Accurate allele frequencies from ultra-low coverage pool-seq samples in evolve-and-resequence experiments

Tilk, Susanne; Bergland, Alan O.; Goodman, Aaron J.; Schmidt, Paul S.; Petrov, Dmitri A.; Greenblum, Sharon

doi:10.1101/244004

Cited by 8 publications

(18 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regular coverage pool sequencing of crops with exceptionally large genomes is possible and can produce reliable output down to the gene allele frequency level, as based on WGrS of pool sizes of 300 genotypes. Using a regular sequencing coverage can successfully be compensated by haplotype-based analysis, as reported previously 36 . Pool sequencing approaches have been successfully applied to small genomes 37,38 , but relatively few studies have attempted to estimate genetic variation at the gene level 39,40 .…”

Section: Discussionmentioning

confidence: 82%

Organic farming - Deep genotyping reveals specific selection footprints in barley populations

Schneider

Barbosa

Ballvora

et al. 2021

Preprint

View full text Add to dashboard Cite

Organic farming has the potential to tackle the imminent task of sustainable food production, if the yields could be raised. Here, the benefits of additional exotic alleles, the necessity of increased genetic heterogeneity in organically farmed systems and the buffering capacities by the pronounced plasticity of root traits are demonstrated. Two barley populations, naturally adapted for more than two decades to organic and conventional farming systems, were compared by a novel strategy of whole genome resequencing of pooled samples. Substantial allele frequency deviations between the farming systems were uncovered (for various agronomically relevant chromosomal regions) by testing multiple generations. In contrast to the organic adapted population, an early equilibrium in the conventional population was observed, accompanied reduced genetic diversity. Differences between the populations were revealed in root morphology, developmental processes and abiotic stress responses. These findings indicate that wild genetic resources play a critical role in the development of organically adapted varieties and distinct variations in ecosystems demand different genetic compositions.

show abstract

Section: Discussionmentioning

confidence: 82%

Organic farming - Deep genotyping reveals specific selection footprints in barley populations

Schneider

Barbosa

Ballvora

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The proposed method in this study can be a useful extension to close the gap between big and small genomes. As highlighted by Tilk et al (2019), even 5x coverage could be sufficient. Generally, the sequencing coverage can be reduced, if high numbers of polymorphisms are detected.…”

Section: Discussionmentioning

confidence: 99%

“…Haplotype construction Tilk et al (2019) proposed a sliding window approach to generate contigs, which ultimately generate the boundaries of haplotypes. The allele frequency information observed for variants in the sliding window, are aggregated to a haplotype frequency.…”

Section: Genotyping Approachesmentioning

confidence: 99%

“…In contrast to a static coverage level, Rellstab et al (2013) proposed a minimum coverage adapted to the number of individuals in a pool and the ploidy level (21). Another approach tries to assess the true allele frequency on low coverage levels with only 5x coverage per locus (24). The third derives from the sequencing, especially by the amplification step.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High throughput crop genome genotyping by a combination of pool next generation sequencing and haplotype-based data processing

Schneider

Shrestha

Ballvora

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundThe identification of environmentally specific alleles and the observation of evolutional processes is a goal of conservation genomics. By generational changes of allele frequencies in populations, questions regarding effective population size, gene flow, drift, and selection can be addressed. The observation of such effects often is a trade-off of costs and resolution, when a decent sample of genotypes should be genotyped for many loci. Pool genotyping approaches can derive a high resolution and precision in allele frequency estimation, when high coverage sequencing is utilized. Still, pool high coverage pool sequencing of big genomes comes along with high costs.ResultsHere we present a reliable method to estimate a barley population’s allele frequency at low coverage sequencing. Three hundred genotypes were sampled from a barley backcross population to estimate the entire population’s allele frequency. The allele frequency estimation accuracy and yield were compared for three next generation sequencing methods. To reveal accurate allele frequency estimates on a low coverage sequencing level, a haplotyping approach was performed. Low coverage allele frequency of positional connected single polymorphisms were aggregated to a single haplotype allele frequency, resulting in two to 271 times higher depth and increased precision. We compared different haplotyping tactics, showing that gene and chip marker-based haplotypes perform on par or better than simple contig haplotype windows. The comparison of multiple pool samples and the referencing against an individual sequencing approach revealed whole genome pool resequencing having the highest correlation to individual genotyping (up to 0.97), while transcriptomics and genotyping by sequencing indicated higher error rates and lower correlations.ConclusionUsing the proposed method allows to identify the allele frequency of populations with high accuracy at low cost. This is particularly interesting for conservation genomics in species with big genomes, like barley or wheat. Whole genome low coverage resequencing at 10x coverage can deliver a highly accurate estimation of the allele frequency, when a loci-based haplotyping approach is applied. Using annotated haplotypes allows to capitalize from biological background and statistical robustness.

show abstract

“…However, whole‐genome resequencing of many individuals to coverages that allow us to control the sequencing error is still prohibited especially for species with medium‐size to large genomes. In the last years, Pool‐seq data is increasingly being used for population genomic studies since among other benefits, it bypasses the most expensive step in the sequencing process: library preparation for each individual in the sample (Healy & Burton, 2020; Jónás et al, 2016; Micheletti & Narum, 2018; Neethiraj et al, 2017; Schlötterer et al, 2014; Tilk et al, 2019). Consequently, at equal sequencing effort and cost, Pool‐seq allows us to sequence larger samples of individuals and hence, to decrease the variance in the estimates of population allele frequencies (Ferretti et al, 2013; Futschik & Schlötterer, 2010).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking the performance of Pool‐seq SNP callers using simulated and real sequencing data

Guirao-Rico

González

2021

Molecular Ecology Resources

View full text Add to dashboard Cite

Population genomics is a fast‐developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome‐wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool‐seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE‐pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large‐scale population studies but also in cases where the Pool‐seq strategy is the only option, such as in metagenomic or polyploid studies.

show abstract

Accurate allele frequencies from ultra-low coverage pool-seq samples in evolve-and-resequence experiments

Cited by 8 publications

References 44 publications

Organic farming - Deep genotyping reveals specific selection footprints in barley populations

Organic farming - Deep genotyping reveals specific selection footprints in barley populations

High throughput crop genome genotyping by a combination of pool next generation sequencing and haplotype-based data processing

Benchmarking the performance of Pool‐seq SNP callers using simulated and real sequencing data

Contact Info

Product

Resources

About