Reduced-Representation Sequencing for Population Genetics
Overview
CD Genomics provides advanced Reduced-Representation Sequencing(RRS) to identify genetic variations, driving discoveries in conservation and precise medicine. Our services employ restriction enzymes to digest and target specific genomic regions, enabling cost-effective, high-depth analysis of both genetic and epigenetic variations. Utilizing state-of-the-art sequencing platforms and expert bioinformatics analysis, we deliver high resolution population genomic data tailored to your research needs, from disease studies to population genetics and evolutionary research.
Our RRS services include:
Restriction site Associated DNA Sequence-RAD
Double digest restrictionsite associated DNA
What is Reduced-Representation Sequencing
RRS utilizes restriction enzymes to recognize and cleave specific sites in genomic DNA, generating DNA fragments of varying sizes. These fragments are then sequenced using high-throughput platforms to yield extensive sequence data. This technique significantly reduces genome complexity, enabling cost-effective, high-depth representative analysis of the entire genome. It is suitable for high-throughput sample genotyping and operates independently of a reference genome. Owing to these advantages, RRS is widely applied in fields such as evolutionary biology and genomics. RRS encompasses various techniques such as RAD-seq, ddRAD, GBS, , and 2b-RAD, classified based on the enzymes used.
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Diagram of Four Major RRS Pipelines. (Andrew, et al., 2016)
Sequencing Platforms
NextSeq 500
Illumina NovaSeq
PacBio Sequel II
Workflow of RRS in Population Genetics
Our RRS service workflow includes sample collection, library preparation, high-throughput sequencing, quality control, and detailed variant analysis to uncover genetic variations and population insights. Customers are encouraged to ensure proper sample handling and share specific research goals for tailored analysis. For any questions about sample requirements, sequencing, or data interpretation, our team is always ready to assist.
Bioinformatics Content
| Basic Analysis | Advanced analysis |
| Raw Data Quality Control: Per-base sequence quality, GC content distribution and adapter contamination ratio. Sequence Alignment: Retain uniquely mapped reads and mark duplicates. Variant Calling: SNP identification. Data Format Conversion and Integration |
Population Genetic Structure: Principal Component Analysis(PCA) and ancestry component analysis. Phylogeny and Demographic History: Phylogenetic tree construction and demographic dynamics. Landscape Genomics |
Why CD Genomics
- Cutting-edge Sequencing Platforms: Compatible with NGS (Illumina) and third-gen (PacBio/Nanopore) platforms.
- High-throughput, High-quality Sequencing: Deliver premium-quality sequencing data faster, at lower cost, and with unmatched efficiency.
- One-Stop Service: End-to-end analysis from raw data QC, variant calling to population structure analysis, and provide expert data interpretation support.
- Integration of Comprehensive Genomics Analysis: Offer various omics technology services, including advanced and customized genomics analysis solutions.
Sample Requirements
| General Requirements | DNA Quality | Free from RNA, protein, or exogenous DNA contamination(verify by OD260/280≈1.8-2.0) | |
| Degradation-free genomic DNA (clear electrophoresis bands, no smearing) | |||
| Precise quantification (recommended: Qubit fluorometry; avoid Nanodrop) | |||
| Sample Type | Fresh and frozen tissues, blood, saliva, etc., especially those with high nucleic acid content(avoid long-term preservation and repeatedly frozen) | ||
| Sample Sizes Required | RAD-seq | GBS-seq | |
| Plant Tissue | ≥1 g | ≥0.5 g | |
| Animal Tissue | >0.5 g | ||
| Blood (Mammals) | ≥1 mL | ||
| Blood (Nucleated RBCs) | ≥0.5 mL | ||
| Cells | >5 × 10^6(Log-phase growth; adherent cells should be trypsinized) | ||
| Nucleic Acid | - Total DNA ≥ 300 ng - Concentration ≥ 10 ng/μL |
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Demo Results
Lineages Hierarchical Structure Results
( Stobie et al., Ecology and Evolution, 2017）
Polymorphism Rates of the Populations
( Davey et al., Nature Reviews Genetics, 2011）
Case Study
Population genetic diversity and structure of the endangered species Tetracentron sinense Oliver
(Tetracentraceae) with SNPs based on RAD sequencing
Journal:PLOS one
Published:2025
https://doi.org/10.1371/journal.pone.0324161
This study focuses on the conservation genetics of Tetracentron sinense. This ancient and endangered tree lives only in East Asia. It is valued for medicine, ecology, and commerce, but faces threats from shrinking habitats and declining populations. Despite its vulnerability, past studies lacked detailed genetic analysis across populations. Previous work mainly looked at ecology or used less detailed genetic markers. To fill this gap, the authors used RAD-seq technology. They gathered genome-wide SNP data from 164 trees across 25 wild populations in central and southern China. This helped them assess genetic diversity, population structure, and differences between groups. The goal was to guide effective conservation strategies. The results showed alarmingly low genetic diversity overall, but high genetic differentiation between groups. An analysis (AMOVA) found that 77% of genetic variation actually exists within populations. Population structure analysis consistently identified five distinct genetic groups. Historical isolation, complex landscape, and limited movement of genes created these groups. Importantly, the group in Clade V showed the fastest decay of genetic linkage (LD), suggesting it might be the original evolutionary source. The study urges immediate conservation action to reduce inbreeding problems in isolated populations.
- Reduced-representation sequencing - RADseq
- Bioinfomatics
Using genome-wide SNPs (RAD-seq) analysis, the authors found low genetic diversity and high differentiation in T. sinense. Additionally, they confirmed the presence of five conservation management units. The populations in the Qinling-Daba Mountains are considered the original center of T. sinense. This study represents the first exploration of genetic information through SNP analysis.
The five clades are represented in different colors. (Tian, et al. 2025)
DAPC was performed to characterize genetic structure among 164 T. sinense individuals. Principal components 1–3(PC1–PC3) accounted for 33.62% of cumulative variance. DAPC plots revealed distinct clustering of the five genetic groups identified in ADMIXTURE analysis, confirming their genetic divergence.
A Maximum-likelihood(ML) phylogenetic tree was reconstructed using high-quality unlinked SNPs with RAxML v7.3. The tree exhibited strong bootstrap support across most branches, validating its reliability. Five major clades were resolved.
DAPC and ML phylogenetic analysis of T. sinense samples. (Tian, et al. 2025)
FAQs
RAD-seq demonstrates high marker density, making it ideal for fine-scale genetic maps. However, its data consistency is lower due to biases from single-enzyme digestion. Increased sequencing depth (≥15X) can compensate for allelic dropout.
ddRAD leverages dual-enzyme fragmentation to standardize fragment sizes, effectively bypassing repetitive genomic regions and enhancing accuracy in complex genomes. This precision comes at a ~30% higher cost per sample than GBS.
GBS offers cost efficiency but suffers from uneven coverage, which is caused by PCR amplification bias. Integrating unique molecular identifiers(UMI) tags or boosting sequencing depth could mitigate it. Its optimal cost-effectiveness emerges in large-scale studies (>500 samples). Whereas smaller cohorts (<100 samples) incur higher relative costs due to fixed library processing overheads.
2b-RAD excels in degraded DNA resistance owing to its fixed short tags (33-36bp). It is particularly suitable for FFPE tissues, archaeological samples, or degraded environmental DNA. However, it is unsuitable for large-genome de novo assembly due to challenges in resolving repetitive regions with short reads.
Uneven data coverage can arise from several causes. Inefficient enzyme cleavage can lead to failed digestion. This can be addressed by adjusting enzyme dosage or performing digestion in the order dictated by salt/temperature requirements. PCR amplification bias can cause over- or under-representation of specific fragments. Attaching UMIs to original molecules prior to PCR amplification effectively counters this issue. Low coverage often occurs in GC-rich regions, repetitive sequences, or for minority variants. To mitigate this, sequencing depth should be increased to ensure coverage exceeds 20X per target site.
The sequencing depth depends on the research objectives and sample size. For SNP genotyping, we recommend a minimum effective sequencing depth of 10X. For large cohorts(more than 100 samples), recommended sequencing depth may be reduced to 5X with ensured SNP detection rate.
References
- Andrews, K. R., Good, J. M., et al. (2016). Harnessing the power of RADseq for ecological and evolutionary genomics. Nature Reviews Genetics, 17(2), 81–92. https://doi.org/10.1038/nrg.2015.28
- Stobie, C. S., Oosthuizen, C. J., et al. (2018). Exploring the phylogeography of a hexaploid freshwater fish by RAD sequencing. Ecology and Evolution, 8(4), 2326–2342. https://doi.org/10.1002/ece3.3821
- Davey, J. W., Hohenlohe, P. A., et al. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499–510. https://doi.org/10.1038/nrg3012
- Tian, Z.-Q., Jiang, C.-Y., et al. (2025). Population genetic diversity and structure of the endangered species Tetracentron sinense Oliver (Tetracentraceae) with SNPs based on RAD sequencing. PLOSONE, 20(5), e0324161. https://doi.org/10.1371/journal.pone.0324161
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