Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data

Fragoso, Christopher A; Heffelfinger, Christopher; Zhao, Hongyu; Dellaporta, Stephen L.

doi:10.1534/genetics.115.182071

Cited by 59 publications

(75 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 88%

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

Crysnanto

Wurmser

Pausch

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Given the results obtained with simulated and real data, we recommend the use of NOISYmputer to impute noisy or high-quality GBS or WGS data. LB-Impute or Tassel-FSFHap can also be used for imputation of high-quality data, with a preference for LB-Impute in case of very low-coverage (< 0.4 X), as discussed in (Fragoso et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…To test NOISYmputer and other methods' accuracy on real data, we used the high-quality Rice_GBS dataset. We compared map sizes to the map obtained with LB-Impute combined with BP-Impute and a custom R script that selects the most probable genotype, as extensive testing on this dataset showed that this method provided accurate imputation (Fragoso et al, 2016.…”

Section: Methods For Algorithm Comparisonmentioning

confidence: 99%

“…Very efficient methods have been recently developed to impute genotypic data derived from LC-NG assays in biparental populations. For instance, Tassel-FSFHap (Swarts et al, 2015), LB-Impute (Fragoso, Heffelfinger, Zhao, & Dellaporta, 2016) or Genotype-Corrector (Miao et al, 2018) can all address issues 1 and 2 accurately. Yet, we found in preliminary analyses that these methods can produce inaccurate results when the errors mentioned in issues 3 and 4 -thereafter called "noisy data" -are too frequent.…”

Section: Problemmentioning

confidence: 99%

See 1 more Smart Citation

NOISYmputer: genotype imputation in bi-parental populations for noisy low-coverage next-generation sequencing data

Lorieux

Gkanogiannis

Fragoso³

et al. 2019

Preprint

View full text Add to dashboard Cite

Motivation: Low-coverage next-generation sequencing (LC-NGS) methods can be used to genotype bi-parental populations. This approach allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints, and minimize mapping intervals for quantitative-trait locus analysis.The main issues with these genotyping methods are (1) poor performance at heterozygous loci, (2) a high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself.Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e. "noisy" data). Here, we present an algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomic regions, corrects erroneous data, and imputes missing data. We compare its performance with Tassel-FSFHap, LB-Impute, and Genotype-Corrector using simulated data and three real datasets: a rice single seed descent (SSD) population genotyped by genotyping by sequencing (GBS) by whole genome sequencing (WGS), and a sorghum SSD population genotyped by GBS.Availability: NOISYmputer, a Microsoft Excel-Visual Basic for Applications program that implements the algorithm, is available at mapdisto.free.fr. It runs in Apple macOS and Microsoft Windows operating systems. Supplementary files: Download link

show abstract

“…Constructing linkage maps using sequencing data is complicated by the presence of two types of missing data that can result when the sequencing depth is low. The first is a missing genotype resulting from no alleles being called, while the second consists of a heterozygous genotype being called as homozygous due to only one of the parental alleles being sequenced at a particular locus (Fragoso et al . 2016; Dodds et al .…”

mentioning

confidence: 99%

Accounting for Errors in Low Coverage High-Throughput Sequencing Data when Constructing Genetic Maps using Biparental Outcrossed Populations

Bilton

Schofield

Black

et al. 2018

Preprint

View full text Add to dashboard Cite

Next generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high density genetic linkage maps, which facilitate the development of non-model species’ genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (e.g., genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sib family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sib populations of diploid species, implemented in a package called GUSMap. Our model is based on an extension of the Lander-Green hidden Markov model that accounts for errors present in sequencing data. Results show that GUSMap was able to give accurate estimates of the recombination fractions and overall map distance, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model.

show abstract

Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data

Cited by 59 publications

References 37 publications

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

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

NOISYmputer: genotype imputation in bi-parental populations for noisy low-coverage next-generation sequencing data

Accounting for Errors in Low Coverage High-Throughput Sequencing Data when Constructing Genetic Maps using Biparental Outcrossed Populations

Contact Info

Product

Resources

About