Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows

Roberts, Adam P.; McMillan, Leonard; Wang, Wei; Parker, Joel S.; Rusyn, Ivan; Threadgill, David W.

doi:10.1093/bioinformatics/btm220

Cited by 80 publications

(66 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Genotyping was done by GBS (Elshire et al, 2011) using GBS pipeline 3.0 in the TASSEL software package (Bradbury et al, 2007;Glaubitz et al, 2014). Missing genotype calls were imputed using the NPUTE version 4.0 software (Roberts et al, 2007). SNPs with minor allele frequency .…”

Section: Biparental Mapping In Sorghum Rilsmentioning

confidence: 99%

Duplicate and Conquer: Multiple Homologs ofPHOSPHORUS-STARVATION TOLERANCE1Enhance Phosphorus Acquisition and Sorghum Performance on Low-Phosphorus Soils

et al. 2014

View full text Add to dashboard Cite

Low soil phosphorus (P) availability is a major constraint for crop production in tropical regions. The rice (Oryza sativa) protein kinase, PHOSPHORUS-STARVATION TOLERANCE1 (OsPSTOL1), was previously shown to enhance P acquisition and grain yield in rice under P deficiency. We investigated the role of homologs of OsPSTOL1 in sorghum (Sorghum bicolor) performance under low P. Association mapping was undertaken in two sorghum association panels phenotyped for P uptake, root system morphology and architecture in hydroponics and grain yield and biomass accumulation under low-P conditions, in Brazil and/or in Mali. Root length and root surface area were positively correlated with grain yield under low P in the soil, emphasizing the importance of P acquisition efficiency in sorghum adaptation to low-P availability. SbPSTOL1 alleles reducing root diameter were associated with enhanced P uptake under low P in hydroponics, whereas Sb03g006765 and Sb03g0031680 alleles increasing root surface area also increased grain yield in a low-P soil. SbPSTOL1 genes colocalized with quantitative trait loci for traits underlying root morphology and dry weight accumulation under low P via linkage mapping. Consistent allelic effects for enhanced sorghum performance under low P between association panels, including enhanced grain yield under low P in the soil in Brazil, point toward a relatively stable role for Sb03g006765 across genetic backgrounds and environmental conditions. This study indicates that multiple SbPSTOL1 genes have a more general role in the root system, not only enhancing root morphology traits but also changing root system architecture, which leads to grain yield gain under low-P availability in the soil.

show abstract

Section: Biparental Mapping In Sorghum Rilsmentioning

confidence: 99%

Duplicate and Conquer: Multiple Homologs ofPHOSPHORUS-STARVATION TOLERANCE1Enhance Phosphorus Acquisition and Sorghum Performance on Low-Phosphorus Soils

et al. 2014

View full text Add to dashboard Cite

show abstract

“…In the data set without LD threshold, the mean accuracies are 95. 9 Table 1 also indicate that the FFNN method has relative stable performance at missing rates from 1 to 10% but requires more computation rounds for a data set with a higher missing rate to reach the best imputation accuracy.…”

Section: Resultsmentioning

confidence: 95%

“…Knowing that most genetic variations such as SNPs are moderately dependent on each other, we can utilize the predictive model to illustrate the dependencies to impute the missing genotypic data. 8,9 The goal of imputing missing SNP genotypes focuses on accurately predicting individual missing values. Because of the amount of correlations between SNPs, especially high-density SNPs, it is feasible to accurately predict missing SNP genotypes.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the amount of correlations between SNPs, especially high-density SNPs, it is feasible to accurately predict missing SNP genotypes. 9 Feed-forward neural networks (FFNNs) have received considerable attention due to their successful use in a wide variety of statistical applications, including regression and classification problems. A general description of the use of FFNNs can be found in Bishop.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks

Sun

Kardia

2008

Eur J Hum Genet

View full text Add to dashboard Cite

With advances in high-throughput single-nucleotide polymorphism (SNP) genotyping, the amount of genotype data available for genetic studies is steadily increasing, and with it comes new abilities to study multigene interactions as well as to develop higher dimensional genetic models that more closely represent the polygenic nature of common disease risk. The combined impact of even small amounts of missing data on a multi-SNP analysis may be considerable. In this study, we present a neural network method for imputing missing SNP genotype data. We compared its imputation accuracy with fastPHASE and an expectation-maximization algorithm implemented in HelixTree. In a simulation data set of 1000 SNPs and 1000 subjects, 1, 5 and 10% of genotypes were randomly masked. Four levels of linkage disequilibrium (LD), LD R 2 o0.2, R 2 o0.5, R 2 o0.8 and no LD threshold, were examined to evaluate the impact of LD on imputation accuracy. All three methods are capable of imputing most missing genotypes accurately (accuracy 486%). The neural network method accurately predicted 92.0-95.9% of the missing genotypes. In a real data set comparison with 419 subjects and 126 SNPs from chromosome 2, the neural network method achieves the highest imputation accuracies 483.1% with missing rate from 1 to 5%. Using 90 HapMap subjects with 1962 SNPs, fastPHASE had the highest accuracy (B97%) while the other two methods had 495% accuracy. These results indicate that the neural network model is an accurate and convenient tool, requiring minimal parameter tuning for SNP data recovery, and provides a valuable alternative to usual complete-case analysis.

show abstract

“…GBS significantly reduces the cost of GWA genotyping with the tradeoff of a higher degree of missing SNPs compared to whole genome sequencing. Therefore, we have integrated both the GBS analysis workflow [12] and a missing SNP imputation tool, called NPUTE [13], to support the genotype analysis for GWA.…”

Section: Introduction the Iplant Collaborative (Iplant) Is A United Smentioning

confidence: 99%

A GWAS platform built on iPlant cyber-infrastructure

Wang

Ware

Lushbough

et al. 2014

Preprint

View full text Add to dashboard Cite

Abstract-We demonstrated a flexible Genome-Wide Association Study (GWAS) platform built upon the iPlant Collaborative Cyber-infrastructure. The platform supports big data management, sharing, and large scale study of both genotype and phenotype data on clusters. End users can add their own analysis tools, and create customized analysis workflows through the graphical user interfaces in both iPlant Discovery Environment and BioExtract server.

show abstract

Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows

Abstract: A free open source software package, NPUTE, is available at http://compgen.unc.edu/software, for non-commercial uses.

Cited by 80 publications

References 16 publications

Duplicate and Conquer: Multiple Homologs ofPHOSPHORUS-STARVATION TOLERANCE1Enhance Phosphorus Acquisition and Sorghum Performance on Low-Phosphorus Soils

Duplicate and Conquer: Multiple Homologs ofPHOSPHORUS-STARVATION TOLERANCE1Enhance Phosphorus Acquisition and Sorghum Performance on Low-Phosphorus Soils

Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks

A GWAS platform built on iPlant cyber-infrastructure

Contact Info

Product

Resources

About