Dataset Information

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost.

ABSTRACT:

Background

Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations.

Methods

Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated.

Results

Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP).

Conclusions

Among the selection candidates, 95.5% and 93.5% of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and $20.58 per candidate.

SUBMITTER: Huang Y

PROVIDER: S-EPMC3436735 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Publications

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost.

Huang Yijian Y Hickey John M JM Cleveland Matthew A MA Maltecca Christian C

Genetics, selection, evolution : GSE 20120731

<h4>Background</h4>Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to exp ...[more]

PMID: 22849718

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Project description:BACKGROUND:During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~?850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis. RESULTS:We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K. CONCLUSIONS:Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

Project description:Background In cattle, genome-wide association studies (GWAS) have largely focused on European or Asian breeds, using genotyping arrays that were primarily designed for European cattle. Because there is growing interest in performing GWAS in African breeds, we have assessed the performance of 23 commercial bovine genotyping arrays for capturing the diversity across African breeds and performing imputation. We used 409 whole-genome sequences (WGS) spanning global cattle breeds, and a real cohort of 2481 individuals (including African breeds) that were genotyped with the Illumina high-density (HD) array and the GeneSeek bovine 50 k array. Results We found that commercially available arrays were not effective in capturing variants that segregate among African indicine animals. Only 6% of these variants in high linkage disequilibrium (LD) (r2 > 0.8) were on the best performing arrays, which contrasts with the 17% and 25% in African and European taurine cattle, respectively. However, imputation from available HD arrays can successfully capture most variants (accuracies up to 0.93), mainly when using a global, not continent-specific, reference panel, which partially reflects the unusually high levels of admixture on the continent. When considering functional variants, the GGPF250 array performed best for tagging WGS variants and imputation. Finally, we show that imputation from low-density arrays can perform almost as well as HD arrays, if a two-stage imputation approach is adopted, i.e. first imputing to HD and then to WGS, which can potentially reduce the costs of GWAS. Conclusions Our results show that the choice of an array should be based on a balance between the objective of the study and the breed/population considered, with the HD and BOS1 arrays being the best choice for both taurine and indicine breeds when performing GWAS, and the GGPF250 being preferable for fine-mapping studies. Moreover, our results suggest that there is no advantage to using the indicus-specific arrays for indicus breeds, regardless of the objective. Finally, we show that using a reference panel that better represents global bovine diversity improves imputation accuracy, particularly for non-European taurine populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00751-5.

Project description:IntroductionIn the United States, tens of thousands of inspections of tobacco retailers are conducted each year. Various sampling choices can reduce travel costs, emphasize enforcement in areas with greater noncompliance, and allow for comparability between states and over time. We sought to develop a model sampling strategy for state tobacco retailer inspections.MethodsUsing a 2014 list of 10,161 North Carolina tobacco retailers, we compared results from simple random sampling; stratified, clustered at the ZIP code sampling; and, stratified, clustered at the census tract sampling. We conducted a simulation of repeated sampling and compared approaches for their comparative level of precision, coverage, and retailer dispersion.ResultsWhile maintaining an adequate design effect and statistical precision appropriate for a public health enforcement program, both stratified, clustered ZIP- and tract-based approaches were feasible. Both ZIP and tract strategies yielded improvements over simple random sampling, with relative improvements, respectively, of average distance between retailers (reduced 5.0% and 1.9%), percent Black residents in sampled neighborhoods (increased 17.2% and 32.6%), percent Hispanic residents in sampled neighborhoods (reduced 2.2% and increased 18.3%), percentage of sampled retailers located near schools (increased 61.3% and 37.5%), and poverty rate in sampled neighborhoods (increased 14.0% and 38.2%).ConclusionsStates can make retailer inspections more efficient and targeted with stratified, clustered sampling. Use of statistically appropriate sampling strategies like these should be considered by states, researchers, and the Food and Drug Administration to improve program impact and allow for comparisons over time and across states.ImplicationsThe authors present a model tobacco retailer sampling strategy for promoting compliance and reducing costs that could be used by US states and the Food and Drug Administration (FDA). The design is feasible to implement in North Carolina. Use of the sampling design would help document the impact of FDA's compliance and enforcement program, save money, and emphasize inspections in areas where they are needed most. FDA should consider requiring probability-based sampling in their inspections contracts with states and private contractors.

Dataset Information

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost.

Background

Methods

Results

Conclusions

Publications

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost.

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