A beginner’s guide to low‐coverage whole genome sequencing for population genomics

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency and genetic diversity estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

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A beginner's guide to low-coverage whole genome sequencing for population genomics

10.22541/au.160689616.68843086/v4 ◽

2021 ◽

Author(s):

Runyang Nicolas Lou ◽

Arne Jacobs ◽

Aryn Wilder ◽

Nina Overgaard Therkildsen

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Population Genomics ◽

Cost Effective ◽

Whole Genome ◽

Model Species ◽

Population Genomic ◽

Genomic Studies ◽

Lower Depth ◽

Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency and genetic diversity estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

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A beginner's guide to low-coverage whole genome sequencing for population genomics

10.22541/au.160689616.68843086/v1 ◽

2020 ◽

Author(s):

Runyang Nicolas Lou ◽

Arne Jacobs ◽

Aryn Wilder ◽

Nina Overgaard Therkildsen

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Population Genomics ◽

Frequency Estimation ◽

Cost Effective ◽

Whole Genome ◽

Model Species ◽

Population Genomic ◽

Lower Depth ◽

Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate that the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference compared to sequencing fewer samples each at higher depth. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based analysis. With this overview, we hope to make lcWGS more approachable and stimulate broader adoption.

Download Full-text

A beginner's guide to low-coverage whole genome sequencing for population genomics

10.22541/au.160689616.68843086/v2 ◽

2020 ◽

Author(s):

Runyang Nicolas Lou ◽

Arne Jacobs ◽

Aryn Wilder ◽

Nina Overgaard Therkildsen

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Population Genomics ◽

Frequency Estimation ◽

Cost Effective ◽

Whole Genome ◽

Model Species ◽

Population Genomic ◽

Lower Depth ◽

Low Coverage

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and non-model species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data and how the distribution of sequencing effort between the number of samples analyzed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate that the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference compared to sequencing fewer samples each at higher depth. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in non-model species, and discuss current limitations and future perspectives for lcWGS-based analysis. With this overview, we hope to make lcWGS more approachable and stimulate broader adoption.

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353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

Journal of Animal Science ◽

10.1093/jas/skaa278.149 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 81-82

Author(s):

Joaquim Casellas ◽

Melani Martín de Hijas-Villalba ◽

Marta Vázquez-Gómez ◽

Samir Id Lahoucine

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Error Rate ◽

Allele Frequencies ◽

Paternity Testing ◽

Sequencing Error ◽

Whole Genome ◽

Genomic Evaluation ◽

Sequencing Error Rate ◽

Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

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