1:缺失比例(Missing

rates)：(

GENO > 0.05 )

Shortly we will

apply more stringent criteria, such that GENO > 0.05. In this

case, 0.05*89 = 4.45 samples, meaning that if a SNP is missing in

4.45 more more samples, that SNP will be removed from the

dataset.

2:最小等位基因频率(Minor

Allele frequencies)(

MAF< 0.03 如果SNP较多可以设置为MAF<0.05)

MAF

is the Minor Allele Frequency. It can be used to exclude SNPs which

are not informative because they show little variation in the

sample set being analyzed. For instance, if a SNP shows variation

in only 1 of the 89 individuals, it is not useful statistically and

should be removed.

3:Removing SNPs

out of Hardy-Weinberg equilibrium(p-value > 10−6 -

10−4

)

Population

genetic theory suggests that under ‘normal’ conditions, there is a

predictable relationship between allele frequencies and genotype

frequencies. In cases where the genotype distribution is different

from what one would expect based on the allele frequencies, one

potential explanation for this is genotyping error. Natural

selection is another explanation. For this reason, we typically

check for deviation from Hardy-Weinberg equilibrium in the controls

for a case- control study. For a quantitative trait, PLINK just

uses everyone. The following command generates p-values for

deviation from HWE for each SNP. Low p-values indicate that a SNP

is out of HWE.

4:如果你有vcf文件，可以先用vcftools转换为plink的输入形势，输出结果为：.bed与.map文件，然后以此作为输入进行过滤：

vcftools --vcf

my.vcf --plink --out plink

plink

--noweb --file plink --geno 0.05 --maf 0.05 --hwe 0.0001 --make-bed

--out QC

参考文献：

Roshyara N R,

Kirsten H, Horn K, et al. Impact of pre-imputation SNP-filtering on

genotype imputation results[J]. BMC genetics, 2014, 15(1):

1.

Pongpanich M,

Sullivan P F, Tzeng J Y. A quality control algorithm for filtering

SNPs in genome-wide association studies[J]. Bioinformatics, 2010,

26(14): 1731-1737.