SEQLinkage 1.0.6

Collapsed Haplotype Pattern Method for Linkage Analysis of Next-Generation Sequencing Data

SEQLinkage

Collapsed Haplotype Pattern Method for Linkage Analysis of Next-Generation Sequencing Data

Pre-requisites

Make sure you install the pre-requisited before running seqlink:

conda install -c conda-forge xeus-cling
conda install -c anaconda swig 
conda install -c conda-forge gsl
pip install egglib
git clone https://github.com/statgenetics/cstatgen.git
cd cstatgen
python setup.py install

Install

pip install SEQLinkage

How to use

1. Test on seqlinkage-example

seqlink --fam seqlinkage-example.fam --vcf seqlinkage-example.vcf.gz -f MERLIN --output RMBPt8 --jobs 8

seqlink --fam seqlinkage-example.fam --vcf seqlinkage-example.vcf.gz -f MERLIN --output RMB0 --jobs 8 --bin 0

seqlink --fam seqlinkage-example.fam --vcf seqlinkage-example.vcf.gz -f MERLIN --output RMB1 --jobs 8 --bin 1

seqlink --fam seqlinkage-example.fam --vcf seqlinkage-example.vcf.gz --freq EVSEAAF -o LinkageAnalysis -K 0.001 --moi AR -W 0 -M 1 --theta-max 0.5 --theta-inc 0.05 -j 8 --run-linkage

2. Test on AD family

seqlink --fam data/mwe_normal_fam.csv --vcf data/first1000snp_full_samples.vcf.gz -f LINKAGE --blueprint data/genemap.hg38.txt --freq AF -K 0.001 --moi AD -W 0 -M 1

seqlink --fam data/mwe_normal_fam.csv --vcf data/first1000snp_full_samples.vcf.gz -f MERLIN --blueprint data/genemap.hg38.txt --freq AF

./seqlink --fam seqlinkage-example/seqlinkage-example.fam --vcf seqlinkage-example/seqlinkage-example.vcf.gz -f MERLIN --blueprint data/genemap.txt --freq EVSEAAF -o seqtest
./seqlink --fam data/new_trim_ped_famless17.fam --vcf data/first1000snp_full_samples.vcf.gz -f MERLIN --blueprint data/genemap.hg38.txt --freq AF

./seqlink --fam data/new_trim_ped_famless17.fam --vcf data/first1000snp_full_samples.vcf.gz -f MERLIN --blueprint data/genemap.hg38.txt --freq AF -K 0.001 --moi AD -W 0 -M 1 --run-linkage

./seqlink --fam data/Example_data/pedigree.fam --vcf data/Example_data/example.vcf.gz -f MERLIN MEGA2 PLINK LINKAGE --build hg38 --chrom-prefix chr --freq AF -o data/Example_data/output -K 0.001 --moi AD -W 0 -M 1

./seqlink --fam data/mwe_normal_fam.csv --vcf data/first1000snp_full_samples.vcf.gz --anno data/first1000_chr1_multianno.csv --pop data/full_sample_fam_pop.txt -f MERLIN MEGA2 PLINK LINKAGE --build hg38 --freq AF -o data/first1000test -K 0.001 --moi AD -W 0 -M 1

./seqlink --fam data/new_trim_ped_famless17_no:xx.fam --vcf /mnt/mfs/statgen/alzheimers-family/linkage_files/geno/full_sample/vcf/full_sample.vcf.gz --anno MWE/annotation/EFIGA_NIALOAD_chr1.hg38.hg38_multianno.csv --pop data/full_sample_fam_pop.txt -f MERLIN MEGA2 PLINK LINKAGE --build hg38 --freq AF -o data/fullchr1data -K 0.001 --moi AD -W 0 -M 1 -j 4

Testing output

seqlink --fam seqlinkage-example/seqlinkage-example.fam --vcf seqlinkage-example/seqlinkage-example.vcf.gz -f MERLIN MEGA2 PLINK LINKAGE --blueprint data/genemap.txt --freq EVSEAAF -o data/seqtest_20220221 -K 0.001 --moi AD -W 0 -M 1 -j 4

seqlink --fam data/new_trim_ped_famless17_no:xx.fam --vcf /mnt/mfs/statgen/alzheimers-family/linkage_files/geno/full_sample/vcf/full_sample.vcf.gz --anno MWE/annotation/EFIGA_NIALOAD_chr22.hg38.hg38_multianno.csv --pop data/full_sample_fam_pop.txt -f MERLIN MEGA2 PLINK LINKAGE --build hg38 --freq AF -o data/fullchr22data -K 0.001 --moi AD -W 0 -M 1 -j 8

import pandas as pd

tmp = pd.read_csv('../data/genemap.hg38.txt',sep='\t',header=None)

tmp

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	0	1	2	3	4	5	6
0	1	11868	14362	LOC102725121@1	9.177127e-07	0.000001	6.814189e-07
1	1	11873	14409	DDX11L1	9.195321e-07	0.000001	6.827698e-07
2	1	14361	29370	WASH7P	1.529988e-06	0.000002	1.136045e-06
3	1	17368	17436	MIR6859-1@1,MIR6859-2@1,MIR6859-3@1,MIR6859-4@1	1.217693e-06	0.000002	9.041595e-07
4	1	30365	30503	MIR1302-10@1,MIR1302-11@1,MIR1302-2@1,MIR1302-9@1	2.129597e-06	0.000003	1.581266e-06
...	...	...	...	...	...	...	...
28320	X	155612564	155782457	SPRY3	NaN	196.056662	NaN
28321	X	155881344	155943769	VAMP7	NaN	196.190010	5.600000e+01
28322	X	155997695	156010817	IL9R	NaN	196.305985	NaN
28323	X	156014563	156016830	WASIR1	NaN	196.320452	NaN
28324	X	156025657	156028183	DDX11L16	NaN	196.334645	NaN

28325 rows × 7 columns

tmp[0].value_counts()

1                      2809
2                      1816
19                     1779
11                     1678
17                     1583
3                      1560
6                      1453
12                     1386
7                      1359
5                      1314
X                      1209
16                     1177
9                      1119
10                     1107
4                      1091
8                      1056
15                     1012
14                      946
20                      774
22                      646
13                      629
18                      436
21                      374
17_KI270909v1_alt         3
22_KI270879v1_alt         3
7_KI270803v1_alt          2
15_KI270850v1_alt         2
4_GL000008v2_random       1
1_KI270706v1_random       1
Name: 0, dtype: int64

tmp1 = tmp[tmp[0]=='22']

tmp1[tmp1[3]=='LINC01664']

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	0	1	2	3	4	5	6
26484	22	17121594	17132104	LINC01664	3.496778	5.134655	1.946244

tmp1[tmp1[3]=='BID']

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	0	1	2	3	4	5	6
26493	22	17734139	17774665	BID	7.132236	10.196359	4.192282

tmp1[tmp1[3]=='RTL10']

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	0	1	2	3	4	5	6
26535	22	19846145	19854874	RTL10	12.136091	16.519006	8.210012

tmp1

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	0	1	2	3	4	5	6
26467	22	15784953	15827434	DUXAP8	0.000000	0.000000	0.000000
26468	22	15805697	15820884	BMS1P22@3	0.000000	0.000000	0.000000
26469	22	15805697	15815897	BMS1P17@3,BMS1P18@3	0.000000	0.000000	0.000000
26470	22	15740892	15778287	PSLNR	0.000000	0.000000	0.000000
26471	22	15690025	15721631	POTEH	0.000000	0.000000	0.000000
...	...	...	...	...	...	...	...
27111	22	50674390	50733212	SHANK3	79.970067	90.409669	70.999029
27112	22	50735828	50738169	LOC105373100	80.037493	90.485907	71.073657
27113	22	50738203	50745339	ACR	80.044958	90.494347	71.080286
27114	22	50757085	50799637	RPL23AP82	80.102184	90.559043	71.131103
27115	22	50767506	50783636	RABL2B	80.097821	90.554110	71.127228

646 rows × 7 columns

535-467

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