Tutorial

Tutorial v.1 -- Sungkyoung Choi (June 28, 2016)

FARVATX has functions to test an association between marker sets on the X chromosome and continuous or binary phenotype with related samples.


Tutorial Data Download : SAMPLE_DATA


1. Data description

FARVATX provides an example data set that has a genotype data (test.bed or test.ped) of 2,000 individuals, 100 pedigrees, and 300 markers, a vector of binary phenotype (pheno) and a matrix of covariates (sex and age).

1.1. Ped File

그림6

1.2. Map File

그림9

1.3. Phenotype File

그림7

1.4. Set File

그림4

1.5. Pedigree Structure

: The family structure consist of 10 individuals, and extend over three generations.

그림5

2. Filtering rare variants by minor allele frequency (MAF)

: To analysis of rare variant, we need to extract rare variants of which MAFs were less than 0.01 or 0.05.

  • common variant: MAF >0.05
  • less common variant: 0.01 < MAF < 0.05
  • rare variant: 0< MAF < 0.01

2.1. Calculating Allele Frequency

farvatx --ped test.ped --freq

: This will generate the following files;
res.founders.maf : [CHR], [VARIANT], [MAJOR], [MINOR], [MAF], [MAC] and so on.

2.2. Extracting Rare Variants

> farvatx --ped test.ped --selvariant rare_list.txt --makebed --out rare_test

 

3. Generating Kinship coefficient matrix

farvatx --bed rare_test.bed --makecor --kinship --x2 (will generate res.theo.cor)

 

4. Assign weights for each Marker

: It is generally assumed that rarer variants have larger effect sizes. To incorporate it, we can select the weight terms (W).

  • W = Beta(p,1,25)(defaults)
  • W = 1: --noweight

5. Adjusting Phenotype

: If genotype frequencies of affected and unaffected samples are compared to detect the genetic association, it has been shown that the statistical efficiency can be improved by modifying the phenotype (Lange and Laird, 2002; Thornton and McPeek, 2007).

  • Prevalence: --prevalence
  • BLUP by covariate: --makeblup

5.1. Prevalence

farvatx --bed rare_test.bed --set test.group --genetest --cor res.theo.cor --prevalence 0.12 --genesummary --mispheno -9 --out results_theo_preval

:will generate [out_prefix].gene.res

5.2. BLUP by covariates

Note that the phenotype and covariates must be equivalent for step 1 and 2, otherwise FARVATX will produce error.

# Step 1 : Estimated BLUP

farvatx --bed rare_test.bed --set test.group --makeblup --sampvar test_pheno.txt --pname Pheno --cname SEX,AGE --mispheno NA --cor res.theo.cor --out results_theo_blup

: will generate [out_prefix].[SD/AI].blup and [out_prefix].poly.est.res

# Step 2 : Calculated test statistics

farvatx --bed rare_test.bed --set test.group --genetest --cor res.theo.cor --skato --genesummary --sampvar test_pheno.txt --pname Pheno --cname SEX,AGE --blup [out_prefix].[SD/AI].blup --est [out_prefix].poly.est.res --mispheno NA --out results_theo

: will generate [out_prefix].gene.res

6. Biological Model

: X chromosome inactivation (XCI) and escaped XCI (E-XCI) are efficiently tested with d = 0.5 and d = 1 respectively. Appropriate choices of d for Skewed XCI (S-XCI) toward either the normal and deleterious allele may be d = 0.25 and d = 0.75 respectively.

7. Output files

: This will generate the following files:

*[out_prefilx].gene.res : [CHR], [GENE], [NSAMP], [NVARIANT], [MAC], [P_FARVAT_XB#], [P_FARVAT_XC#], [P_FARVAT_XO#], [P_FARVAT_XD], and so on.

8. References

[1] Lange,C. and Laird,N.M. (2002) Power calculations for a general class of family- based association tests: dichotomous traits. Am. J. Hum. Genet., 71, 575–584.

[2] Thornton,T. and McPeek,M.S. (2007) Case-control association testing with related individuals: a more powerful quasi-likelihood score test. Am. J. Hum. Genet., 81, 321–337.

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