Analyses and comparison of accuracy of different genotype imputation methods

The power of genetic association analyses is often compromised by missing genotypic data which contributes to lack of significant findings, e.g., in in silico replication studies. One solution is to impute untyped SNPs from typed flanking markers, based on known linkage disequilibrium (LD) relationships. Several imputation methods are available and their usefulness in association studies has been demonstrated, but factors affecting their relative performance in accuracy have not been systematically investigated. Therefore, we investigated and compared the performance of five popular genotype imputation methods, MACH, IMPUTE, fastPHASE, PLINK and Beagle, to assess and compare the effects of factors that affect imputation accuracy rates (ARs). Our results showed that a stronger LD and a lower MAF for an untyped marker produced better ARs for all the five methods. We also observed that a greater number of haplotypes in the reference sample resulted in higher ARs for MACH, IMPUTE, PLINK and Beagle, but had little influence on the ARs for fastPHASE. In general, MACH and IMPUTE produced similar results and these two methods consistently outperformed fastPHASE, PLINK and Beagle. Our study is helpful in guiding application of imputation methods in association analyses when genotype data are missing.     

Genotype imputation in the domestic dog

Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; (2) evaluated several reference panel breed combinations to determine an optimal reference panel composition; and (3) compared the accuracy of two commonly used software algorithms (Beagle and IMPUTE2). Breed clustering was well preserved in the imputation process across eigenvalues representing 75 % of the variation in the imputed data. Using Beagle with a target panel from a single breed, genotype concordance was highest using a multi-breed reference panel (92.4 %) compared to a breed-specific reference panel (87.0 %) or a reference panel containing no breeds overlapping with the target panel (74.9 %). This finding was confirmed using target panels derived from two other breeds. Additionally, using the multi-breed reference panel, genotype concordance was slightly higher with IMPUTE2 (94.1 %) compared to Beagle; Pearson correlation coefficients were slightly higher for both software packages (0.946 for Beagle, 0.961 for IMPUTE2). Our findings demonstrate that genotype imputation from SNP array-derived data to whole genome-level genotypes is both feasible and accurate in the dog with appropriate breed overlap between the target and reference panels.     

Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip

Background

Imputation of genotypes from low-density to higher density chips is a cost-effective method to obtain high-density genotypes for many animals, based on genotypes of only a relatively small subset of animals (reference population) on the high-density chip. Several factors influence the accuracy of imputation and our objective was to investigate the effects of the size of the reference population used for imputation and of the imputation method used and its parameters. Imputation of genotypes was carried out from 50 000 (moderate-density) to 777 000 (high-density) SNPs (single nucleotide polymorphisms).

Methods

The effect of reference population size was studied in two datasets: one with 548 and one with 1289 Holstein animals, genotyped with the Illumina BovineHD chip (777 k SNPs). A third dataset included the 548 animals genotyped with the 777 k SNP chip and 2200 animals genotyped with the Illumina BovineSNP50 chip. In each dataset, 60 animals were chosen as validation animals, for which all high-density genotypes were masked, except for the Illumina BovineSNP50 markers. Imputation was studied in a subset of six chromosomes, using the imputation software programs Beagle and DAGPHASE.

Results

Imputation with DAGPHASE and Beagle resulted in 1.91% and 0.87% allelic imputation error rates in the dataset with 548 high-density genotypes, when scale and shift parameters were 2.0 and 0.1, and 1.0 and 0.0, respectively. When Beagle was used alone, the imputation error rate was 0.67%. If the information obtained by Beagle was subsequently used in DAGPHASE, imputation error rates were slightly higher (0.71%). When 2200 moderate-density genotypes were added and Beagle was used alone, imputation error rates were slightly lower (0.64%). The least imputation errors were obtained with Beagle in the reference set with 1289 high-density genotypes (0.41%).

Conclusions

For imputation of genotypes from the 50 k to the 777 k SNP chip, Beagle gave the lowest allelic imputation error rates. Imputation error rates decreased with increasing size of the reference population. For applications for which computing time is limiting, DAGPHASE using information from Beagle can be considered as an alternative, since it reduces computation time and increases imputation error rates only slightly.     

Allelic Diversity and Geographical Distribution of the Gene Encoding Plasmodium falciparum Merozoite Surface Protein-3 in Thailand

Merozoite surface proteins (MSPs) of malaria parasites play critical roles during the erythrocyte invasion and so are potential candidates for malaria vaccine development. However, because MSPs are often under strong immune selection, they can exhibit extensive genetic diversity. The gene encoding the merozoite surface protein-3 (MSP-3) of Plasmodium falciparum displays 2 allelic types, K1 and 3D7. In Thailand, the allelic frequency of the P. falciparum msp-3 gene was evaluated in a single P. falciparum population in Tak at the Thailand and Myanmar border. However, no study has yet looked at the extent of genetic diversity of the msp-3 gene in P. falciparum populations in other localities. Here, we genotyped the msp-3 alleles of 63 P. falciparum samples collected from 5 geographical populations along the borders of Thailand with 3 neighboring countries (Myanmar, Laos, and Cambodia). Our study indicated that the K1 and 3D7 alleles coexisted, but at different proportions in different Thai P. falciparum populations. K1 was more prevalent in populations at the Thailand-Myanmar and Thailand-Cambodia borders, whilst 3D7 was more prevalent at the Thailand-Laos border. Global analysis of the msp-3 allele frequencies revealed that proportions of K1 and 3D7 alleles of msp-3 also varied in different continents, suggesting the divergence of malaria parasite populations. In conclusion, the variation in the msp-3 allelic patterns of P. falciparum in Thailand provides fundamental knowledge for inferring the P. falciparum population structure and for the best design of msp-3 based malaria vaccines.     

Optimizing imputation of marker data from genotyping-by-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study

Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.     

Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies

Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.     

Genotype determination for polymorphisms in linkage disequilibrium

Background  

Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information.     

Genetic Analysis and Species Specific Amplification of the Artemisinin Resistance-Associated Kelch Propeller Domain in P. falciparum and P. vivax

Plasmodium falciparum resistance to artemisinin has emerged in the Greater Mekong Subregion and now poses a threat to malaria control and prevention. Recent work has identified mutations in the kelch propeller domain of the P. falciparum K13 gene to be associated artemisinin resistance as defined by delayed parasite clearance and ex vivo ring stage survival assays. Species specific primers for the two most prevalent human malaria species, P. falciparum and P. vivax, were designed and tested on multiple parasite isolates including human, rodent, and non- humans primate Plasmodium species. The new protocol described here using the species specific primers only amplified their respective species, P. falciparum and P. vivax, and did not cross react with any of the other human malaria Plasmodium species. We provide an improved species specific PCR and sequencing protocol that could be effectively used in areas where both P. falciparum and P. vivax are circulating. To design this improved protocol, the kelch gene was analyzed and compared among different species of Plasmodium. The kelch propeller domain was found to be highly conserved across the mammalian Plasmodium species.     

Emergence of artemisinin-resistant Plasmodium falciparum with kelch13 C580Y mutations on the island of New Guinea

The rapid and aggressive spread of artemisinin-resistant Plasmodium falciparum carrying the C580Y mutation in the kelch13 gene is a growing threat to malaria elimination in Southeast Asia, but there is no evidence of their spread to other regions. We conducted cross-sectional surveys in 2016 and 2017 at two clinics in Wewak, Papua New Guinea (PNG) where we identified three infections caused by C580Y mutants among 239 genotyped clinical samples. One of these mutants exhibited the highest survival rate (6.8%) among all parasites surveyed in ring-stage survival assays (RSA) for artemisinin. Analyses of kelch13 flanking regions, and comparisons of deep sequencing data from 389 clinical samples from PNG, Indonesian Papua and Western Cambodia, suggested an independent origin of the Wewak C580Y mutation, showing that the mutants possess several distinctive genetic features. Identity by descent (IBD) showed that multiple portions of the mutants’ genomes share a common origin with parasites found in Indonesian Papua, comprising several mutations within genes previously associated with drug resistance, such as mdr1, ferredoxin, atg18 and pnp. These findings suggest that a P. falciparum lineage circulating on the island of New Guinea has gradually acquired a complex ensemble of variants, including kelch13 C580Y, which have affected the parasites’ drug sensitivity. This worrying development reinforces the need for increased surveillance of the evolving parasite populations on the island, to contain the spread of resistance.     

Enhancing Blockade of Plasmodium falciparum Erythrocyte Invasion: Assessing Combinations of Antibodies against PfRH5 and Other Merozoite Antigens

No vaccine has yet proven effective against the blood-stages of Plasmodium falciparum, which cause the symptoms and severe manifestations of malaria. We recently found that PfRH5, a P. falciparum-specific protein expressed in merozoites, is efficiently targeted by broadly-neutralizing, vaccine-induced antibodies. Here we show that antibodies against PfRH5 efficiently inhibit the in vitro growth of short-term-adapted parasite isolates from Cambodia, and that the EC₅₀ values of antigen-specific antibodies against PfRH5 are lower than those against PfAMA1. Since antibody responses elicited by multiple antigens are speculated to improve the efficacy of blood-stage vaccines, we conducted detailed assessments of parasite growth inhibition by antibodies against PfRH5 in combination with antibodies against seven other merozoite antigens. We found that antibodies against PfRH5 act synergistically with antibodies against certain other merozoite antigens, most notably with antibodies against other erythrocyte-binding antigens such as PfRH4, to inhibit the growth of a homologous P. falciparum clone. A combination of antibodies against PfRH4 and basigin, the erythrocyte receptor for PfRH5, also potently inhibited parasite growth. This methodology provides the first quantitative evidence that polyclonal vaccine-induced antibodies can act synergistically against P. falciparum antigens and should help to guide the rational development of future multi-antigen vaccines.     

Marker imputation efficiency for genotyping-by-sequencing data in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis>) and alfalfa (<Emphasis Type="Italic">Medicago sativa</Emphasis>)

Genotyping-by-sequencing (GBS) is a rapid and cost-effective genome-wide genotyping technique applicable whether a reference genome is available or not. Due to the cost-coverage trade-off, however, GBS typically produces large amounts of missing marker genotypes, whose imputation becomes therefore both challenging and critical for later analyses. In this work, the performance of four general imputation methods (K-nearest neighbors, Random Forest, singular value decomposition, and mean value) and two genotype-specific methods (“Beagle” and FILLIN) was measured on GBS data from alfalfa (Medicago sativa L., autotetraploid, heterozygous, without reference genome) and rice (Oryza sativa L., diploid, 100 % homozygous, with reference genome). Alfalfa SNP were aligned on the genome of the closely related species Medicago truncatula L.. Benchmarks consisted in progressive data filtering for marker call rate (up to 70 %) and increasing proportions (up to 20 %) of known genotypes masked for imputation. The relative performance was measured as the total proportion of correctly imputed genotypes, globally and within each genotype class (two homozygotes in rice, two homozygotes and one heterozygote in alfalfa). We found that imputation accuracy was robust to increasing missing rates, and consistently higher in rice than in alfalfa. Accuracy was as high as 90–100 % for the major (most frequent) homozygous genotype, but dropped to 80–90 % (rice) and below 30 % (alfalfa) in the minor homozygous genotype. Beagle was the best performing method, both accuracy- and time-wise, in rice. In alfalfa, KNNI and RFI gave the highest accuracies, but KNNI was much faster.     

Contrasting Population Structures of the Genes Encoding Ten Leading Vaccine-Candidate Antigens of the Human Malaria Parasite,Plasmodium falciparum

The extensive diversity of Plasmodium falciparum antigens is a major obstacle to a broadly effective malaria vaccine but population genetics has rarely been used to guide vaccine design. We have completed a meta-population genetic analysis of the genes encoding ten leading P. falciparum vaccine antigens, including the pre-erythrocytic antigens csp, trap, lsa1 and glurp; the merozoite antigens eba175, ama1, msp''s 1, 3 and 4, and the gametocyte antigen pfs48/45. A total of 4553 antigen sequences were assembled from published data and we estimated the range and distribution of diversity worldwide using traditional population genetics, Bayesian clustering and network analysis. Although a large number of distinct haplotypes were identified for each antigen, they were organized into a limited number of discrete subgroups. While the non-merozoite antigens showed geographically variable levels of diversity and geographic restriction of specific subgroups, the merozoite antigens had high levels of diversity globally, and a worldwide distribution of each subgroup. This shows that the diversity of the non-merozoite antigens is organized by physical or other location-specific barriers to gene flow and that of merozoite antigens by features intrinsic to all populations, one important possibility being the immune response of the human host. We also show that current malaria vaccine formulations are based upon low prevalence haplotypes from a single subgroup and thus may represent only a small proportion of the global parasite population. This study demonstrates significant contrasts in the population structure of P. falciparum vaccine candidates that are consistent with the merozoite antigens being under stronger balancing selection than non-merozoite antigens and suggesting that unique approaches to vaccine design will be required. The results of this study also provide a realistic framework for the diversity of these antigens to be incorporated into the design of next-generation malaria vaccines.     

Molecular prediction for atherogenic risks across different cell types of leukocytes

Background

We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.

Methods

The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n?=?1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.

Results

The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.

Conclusions

The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.     

The relationship between reported fever and Plasmodium falciparum infection in African children

Background

The genetic diversity of Plasmodium falciparum has been extensively studied in various parts of the world. However, limited data are available from Pakistan. This study aimed to establish molecular characterization of P. falciparum field isolates in Pakistan measured with two highly polymorphic genetic markers, i.e. the merozoite surface protein 1 (msp-1)and 2 (msp-2).

Methods

Between October 2005 and October 2007, 244 blood samples from patients with symptomatic blood-slide confirmed P. falciparum mono-infections attending the Aga Khan University Hospital, Karachi, or its collection units located in Sindh and Baluchistan provinces, Pakistan were collected. The genetic diversity of P. falciparum was analysed by length polymorphism following gel electrophoresis of DNA products from nested polymerase chain reactions (PCR) targeting block 2 of msp-1 and block 3 of msp-2, including their respective allelic families KI, MAD 20, RO33, and FC27, 3D7/IC.

Results

A total of 238/244 (98%) patients had a positive PCR outcome in at least one genetic marker; the remaining six were excluded from analysis. A majority of patients had monoclonal infections. Only 56/231 (24%) and 51/236 (22%) carried multiple P. falciparum genotypes in msp-1 and msp-2, respectively. The estimated total number of genotypes was 25 msp-1 (12 KI; 8 MAD20; 5 RO33) and 33 msp-2 (14 FC27; 19 3D7/IC).

Conclusions

This is the first report on molecular characterization of P. falciparum field isolates in Pakistan with regards to multiplicity of infection. The genetic diversity and allelic distribution found in this study is similar to previous reports from India and Southeast Asian countries with low malaria endemicity.     

A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions.     

Imputation of sequence level genotypes in the Franches-Montagnes horse breed

Background

A cost-effective strategy to increase the density of available markers within a population is to sequence a small proportion of the population and impute whole-genome sequence data for the remaining population. Increased densities of typed markers are advantageous for genome-wide association studies (GWAS) and genomic predictions.

Methods

We obtained genotypes for 54 602 SNPs (single nucleotide polymorphisms) in 1077 Franches-Montagnes (FM) horses and Illumina paired-end whole-genome sequencing data for 30 FM horses and 14 Warmblood horses. After variant calling, the sequence-derived SNP genotypes (~13 million SNPs) were used for genotype imputation with the software programs Beagle, Impute2 and FImpute.

Results

The mean imputation accuracy of FM horses using Impute2 was 92.0%. Imputation accuracy using Beagle and FImpute was 74.3% and 77.2%, respectively. In addition, for Impute2 we determined the imputation accuracy of all individual horses in the validation population, which ranged from 85.7% to 99.8%. The subsequent inclusion of Warmblood sequence data further increased the correlation between true and imputed genotypes for most horses, especially for horses with a high level of admixture. The final imputation accuracy of the horses ranged from 91.2% to 99.5%.

Conclusions

Using Impute2, the imputation accuracy was higher than 91% for all horses in the validation population, which indicates that direct imputation of 50k SNP-chip data to sequence level genotypes is feasible in the FM population. The individual imputation accuracy depended mainly on the applied software and the level of admixture.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0063-7) contains supplementary material, which is available to authorized users.     

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions

Key message

Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy.

Abstract

Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.

     

Pre-clinical assessment of novel multivalent MSP3 malaria vaccine constructs

Background

MSP3 has been shown to induce protection against malaria in African children. The characterization of a family of Plasmodium falciparum merozoite surface protein 3 (MSP3) antigens sharing a similar structural organization, simultaneously expressed on the merozoite surface and targeted by a cross-reactive network of protective antibodies, is intriguing and offers new perspectives for the development of subunit vaccines against malaria.

Methods

Eight recombinant polyproteins containing carefully selected regions of this family covalently linked in different combinations were all efficiently produced in Escherichia coli. The polyproteins consisted of one monovalent, one bivalent, one trivalent, two tetravalents, one hexavalent construct, and two tetravalents incorporating coiled-coil repeats regions from LSA3 and p27 vaccine candidates.

Results

All eight polyproteins induced a strong and homogeneous antibody response in mice of three distinct genotypes, with a dominance of cytophilic IgG subclasses, lasting up to six months after the last immunization. Vaccine-induced antibodies exerted a strong monocyte-mediated in vitro inhibition of P. falciparum growth. Naturally acquired antibodies from individuals living in an endemic area of Senegal recognized the polyproteins with a reactivity mainly constituted of cytophilic IgG subclasses.

Conclusions

Combination of genetically conserved and antigenically related MSP3 proteins provides promising subunit vaccine constructs, with improved features as compared to the first generation construct employed in clinical trials (MSP3-LSP). These multivalent MSP3 vaccine constructs expand the epitope display of MSP3 family proteins, and lead to the efficient induction of a wider range of antibody subclasses, even in genetically different mice. These findings are promising for future immunization of genetically diverse human populations.     

High-density marker imputation accuracy in sixteen French cattle breeds

Background

Genotyping with the medium-density Bovine SNP50 BeadChip^® (50K) is now standard in cattle. The high-density BovineHD BeadChip^®, which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.

Methods

Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.

Results

Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.

Conclusion

In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.