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  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]
                                      [-p N] [--adaptive C]
                                      [--moi {additive,dominant,recessive}]

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
  -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
  --adaptive C          Adaptive permutation using Edwin Wilson 95 percent
                        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
  --moi {additive,dominant,recessive}
                        Mode of inheritance. Will code genotypes as 0/1/2/NA
                        for additive mode, 0/1/NA for dominant or recessive
                        model. Default set to additive

2.2  Application

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