Tutorial

The PED, MAP, phenotype, and sample information files should be named with the suffix of “.ped”, “.map”, ”.phe”, and “.txt”, respectively.

example.ped file:
The ped file is white-space (space or tab) delimited and contains genotypes for each sample. The first six columns are mandatory: Family ID; Individual ID; Paternal ID; Maternal ID; Sex (1=male, 2=female, other=unknown); and Phenotype (-9 for missing value). The IDs should only consist of numbers and letters. Please refer to the format reference for details (https://www.cog-genomics.org/plink/1.9/formats#ped).

example.map file:
The map file is white-space delimited and includes four columns: chromosome ID, SNP ID, genetic distance (cM), and genomic coordinates (bp). We recommend users to name the chromosomes with numbers. Otherwise, the chromosome IDs will be replaced to numbers automatically. The modified IDs will be stored in the file of “Chr.rename.txt”. Please refer to the format reference for details (https://www.cog-genomics.org/plink/1.9/formats#map).

example.phe file:
The phe file is white-space delimited file and includes three columns: family ID, individual ID, and phenotypes. The phenotype can be continuous or discrete (0 and 1). Please note that only the phenotype from the 3rd column is extracted for analysis. Please refer to the format reference for details (https://www.cog-genomics.org/plink/1.9/input#pheno).

sample_information.txt:
The file has columns of sample IDs , years of birth, genders , source of samples, estimated breeding values. The headers of every columns must be named as “Indiv”, “Born”, “Sex”, “Source”, and “EBV”, respectively.

Input files

Users can upload one or more VCF files and carry out online imputation with corresponding reference panels by Beagle and Glimpse (Browning, et al., 2018; Rubinacci, et al., 2021). As Glimpse requires the genotype likelihoods (GL) or the phred-scaled genotype likelihoods (PL), please make sure your VCF files contain these fields if you would like to use Glimpse for imputation. And a qualified VCF could be generated by common VCF processing tools such as GATK, ATLAS and bcftools (Li, 2011). For more details, please refer to the manuals of Beagle and Glimpse.

Note:
(1) Your input files should be named as ${chr_name}.vcf.gz. You can check the list of chromosomes for each species on the Online imputation page.
Example:
lg3.vcf.gz
lg36.vcf.gz

(2) The header of VCF files should have the information of chromosomes.
Example:
lg3.vcf.gz
lg36.vcf.gz
##contig=<ID=lg3,length=57207694>
##contig=<ID=lg36,length=23119636>

(3) The sample IDs should only consist of numbers and letters.

Output files
Users will get imputed genotypes in vcf.gz format. A ped file and map file will also be generated for subsequent analysis. By default, genotypes with low genotype probabilities (GP) will be filtered. The global imputation rate and linkage disequilibrium (LD) patterns will be visually illustrated for chromosomes longer than 2M bp.

Input file
Genetic structure inference requires three input files: genotypes (ped), marker positions (map), and sample_information (txt, optional). Please refer to the File Format section for details. The sample_information should contain columns of “Indiv” and “Source”. The sample IDs should only consist of numbers and letters.

Output results
Ancestry estimation and principal component analysis will be performed to infer the population structure (Alexander, et al., 2009; Purcell, et al., 2007). The optimal number of ancestries will be checked by cross-validation.

Input files
Kinship analysis requires two input files (ped, map) to identify pairwise identical by descent (IBD) segments and estimate pairwise kinship coefficients.

Output files
Relationships between the individuals of the population will be evaluated by KING (Manichaikul, et al., 2010). The kinship coefficient of each pairs will be plotted against the proportion of SNPs with two different alleles (IBS0). All pairs will be classified into five degrees: duplicate/monozygotic twin, full sib/parent–offspring, 2nd-degree (such as half-sibs), 3rd-degree (such as first cousins), and unrelated relationships. The results could also be downloaded in tabular format.

Input files
GWAS requires three input files (ped, map, and phe). Please note that the sample IDs should only consist of numbers and letters. Pairwise linkage disequilibrium (LD) of SNPs were measured as the squared correlation between phased alleles (r²). By default, r² threshold of 0.5 was applied to prune SNPs in relatively high LD. Loci with missing rate over 20% will also be trimmed. Genome-wide association analysis will be conducted using a univariate linear mixed model with centered relatedness matrix (Zhou, Stephens, 2012).

Output results
Statistical significance of association will be evaluated by Wald test and illustrated in a Manhattan plot. The pipeline will also exhibit the deviations of the observed P values from the expectations of a theoretical uniform distribution in a QQ plot. The result could be downloaded in tabular format, which contains 11 columns: chromosome numbers, SNP ID, base pair positions on the chromosome, number of missing values for a given SNP, minor allele, major allele, allele frequency, beta estimates, standard errors for beta, remle estimates for lambda, and P values from Wald test.

Input files
GS analysis uses dataset from the reference population and prediction population. The input files for the reference and candidate population should be prepared separately and have an identical set of genetic markers. The PED, MAP, and phenotype files for the reference population should be named as “mydata.ref.ped”, “mydata.ref.map”, and “mydata.ref.phe”. The PED and MAP files for the candidates should be named as “mydata.predict.ped” and “mydata.predict.map”. Please note that the sample ID should only consist of numbers and letters. We implanted three models (GBLUP, GBayes, SNN) to predict the genomic estimated breeding values (GEBV) (Wang, et al., 2018; Wimmer, et al., 2012). By default, r² threshold of 0.5 was applied to prune SNPs in relatively high LD. Loci with missing rate over 20% will also be trimmed.User can also enable10-fold Cross-validation on reference dataset in step 3.

Output files
The predicted GEBVs will be illustrated according to the sample phylogeny. The estimated marker effects will also be shown to facility inspecting markers of interest. The result could be downloaded in tabular format. Users can retrieve the GEBV from the results for subsequent GM analysis.

Input files
GM analysis requires four input files: genotypes (ped), marker positions (map), phenotype (phe), and sample_information (txt). Please refer to the File Format section for details. The file of sample_information should contain columns of “Indiv”, “Born”, and “Sex”. The sample IDs should only consist of numbers and letters. Users can include the pre-calculated GEBVs in the column named “EBV”. Otherwise, the pipeline will calculate GEBV with GBLUP. By default, r² threshold of 0.5 was applied to prune SNPs in relatively high LD. Loci with missing rate over 20% will also be trimmed.

Output results
The optimum contribution selection (OCS) and mating strategy will be made according to maximum genetic gain with a constraining increase in mean kinship (Wellmann, 2019). The threshold for a kinship K is set according to the formula:

Where K_g0 is the mean kinship in the population at generation g₀, L is the generation interval in years. By default,N_e is set as 200 to avoid inbreeding depression. The mate allocation and optimum number of offspring will be illustrated in a heatmap and a chord plot. The results could also be downloaded in tabular format.

Input files
Simulation analysis requires four input files: genotypes (ped), marker positions (map), phenotype (phe). Please refer to the File Format section for details.

Output results
The simulation results of 30 generations will be output. And the genetic gain and rate of inbreeding will be displayed.

To run AMBP on large dataset locally, the users may use Docker and Docker Hub. Please install Docker first (https://docs.docker.com/get-docker/).
1.Acquire Docker image from Docker hub.

docker pull ouc2021mgb/ambp:latest

2.For genotype imputation, please download the reference panel of interest from the server and move them to the directory that will be mounted in the docker container.

mkdir -p /host_path/host_analysis/reference_panel/L_vannamei
mv Litopenaeus_vannamei.all_vcf_v1_file.tar /host_path/host_analysis/reference_panel/L_vannamei
tar -xvzf /host_path/host_analysis/reference_panel/L_vannamei/Litopenaeus_vannamei.all.vcf.tar

3. Prepare your input files according to the tutorial above and move them to the directory that will be mounted in the docker container.

mkdir -p /host_path/host_analysis/impute_example
mv input.vcf.gz /host_path/host_analysis/impute_example

4. Prepare a config file, you can download the template file and adjust it to fit your analysis. The config file should be deposited in the directory that will be mounted in the docker container.

mkdir -p /host_path/to/host_analysis/impute_input
mv allinputfile.vcf.gz /host_path/host_analysis/impute_input

5. Analyze your data in the docker container.

 docker run -v /host_path/host_analysis/:/docker_path/ -it ouc2021mgb/ambp:latest /bin/bash /docker_path/imputation.config 

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Browning, B.L., Zhou, Y., Browning, S.R., 2018. A One-Penny Imputed Genome from Next-Generation Reference Panels. Am J Hum Genet. 103, 338-348.
Li, H., 2011. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 27, 2987-2993.
Manichaikul, A., Mychaleckyj, J.C., Rich, S.S., Daly, K., Sale, M., Chen, W.M., 2010. Robust relationship inference in genome-wide association studies. Bioinformatics. 26, 2867-2873.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., De Bakker, P.I., Daly, M.J., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics. 81, 559-575.
Rubinacci, S., Ribeiro, D.M., Hofmeister, R.J., Delaneau, O., 2021. Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat Genet. 53, 120-126.
Wang, Y., Mi, X., Rosa, G.J.M., Chen, Z., Lin, P., Wang, S., Bao, Z., 2018. Technical note: an R package for fitting sparse neural networks with application in animal breeding. J Anim Sci. 96, 2016-2026.
Wellmann, R., 2019. Optimum contribution selection for animal breeding and conservation: the R package optiSel. BMC Bioinformatics. 20, 25.
Wimmer, V., Albrecht, T., Auinger, H.J., Schon, C.C., 2012. synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics. 28, 2086-2087.
Zhou, X., Stephens, M., 2012. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 44, 821-824.