vtools associate --method KBAC [-h] [--name NAME] [-q1 MAFUPPER]
[-q2 MAFLOWER] [--alternative TAILED]

Kernel Based Adaptive Clustering method, Liu & Leal 2010. Genotype pattern
frequencies, weighted by a hypergeometric density kernel function, is compared
for differences between cases and controls. p-value is calculated using
permutation for consistent estimate with different sample sizes (the
approximation method of the original publication is not implemented). Two-
sided KBAC test is implemented by calculating a second statistic with
case/ctrl label swapped, and the larger of the two statistic is used as two-
sided test statistic

optional arguments:
-h, --help            show this help message and exit
--name NAME           Name of the test that will be appended to names of
output fields, usually used to differentiate output of
different tests, or the same test with different
parameters.
-q1 MAFUPPER, --mafupper MAFUPPER
Minor allele frequency upper limit. All variants
having sample MAF<=m1 will be included in analysis.
Default set to 0.01
-q2 MAFLOWER, --maflower MAFLOWER
Minor allele frequency lower limit. All variants
having sample MAF>m2 will be included in analysis.
Default set to 0.0
--alternative TAILED  Alternative hypothesis is one-sided ("1") or two-sided
("2"). Default set to 1
-p N, --permutations N
Number of permutations
confidence interval for binomial distribution. The
program will compute a p-value every 1000 permutations
and compare the lower bound of the 95 percent CI of
p-value against "C", and quit permutations with the
p-value if it is larger than "C". It is recommended to
specify a "C" that is slightly larger than the
significance level for the study. To disable the
adaptive procedure, set C=1. Default is C=0.1
Mode of inheritance. Will code genotypes as 0/1/2/NA
for additive mode, 0/1/NA for dominant or recessive

Example using snapshot vt_ExomeAssociation
 vtools associate rare status -m "KBAC --name kbac -p 5000" --group_by refGene.name2 --to_db\ kbac -j8 > kbac.txt  INFO: 3180 samples are found INFO: 2632 groups are found INFO: Starting 8 processes to load genotypes Loading genotypes: 100% [=====================] 3,180 34.4/s in 00:01:32 Testing for association: 100% [=====================] 2,632/591 18.9/s in 00:02:19 INFO: Association tests on 2632 groups have completed. 591 failed. INFO: Using annotation DB kbac in project test. INFO: Annotation database used to record results of association tests. Created on Wed, 30 Jan 2013 05:26:43  vtools show fields | grep kbac  kbac.refGene_name2 refGene_name2 kbac.sample_size_kbac sample size kbac.num_variants_kbac number of variants in each group (adjusted for specified MAF kbac.total_mac_kbac total minor allele counts in a group (adjusted for MOI) kbac.statistic_kbac test statistic. kbac.pvalue_kbac p-value kbac.std_error_kbac Empirical estimate of the standard deviation of statistic kbac.num_permutations_kbac number of permutations at which p-value is evaluated  head kbac.txt  refGene_name2 sample_size_kbac num_variants_kbac total_mac_kbac statistic_kbac pvalue_kbac std_error_kbac num_permutations_kbac ABCG5 3180 6 87 0.00610092 0.353646 0.00629806 1000 ABCB6 3180 7 151 0.00375831 0.633367 0.00807416 1000 ABCB10 3180 6 122 0.0157014 0.0973805 0.00733189 5000 ABCG8 3180 12 152 -0.00160383 0.876124 0.00861691 1000 ABCA4 3180 43 492 0.0293608 0.387612 0.0142427 1000 ABHD1 3180 5 29 -0.000709548 0.732268 0.00400521 1000 ABCA12 3180 28 312 0.015846 0.509491 0.011858 1000 ABL2 3180 4 41 0.000628395 0.553447 0.00456862 1000 ACADL 3180 5 65 0.00239811 0.501499 0.00545028 1000 
1 Dajiang J. Liu and Suzanne M. Leal (2010) A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions. PLoS Genetics doi:10.1371/journal.pgen.1001156. http://dx.plos.org/10.1371/journal.pgen.1001156
2 Iuliana Ionita-Laza, Joseph D. Buxbaum, Nan M. Laird and Christoph Lange (2011) A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease. PLoS Genetics doi:10.1371/journal.pgen.1001289. http://dx.plos.org/10.1371/journal.pgen.1001289