Documentation

General

Many genome projects have generated a deluge of whole-genome data from modern domesticated goats, ancient goats, and their wild relatives, to identify genetic diversity, selective regions and historic introgression events that involved in domestication, subsequent intensive breeding, or climate-driven adaptation. Nevertheless, there is no dedicated database integrating these resource. Here we used a uniform pipeline to develop a comprehensive goat database (GGVD) which focuses on high-quality SNPs, indels, selective regions, and introgression from 208 modern domestic goats, 24 bezoars, 46 ibex, and 82 ancient goats. A total of ~41.44 M SNPs and ~5.14 M indels were identified in modern goat samples. Selected loci were implemented through eight statistical tests (FST, Pi ratio, XP-EHH, Pi, Hp, Tajima’s D, CLR, and iHS). Introgression regions between ibex species and domestic goats were also integrated into database. Users can freely visualize the frequency of genomic variations in geographical maps, selective regions in interactive tables, Manhattan plots or line charts, and especially, the SNP genotype patterns in heatmaps. Ancient goats were used to track the spatiotemporal trajectories of each genetic variant. Moreover, we have introduced the UCSC Genome Browser, BLAT, BLAST, LiftOver, and pcadapt into database. GGVD allows users to identify breeding-associated candidate genes and variants, and track the state of variants before, during and following selection and introgression events. GGVD will be a useful archive for future genetic studies and goat breeding.

Related articles:

1. Zheng Z., Wang X., Li M., Li Y., ... Chen Y & Jiang Y. (2020) The origin of domestication genes in goats. Science Advances, 6, eaaz5216.
2. Cai Y., Fu W., Cai D., Heller R., Zheng Z., ... Jiang Y & Wang X. (2020) Ancient genomes reveal the evolutionary history and origin of cashmere producing goats in China. Molecular Biology and Evolution, 37, 2099-2109.

Methods

I. Pipeline of SNPs and indels calling

1. All raw sequencing data were obtained from Sequence Read Archive (SRA) of NCBI. We integrated a total of 360 samples containing 208 modern domestic goats (70 Africa, 31 Europe, 5 Australia, 36 Southwest Asia, 9 South Asia, and 57 East Asia), 24 bezoars, 82 ancient goats (spanning the last ~10,000 years), and six ibex species (30 Alpine ibex, 4 Iberian ibex, 5 Siberian ibex, 4 Markhor, 2 Nubian ibex, and 1 West Caucasian tur).
2. The adapter sequences and low-quality reads were trimmed using fastp. Then cleaned reads were mapped to the latest goat reference genome ARS1 (GCF_001704415.1) using BWA v0.7.15. Modern samples use BWA-mem algorithm while ancient samples use BWA-aln algorithm. The alignment BAM files were processed to sort reads, merge sample, and mark duplicates by Picard v2.1. Then, both Genome Analysis Toolkit (GATK) HaplotypeCaller (version 3.7) and Analysis of next generation Sequencing Data (ANGSD, version 0.918) were implemented to detect the SNPs in all samples. Only biallelic sites detected in both methods with a quality score greater than 50 and no more than 10% missing data were used.
3. For indel calling, we first sifted structural variations for each sample by GATK with the SelectVariants-based method. Then, we applied the hard filter command "VariantFiltration" to exclude potential false-positive variant calls with the parameter –filterExpression "QD < 2.0 || FS > 200.0 || ReadPosRankSum < −20.0 || InbreedingCoeff < −0.8". Finally, we only retained the 1–30 bp indels for downstream analysis.
4. Beagle software was used to phase the identified SNPs in goat.
5. Annotation of SNPs and indels was performed by using snpEff.
6. Minor allele frequencies (MAF) for all goats, and allele frequencies for each goat group were calculated with PLINK and VCFtools.

II. Population structure

1. The PCA of the SNPs was performed using the smartpca programme in EIGENSOFT v5.0. The Tracy-Widom test was used to determine the significance level of the eigenvectors. For phylogenetic analysis, a neighbour-joining tree was constructed with RapidNJ with 100 bootstraps.
2. Combined our previous result, eight geographically distributed genetic components can be roughly ascribed to: Bezoars, African goats, African dairy goats, European goats, Australian goats, Southwest Asian goats, South Asian goats, and East Asian goats.

III. Selection evaluation

1. GGVD provides nucleotide diversity (Pi), heterozygosity (H_p), Tajima's D, composite likelihood ratio (CLR), integrated haplotype score (iHS), Weir and Cockerham’s Fst (F_ST), cross-population extended haplotype homozygosity (XP-EHH), and differences in nucleotide diversity (Pi ratio) for 16 goat groups. To facilitate the identification of true selective signatures, we set a cutoff corresponding to Z test P < 0.005 if the data conforms to the normal distribution or use top 1% signal value when the data does not satisfy the normal distribution.

IV. Introgression

1. We provides published introgression segments from ibex-like species to domestic goats. The introgressed haplotypes of all samples can be displayed in Gbrowse to help identify the most likely source of species and lineages.

V. liftOver chain file

1. We aligned different versions of the goat genome separately by minimap2, producing six pairwise alignment results.
2. Two utilities, maf-convert and axtChain, were used to produce liftOver chain files to facilitate users to coordinate conversion.

VI. Database implementation

1. High-quality SNPs, indels, selection scores, introgression regions, gene annotations, group information, p values, and threshold values, were processed with Perl scripts and stored in the MySQL database.
2. We use PHP Server Pages, HTML5 and JavaScript to implement search, data visualization and download.

Manual

I. Samples and population structure

Our database integrates 208 modern domestic goats, 24 bezoars, 82 ancient goats, and 46 ibex from published genetic works. The principal component analysis (PCA) and neighbor-joining (NJ) phylogenetic tree revealed a clear genetic structure as samples from the same geographical origin cluster together except African dairy goats, roughly consistent with our previous results. Given that dairy goats are mainly originated from Europe, it is reasonable for this group clustered with European goats to form one large branch. Combined with our previous classification, eight geographically distributed subgroups according to PCA and NJ tree can be roughly ascribed to: Bezoars, Africa, Africa (dairy), Europe, Australia, Southwest Asia, South Asia, and East Asia.

 

Fig 1. Population genetic structure and data processing pipeline of 360 goat individuals.

 

II. Variation search

The GGVD allows users to obtain information of SNPs and indels by searching for a specific variant rsid, a gene symbol or a genomic region. Users can filter SNPs and indels further by "Advanced Search", in which some parameters, such as minor allele frequency and consequence type, can be set; this option enables users to narrow down the items of interest in an efficient and intuitive manner. The results are presented in an interactive table and graph. Based on the returned results, users can obtain related details including variant position, alleles, minor allele frequency, variant effect, rs id and the allele frequency distribution pattern in 25 geographically distributed goat populations or eight subgroups according to genetic structure.

  SNPs or indels search

Fig 2. Screenshots of a SNP data search and the results for examples.

III. Signature search

For basic search, users can select a specific gene symbol or genomic region, one of the statistical methods (Pi, H_p, Tajima's D, CLR, iHS, F_ST, Pi ratio, XP-EHH), and a specific goat group to view the selection scores. The results are retrieved in a tabular format. When users click the "show" button on the table, selective signals are displayed in Manhattan plots, where the target region or gene is highlighted in red colour. For advanced search, users can select groups and methods by any combination to view the selection scores in line charts, which is usually used as a partial enlarged view of selective sweep. In our database, the selection scores are pre-processed by several algorithms, such as Z-transform, logarithm, and p values are calculated according to data distribution mode.

 

Fig 3. Screenshots of search for selective signature and representation of the P2RY1 gene.

IV. Introgression browse

In this section, putative introgressed haplotypes with a frequency higher than 0.1 in goats were kept. Users can browse introgression segments that facilitated goat domestication in the following ways, including viewing segment length, introgression frequency, similarity between ibex species and domestic goats, and gene annotation. Moreover, the introgressed haplotypes of all samples can be displayed in Gbrowse to help identify the most likely source of species and lineages.

 

Fig 4. Screenshots of introgression data search and representation of MUC6 gene.

V. GGVD tools

1. Local UCSC genome browser

Users can search with a gene symbol, or a transcript name, or a genomic region to view SNPs, indels, genomic signature, genotype patterns, and conserved elements in the global view. Currently, 90 tracks have been released for the goat ARS1 assembly. The "PDF/PS" item under the "View" menu of navigation bar was used to generate a high quality image in PostScript or PDF formats.

2. Alignment search tools (BLAT/BLAST)

We introduced two sequence alignment tools, webBlat and viroBLAST. The webBlat can be used to quickly search for homologous regions of a DNA or mRNA sequence, which can then be displayed in the browser. ViroBLAST can find regions of local similarity between sequences, which can be used to infer functional and evolutionary relationships between sequences.

3. Genome coordinate conversion tool (liftOver)

We also introduced a genome coordinate conversion Tool, liftOver. The liftOver tool is used to translate genomic coordinates from one assembly version into another and also retrieves putative orthologous regions in other species. Our database produces six liftOver chain files (ASM_vs_CHIR_1.0.minimap2.chain.gz, ASM_vs_CHIR_2.0.minimap2.chain.gz, CHIR_1.0_vs_ASM.minimap2.chain.gz, CHIR_1.0_vs_CHIR_2.0.minimap2.chain.gz, CHIR_2.0_vs_ASM.minimap2.chain.gz, and CHIR_2.0_vs_CHIR_1.0.minimap2.chain.gz) and provides an online lift for any two versions of the goat genome.

4. Genome selective signature scans tool (pcadapt)

GGVD also provide a web tool, pcadapt, for analyzing PCA and detecting selective signatures of user defined individuals and groups. The pcadapt-related R package is encapsulated in the backend interface and the results will be sent to the user's mailbox after the submitted task is completed.

Project organizers