Constructing a Draft Indian Cattle Pangenome Using Short-Read Sequencing

2024 · doi:10.1101/2024.09.02.610916
preprint OA: closed
📄 Open PDF Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text ⓘ

This study constructed an Indian cattle (desi cattle) pangenome by sequencing 68 genomes from seven breeds using short-read Illumina data, then developing the PanGA pipeline to identify non-reference novel sequences (NRNS) absent from the standard reference. The authors found 13,065 NRNS totaling about 41 Mbp that varied across the population, were largely exclusive to Indian desi cattle relative to the Chinese indicine pangenome, and included ~40% with ancestral origins within the Bos genus. They reported NRNS enrichment in genic regions and association with quantitative trait loci, especially for milk production, and showed that pangenome-based analysis improved read mapping accuracy and reduced spurious SNP calls compared with a single reference genome. The paper does not explicitly discuss limitations in the abstract, but it is limited to short-read–based discovery of NRNS and mapping/QTL associations within the included desi cattle samples. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Background Indian cattle known as desi cattle, renowned for their adaptability to harsh environments and diverse phenotypic traits, represent a valuable genetic resource. While reference genome assemblies have been instrumental in advancing cattle genomics, they often fail to capture the full spectrum of genetic variation present within diverse populations. To address this limitation, we aimed to construct a pangenome for desi cattle by identifying and characterizing Non-Reference Novel Sequences (NRNS). Findings We sequenced 68 desi cattle genomes representing seven distinct breeds, generating 48.35 billion short reads. A PanGenome Analysis (PanGA) pipeline was developed in Bash scripts to process these data to identify NRNS missing in the reference genome. A total of 13,065 NRNS with a cumulative length of ∼41 Mbp were identified that exhibited substantial variation across the population. These NRNS were found to be exclusive to Indian desi cattle, matching only 4.1% with the Chinese indicine pangenome. However, a significant proportion (∼40%) of NRNS displayed ancestral origins within the Bos genus. These sequences were enriched in genic regions, suggesting functional roles, and were associated with quantitative trait loci (QTLs), particularly for milk production. Compared to a single reference genome, the pangenome approach significantly enhanced read mapping accuracy, reduced spurious SNP calls, and facilitated the discovery of novel genetic variants. Conclusions This study has successfully established a within-species cattle pangenome specifically focused on desi cattle breeds from India. Our findings highlight the importance of pangenome based analyses for understanding the complex genetic architecture of desi cattle.
Full text 2,879 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Background Indian cattle known as desi cattle, renowned for their adaptability to harsh environments and diverse phenotypic traits, represent a valuable genetic resource. While reference genome assemblies have been instrumental in advancing cattle genomics, they often fail to capture the full spectrum of genetic variation present within diverse populations. To address this limitation, we aimed to construct a pangenome for desi cattle by identifying and characterizing Non-Reference Novel Sequences (NRNS). Findings We sequenced 68 desi cattle genomes representing seven distinct breeds, generating 48.35 billion short reads. A PanGenome Analysis (PanGA) pipeline was developed in Bash scripts to process these data to identify NRNS missing in the reference genome. A total of 13,065 NRNS with a cumulative length of ∼41 Mbp were identified that exhibited substantial variation across the population. These NRNS were found to be exclusive to Indian desi cattle, matching only 4.1% with the Chinese indicine pangenome. However, a significant proportion (∼40%) of NRNS displayed ancestral origins within the Bos genus. These sequences were enriched in genic regions, suggesting functional roles, and were associated with quantitative trait loci (QTLs), particularly for milk production. Compared to a single reference genome, the pangenome approach significantly enhanced read mapping accuracy, reduced spurious SNP calls, and facilitated the discovery of novel genetic variants.

Conclusions

This study has successfully established a within-species cattle pangenome specifically focused on desi cattle breeds from India. Our findings highlight the importance of pangenome based analyses for understanding the complex genetic architecture of desi cattle. Competing Interest Statement The authors have declared no competing interest. Data availability The short-read sequencing dataset generated from the Illumina platform and used in this study has been submitted to the Indian Biological Data Centre (IBDC). The accession numbers for these datasets are detailed in Supplementary Table S9. Similarly, the accession numbers for the RNA-seq data used in the study are provided in Supplementary Table S10. All the data can also be accessed from the NCBI. Abbreviations - BAM - Binary Alignment Map - bp - base pairs - CDS - Coding sequence - FDR - False Discovery Rate - GB - Gigabyte - Gb - Gigabase - GO - Gene Ontology - Kb - Kilobase - LINE - Long interspersed nuclear element - Mb - Megabase - NCBI - National Center for Biotechnology - NGS - Next Generation Sequencing - NRNS - Non-Reference Novel Sequences - PanGA - PanGenome Analysis - SAM - Sequence Alignment Map - SINE - Short interspersed nuclear element - SNP - Single Nucleotide polymorphism - SV - Structural variation - TE - Transposable Element - QTL - Quantitative trait loci - VCF - Variant Calling file

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00