Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data

Bilton, Timothy P.; McEwan, John C.; Clarke, Shannon; Bräuning, Rüdiger; Stijn, Tracey C. van; Rowe, Suzanne; Dodds, K. G.

doi:10.1534/genetics.118.300831

Cited by 27 publications

(31 citation statements)

References 66 publications

(83 reference statements)

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“…Irrespective of the method evaluated, we observed heterozygous under-calling in animals that have been sequenced at low coverage, i.e., heterozygous variants were erroneously genotyped as homozygous due to an insufficient number of sequencing reads supporting the heterozygous genotype [10,[45][46][47]. In agreement with previous studies [2,5], Beagle imputation improved genotype concordance and reduced heterozygous under-calling particularly in cattle that had been sequenced at low coverage.…”

Section: Discussionsupporting

confidence: 88%

Accurate sequence variant genotyping in cattle using variation-aware genome graphs

Crysnanto

Wurmser

Pausch

2018

Preprint

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Background:The genotyping of sequence variants typically involves as a first step the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of sequence variation within a species, reference allele bias may occur at highly polymorphic or diverged regions of the genome. Graph-based methods facilitate to compare sequencing reads to a variation-aware genome graph that incorporates a collection of non-redundant DNA sequences that segregate within a species.We compared accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely used methods, i.e., GATK and SAMtools, that rely on linear reference genomes using whole-genomes sequencing data of 49 Original Braunvieh cattle. Results:We discovered 21,140,196, 20,262,913 and 20,668,459 polymorphic sites using GATK, Graphtyper, and SAMtools, respectively. Comparisons between sequence variant and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the lowest number of mendelian inconsistencies for both SNPs and indels in nine sire-son pairs with sequence data. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all tools evaluated particularly for animals that have been sequenced at low coverage. Following imputation, the concordance between sequence-and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24 % for GATK, Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved the genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but it required less than GATK.Conclusions: Sequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants which is not possible with the current implementations of state-of-the-art methods that rely on linear reference genomes.

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Section: Discussionsupporting

confidence: 88%

Accurate sequence variant genotyping in cattle using variation-aware genome graphs

Crysnanto

Wurmser

Pausch

2018

Preprint

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“…A drawback of the method is the numerous sources of genotyping errors (Mastretta-Yanes et al, 2015) and missing data (information missing for certain individuals for certain markers). One case of genotyping errors is dropped alleles, that is, one allele is not typed making a heterozygous individual appearing homozygous (Bilton et al, 2018). Missing data and random allelic dropouts can bias LD estimates (Akey, Zhang, Xiong, Doris, & Jin, 2001;Bilton et al, 2018) and subsequently bias LD based N e estimates and increase their variance (Nunziata & Weisrock, 2018;Russell & Fewster, 2009).…”

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confidence: 99%

“…One case of genotyping errors is dropped alleles, that is, one allele is not typed making a heterozygous individual appearing homozygous (Bilton et al, 2018). Missing data and random allelic dropouts can bias LD estimates (Akey, Zhang, Xiong, Doris, & Jin, 2001;Bilton et al, 2018) and subsequently bias LD based N e estimates and increase their variance (Nunziata & Weisrock, 2018;Russell & Fewster, 2009). The degree of bias in LD estimates depends on allele frequency (Akey et al, 2001).…”

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confidence: 99%

Estimating effective population size using RADseq: Effects of SNP selection and sample size

Marandel

Charrier

Lamy

et al. 2020

Ecology and Evolution

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Effective population size (Ne) is a key parameter of population genetics. However, Ne remains challenging to estimate for natural populations as several factors are likely to bias estimates. These factors include sampling design, sequencing method, and data filtering. One issue inherent to the restriction site‐associated DNA sequencing (RADseq) protocol is missing data and SNP selection criteria (e.g., minimum minor allele frequency, number of SNPs). To evaluate the potential impact of SNP selection criteria on Ne estimates (Linkage Disequilibrium method) we used RADseq data for a nonmodel species, the thornback ray. In this data set, the inbreeding coefficient FIS was positively correlated with the amount of missing data, implying data were missing nonrandomly. The precision of Neestimates decreased with the number of SNPs. Mean Ne estimates (averaged across 50 random data sets with2000 SNPs) ranged between 237 and 1784. Increasing the percentage of missing data from 25% to 50% increased Ne estimates between 82% and 120%, while increasing the minor allele frequency (MAF) threshold from 0.01 to 0.1 decreased estimates between 71% and 75%. Considering these effects is important when interpreting RADseq data‐derived estimates of effective population size in empirical studies.

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“…Modelling the sequencing process, analogous to that used by Dodds et al (2015) for relatedness estimation, Bilton et al (2018b) for linkage analysis and Bilton et al (2018a) for linkage disequilibrium estimation, allows depth-dependent errors. Recently, Whalen et al (2019) have adopted this approach to develop likelihood-based methods for relationship classification (including parentage).…”

Section: Discussionmentioning

confidence: 99%

“…The use of either of these models would allow tighter EMM thresholds and may lead to more accurate parentage assignments. These models could also be applied in other GBS analyses, such as the estimation of inbreeding (Dodds et al 2015), linkage (Bilton et al 2018b), linkage disequilibrium (Bilton et al 2018a), calculating genotype likelihoods for downstream analyses (Korneliussen et al 2014) or predicting gender (Bilton et al 2019). More work is required to evaluate whether these differences are important, whether the alternate models are significantly better than the random sampling model or whether there are other models (for example, one which allows sequencing error) which are more realistic and provide a better fit.…”

Section: Discussionmentioning

confidence: 99%

Exclusion and genomic relatedness methods for assignment of parentage using genotyping-by-sequencing data

Dodds

McEwan

Bräuning

et al. 2019

Preprint

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Genotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parentoffspring trios can be further checked for consistency by comparing the offspring's estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analysed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

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Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data

Abstract: High-throughput sequencing methods provide a cost-effective approach for genotyping and are commonly used in population genetics studies. A drawback of these methods, however, is that sequencing and genotyping errors can arise...

Cited by 27 publications

References 66 publications

Accurate sequence variant genotyping in cattle using variation-aware genome graphs

Accurate sequence variant genotyping in cattle using variation-aware genome graphs

Estimating effective population size using RADseq: Effects of SNP selection and sample size

Exclusion and genomic relatedness methods for assignment of parentage using genotyping-by-sequencing data

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