<jupyter_start><jupyter_text>下载vcftools> 下载命令`conda install vcftools` 准备文件- 家系vcf：trios.vcf 运行命令`vcftools --relatedness2 --vcf trios.vcf --out trios.vcftools`- 注：--out 参数是输入文件前缀，可以任意设置，例如示例中为trios.vcftools，输出对应关系文件为trios.vcftools.relatedness2  示例代码> 输入文件：trios.vcf> 输出文件：trios.test.relatedness2<jupyter_code>vcftools --relatedness2 --vcf trios.vcf --out trios.test<jupyter_output>VCFtools - 0.1.15
(C) <NAME> and <NAME> 2009

Parameters as interpreted:
	--vcf trios.vcf
	--out trios.test
	--relatedness2

After filtering, kept 3 out of 3 Individuals
Outputting Individual Relatedness
After filtering, kept 48642 out of a possible 48642 Sites
Run Time = 1.00 seconds<jupyter_text>运行结果展示<jupyter_code>cat trios.test.relatedness2<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10714285714285714, 'fraction_numerical': 0.03835978835978836, 'mean_word_length': 5.81981981981982, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '545790', 'license': 'MIT', 'n_tokens_mistral': 362, 'n_tokens_neox': 350, 'n_words': 62, 'path': 'notebooks/Untitled.ipynb', 'repo_name': 'yipukangda/work_notebooks', 'size': '771'}