Whole Genome Re-sequencing for Population Genetics

Overview

CD Genomics offers cutting-edge Whole Genome Re-sequencing (WGR) services to uncover comprehensive genetic variation across entire genomes. Unlike targeted approaches, WGR captures both coding and non-coding regions, making it ideal for investigating population structure, genetic diversity, evolutionary patterns, and natural selection. Leveraging advanced sequencing platforms and expert bioinformatics analysis, we provide high-quality, cost-effective data solutions tailored to your population genetics, evolutionary biology, and comparative genomics research.

What is Whole Genome Re-sequencing

Whole Genome Re-sequencing involves sequencing the entire genome, including both coding and non-coding regions, providing a complete view of genetic variation across individuals and populations. Unlike Whole Exome Sequencing, which targets only exonic regions, WGR enables the detection of all types of genomic variants—single nucleotide polymorphisms (SNPs), insertions/deletions (InDels), structural variants, and more. This comprehensive approach is essential for population genetics studies, as it captures neutral and adaptive variations that shape population structure, evolutionary history, and genetic diversity. With the power of high-throughput sequencing, WGR supports in-depth analysis of natural selection, gene flow, demographic events, and local adaptation, making it an indispensable tool in modern genomic research.

Sequencing Platforms

NextSeq 500

Illumina NovaSeq

PacBio Sequel II

Workflow of WGR in Population Genetics

Our WGR service follows a streamlined workflow that includes sample collection, library preparation, high-throughput sequencing, rigorous quality control, and comprehensive variant analysis. This process enables the identification of genome-wide genetic variations critical for population genetics studies, such as SNPs, InDels, and structural variants. To ensure optimal results, we recommend that clients follow proper sample preservation protocols and communicate specific research objectives in advance, allowing us to tailor the analysis accordingly. For questions regarding sample requirements, sequencing strategies, or data interpretation, our expert team is here to provide full support throughout your project.

Diagram of a Whole Genome Re-sequencing pipeline. (Shengyong Xu, et al. 2019)

Data Analysis

Workflow of a Whole Genome Re-sequencing analysis (Zegeye, et al. 2018)

Why CD Genomics

Cutting-edge Sequencing Platforms: Utilize advanced high-throughput sequencing platforms and rigorously optimized laboratory protocols to generate accurate, reproducible whole-genome data across diverse populations and species.
High-throughput, High-quality Sequencing: Deliver comprehensive genome-wide datasets with exceptional accuracy, fast turnaround times, and competitive pricing—ideal for large-scale population genetics and evolutionary studies.
Standardized Analysis Workflow: Supported by a professional bioinformatics team, we offer robust, scalable pipelines for variant detection (SNPs, InDels, SVs), population structure analysis, selective sweep detection, demographic inference, and more.
Extensive Experience in Exome Sequencing: Our seasoned genomics experts provide end-to-end support, from experimental design to data interpretation, helping you uncover key patterns in genetic diversity, selection, gene flow, and population differentiation.
Customized Exome Sequencing Solutions: We adapt sequencing strategies to your specific research needs—whether you're investigating evolutionary history, adaptation, phylogeography, genome-wide association studies (GWAS), or conservation genomics.

Demo Results

Whole genome resequencing data for three rockfish species of Sebastes(Xu S,2019)

Case Study

Integration of Whole-Genome Resequencing and Transcriptome Sequencing Reveals Candidate Genes in High Glossiness of Eggshell

Journal: Animals

Published:2024

https://doi.org/10.3390/ani14081141

Eggshell gloss is an important characteristic for the manifestation of eggshell appearance. However, the reason for differences in eggshell glossiness is still unclear. The aim of this study is to perform a preliminary investigation into the formation mechanism of eggshell gloss and to identify potential genes through high-throughput sequencing. HTR1F, ZNF536, NEDD8, NGF and CALM1 were identified as potential candidate genes that may affect eggshell gloss, which provide a reference for the study of eggshell gloss and lay a foundation for improving egg glossiness in layer breeding.

Whole-Genome Resequencing
Bioinfomatics

Whole Genome Resequencing was performed on uterine tissues at the predicted timepoint (2 h before egg laying) to reveal gene expression patterns in HG and LG chickens. Figure 3A shows 99 upregulated DEGs (differentially expressed genes) and 51 downregulated DEGs. To determine the reliability of the RNA-seq results, we randomly selected 10 DEGs for qPCR analysis. We found that the qPCR results were consistent with the RNA-seq results, which indicated the reliability of the RNA-seq results (Figure.3B). To understand the biochemical functions of the DEGs, the 150 DEGs (99 upregulated and 51 downregulated) were used to perform GO and KEGG enrichment analyses. GO terms were classified into biological process (BP), cellular component (CC) and molecular function (MF). In total, 479 GO terms were significantly enriched, and the top 15 terms are shown in Figure 3C (p < 0.05). The DEGs were significantly enriched in six KEGG pathways (p < 0.05), including the calcium signalling pathway and neuroactive ligand–receptor interactions(Fig. 3D).The figures report the Manhattan plots of the single-variant analysis with q.emmax . (Lescai, et al. 2017)

Manhattan plots of the single-variant analysis Figure 3(A) The volcano plot maps of all DEGs between HG and LG uteruses. Red dots represent significantly upregulated genes and blue dots represent significantly downregulated genes. (B) Ten DEGs were validated by qPCR. Symbols "*" and "**" indicate a significant difference at p < 0.05 and p < 0.01, respectively. (C) GO enrichment analysis of DEGs. (D) KEGG pathway enrichment analysis of enriched DEGs. Abbreviations: DEGs, differentially expressed genes; HG, high gloss; LG, low gloss; GO, Gene Ontology. ( Xiang Song,2024)

FAQs

What is Whole Genome Re-sequencing Coverage Efficiency?

Whole Genome Re-sequencing (WGR) coverage efficiency refers to the proportion of the entire genome that is sequenced at a sufficient depth to enable accurate variant detection. Unlike exome sequencing, which targets only the coding regions, WGR provides uniform and comprehensive coverage across both coding and non-coding regions. This is crucial in population genetics, where detecting regulatory variants, structural changes, and genome-wide polymorphisms is essential for understanding genetic diversity and evolutionary dynamics. High coverage efficiency ensures that most genomic loci are sequenced with minimal gaps, maximizing data completeness and analytical power.

How Efficient is Whole Genome Re-sequencing in Variant Detection?

WGR is highly effective for identifying a wide range of genetic variants, including SNPs, insertions/deletions (InDels), copy number variations (CNVs), and structural variants (SVs). With a typical average depth of 10X to 30X, WGR can capture over 95% of common variants across the genome. In population genetics studies, even medium-depth WGR provides sufficient resolution to analyze population structure, infer demographic history, detect signals of selection, and perform genome-wide association studies (GWAS). The uniformity of WGR eliminates capture bias, enabling unbiased discovery of both coding and non-coding variants.

What is the Recommended Sequencing Depth for Whole Genome Re-sequencing in Population Genetics?

The optimal sequencing depth depends on the scale and objectives of the study:

10X–15X: Sufficient for large-scale population studies where the goal is to identify common variants and analyse population structure or phylogenetics.
20X–30X: Recommended for more detailed variant discovery, including rare variants and structural variations, with improved accuracy in genotype calling and haplotype phasing.
30X or higher: Suitable for high-resolution studies such as selective sweep analysis, local adaptation, or when working with highly complex genomes.

Higher sequencing depth enhances sensitivity and specificity, particularly for low-frequency alleles, while also improving confidence in variant calling across repetitive or GC-rich regions.

References

Zegeye, W. A., Zhang, Y., Cao, L., & Cheng, S. (2018). Whole Genome Resequencing from Bulked Populations as a Rapid QTL and Gene Identification Method in Rice. International Journal of Molecular Sciences, 19(12), 4000. https://doi.org/10.3390/ijms19124000
Xu, S., Zhao, L., Xiao, S., & Gao, T. (2019). Whole genome resequencing data for three rockfish species of Sebastes. Scientific data, 6(1), 97. https://doi.org/10.1038/s41597-019-0100-z
Song, X., Li, S., He, S., Zheng, H., Li, R., Liu, L., Geng, T., Zhao, M., & Gong, D. (2024). Integration of Whole-Genome Resequencing and Transcriptome Sequencing Reveals Candidate Genes in High Glossiness of Eggshell. Animals, 14(8), 1141. https://doi.org/10.3390/ani14081141

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.