Genotyping Efforts Research Articles

Population-specific putative causal variants shape quantitative traits.

Human genetic variants are associated with many traits through largely unknown mechanisms. Here, combining approximately 260,000 Japanese study participants, a Japanese-specific genotype reference panel and statistical fine-mapping, we identified 4,423 significant loci across 63 quantitative traits, among which 601 were new, and 9,406 putatively causal variants. New associations included Japanese-specific coding, splicing and noncoding variants, exemplified by a damaging missense variant rs730881101 in TNNT2 associated with lower heart function and increased risk for heart failure (P = 1.4 × 10-15 and odds ratio = 4.5, 95% confidence interval = 3.1-6.5). Putative causal noncoding variants were supported by state-of-art in silico functional assays and had comparable effect sizes to coding variants. A plausible example of new mechanisms of causal variants is an enrichment of causal variants in 3' untranslated regions (UTRs), including the Japanese-specific rs13306436 in IL6 associated with pro-inflammatory traits and protection against tuberculosis. We experimentally showed that transcripts with rs13306436 are resistant to mRNA degradation by regnase-1, an RNA-binding protein. Our study provides a list of fine-mapped causal variants to be tested for functionality and underscores the importance of sequencing, genotyping and association efforts in diverse populations.

SnpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics.

Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems. snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations. snpAIMeR is available on CRAN (https://CRAN.R-project.org/package=snpAIMeR).

Abstract 921: The value of cancer genomic data sharing

Abstract Our understanding of and ability to combat cancer relies on the generation, analysis, and sharing of data. The Epidemiology and Genomics Research Program in the National Cancer Institute’s Division of Cancer Control and Population Sciences (DCCPS) has supported generation of large genomic data sets and advocated for and/or required broad sharing of this data. One of DCCPS’s scientific priorities is focused on data strategies namely, “the use, enhancement, expansion, and accessibility of new and existing DCCPS-supported data resources to accelerate cancer control research” (https://cancercontrol.cancer.gov/overview-highlights/2022/future_directions.html).EGRP funded the Genetic Associations and Mechanisms in Oncology (GAME-ON) Initiative, a multidisciplinary collaboration of researchers focused on understanding the genetic architecture, biology, and clinical and epidemiologic utility of common cancer risk variants. This program focused on 5 common cancer types - breast, colorectal, lung, ovarian, and prostate - and required research that incorporated multiple research domains and sharing of any data generated. A major accomplishment of this program was development of the OncoArray genotyping chip, which used information from genetic epidemiology studies, biological/functional analyses, and clinical and epidemiologic insights to create a comprehensive, high-density genotyping array for pan-cancer studies that has led to the discovery of hundreds of new cancer predisposition variants. This chip was used in a massive, coordinated genotyping effort that culminated in the generation of 7 datasets comprising genotyping data for nearly 500,000 individuals. All the data were deposited in dbGaP between 2014 and 2021 and are available (via controlled access) to any investigators. To understand the secondary use of these data, we determined the number of times the data had been downloaded, who downloaded the data, and types of research for which it was used. We also identified publications using OncoArray data and characterized the publications by research type, cancer type, and other details. Through this analysis, we explore how well the GAME-ON initiative and the OncoArray projects promote the DCCPS scientific priority of optimizing secondary research by making the data findable, accessible, interoperable, and reusable (FAIR). Citation Format: Stefanie Nelson, Charlisse Caga Anan, Elizabeth Gillanders. The value of cancer genomic data sharing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 921.

A Field Survey of Grapevine Germplasm Susceptible to Eutypa Dieback

Eutypa dieback of grapevine is a trunk disease that impacts vineyard productivity worldwide. Grape germplasm is typically evaluated for resistance to Eutypa dieback through controlled inoculations in the greenhouse, although the high level of replication required of this approach (40 plants per genotype) can limit the total number of genotypes evaluated. An alternative approach is to evaluate naturally infected genotypes in the field. We rated the incidence and severity of vines with the diagnostic leaf symptoms of Eutypa dieback and the incidence of mortality among such vines of 973 Vitis vinifera accessions (planted in duplicate) at the US Department of Agriculture, National Clonal Germplasm Repository in Davis, CA, USA, which is maintained as a living collection for grape research. Across 3 years and spanning a total of 5 years (2011, 2013, and 2015), 120 accessions had leaf symptoms in one or more years (“susceptible accessions”). Courbu blanc [Davis Vitis identification tag (DVIT) 2313], Frankenthal blanc (DVIT 2115), and Pinot gris (DVIT 0907) were the only accessions with leaf symptoms each year. Accessions with the most severe leaf symptoms (a rating of 5 points) were Chasselas Napoleon (DVIT 0375) and Queen of the Vineyard (DVIT 0496). We identified susceptible accessions—namely, those related to ‘Chasselas’ and ‘Muscat’—with a shared genetic background, based on a previous single nucleotide polymorphism genotyping effort of the collection. Especially for grapevine, a long-lived perennial that is meant to produce a crop for decades, knowledge of susceptible accessions and their pedigrees can help inform breeding programs and studies on the host response to infection.

Efficient species identification for Pacific salmon genetic monitoring programs.

Genetic monitoring of Pacific salmon in the Columbia River basin provides crucial information to fisheries managers that is otherwise challenging to obtain using traditional methods. Monitoring programs such as genetic stock identification (GSI) and parentage-based tagging (PBT) involve genotyping tens of thousands of individuals annually. Although rare, these large sample collections inevitably include misidentified species, which exhibit low genotyping success on species-specific Genotyping-in-Thousands by sequencing (GT-seq) panels. For laboratories involved in large-scale genotyping efforts, diagnosing non-target species and reassigning them to the appropriate monitoring program can be costly and time-consuming. To address this problem, we identified 19 primer pairs that exhibit consistent cross-species amplification among salmonids and contain 51 species informative variants. These genetic markers reliably discriminate among 11 salmonid species and two subspecies of Cutthroat Trout and have been included in species-specific GT-seq panels for Chinook Salmon, Coho Salmon, Sockeye Salmon, and Rainbow Trout commonly used for Pacific salmon genetic monitoring. The majority of species-informative amplicons (16) were newly identified from the four existing GT-seq panels, thus demonstrating a low-cost approach to species identification when using targeted sequencing methods. A species-calling script was developed that is tailored for routine GT-seq genotyping pipelines and automates the identification of non-target species. Following extensive testing with empirical and simulated data, we demonstrated that the genetic markers and accompanying script accurately identified species and are robust to missing genotypic data and low-frequency, shared polymorphisms among species. Finally, we used these tools to identify Coho Salmon incidentally caught in the Columbia River Chinook Salmon sport fishery and used PBT to determine their hatchery of origin. These molecular and computing resources provide a valuable tool for Pacific salmon conservation in the Columbia River basin and demonstrate a cost-effective approach to species identification for genetic monitoring programs.

Sampling strategies for genotyping common bean (Phaseolus vulgaris L.) Genebank accessions with DArTseq: a comparison of single plants, multiple plants, and DNA pools.

Genotyping large-scale gene bank collections requires an appropriate sampling strategy to represent the diversity within and between accessions. A panel of 44 common bean (Phaseolus vulgaris L.) landraces from the Alliance Bioversity and The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) gene bank was genotyped with DArTseq using three sampling strategies: a single plant per accession, 25 individual plants per accession jointly analyzed after genotyping (in silico-pool), and by pooling tissue from 25 individual plants per accession (seq-pool). Sampling strategies were compared to assess the technical aspects of the samples, the marker information content, and the genetic composition of the panel. The seq-pool strategy resulted in more consistent DNA libraries for quality and call rate, although with fewer polymorphic markers (6,142 single-nucleotide polymorphisms) than the in silico-pool (14,074) or the single plant sets (6,555). Estimates of allele frequencies by seq-pool and in silico-pool genotyping were consistent, but the results suggest that the difference between pools depends on population heterogeneity. Principal coordinate analysis, hierarchical clustering, and the estimation of admixture coefficients derived from a single plant, in silico-pool, and seq-pool successfully identified the well-known structure of Andean and Mesoamerican gene pools of P. vulgaris across all datasets. In conclusion, seq-pool proved to be a viable approach for characterizing common bean germplasm compared to genotyping individual plants separately by balancing genotyping effort and costs. This study provides insights and serves as a valuable guide for gene bank researchers embarking on genotyping initiatives to characterize their collections. It aids curators in effectively managing the collections and facilitates marker-trait association studies, enabling the identification of candidate markers for key traits.

CandiHap: a haplotype analysis toolkit for natural variation study.

The online version contains supplementary material available at 10.1007/s11032-023-01366-4.

Genetic identification and reiterated captures suggest that the Astyanax mexicanus El Pachón cavefish population is closed and declining.

The sizes of Astyanax mexicanus blind cavefish populations of North-East Mexico are demographic parameters of great importance for investigating a variety of ecological, evolutionary, and conservation issues. However, few estimates have been obtained. For these mobile animals living in an environment difficult to explore as a whole, methods based on capture-mark-recapture are appropriate, but their feasibility and interpretation of results depend on several assumptions that must be carefully examined. Here, we provide evidence that minimally invasive genetic identification from captures at different time intervals (three days and three years) can give insights into cavefish population size dynamics as well as other important demographic parameters of interest. We also provide tools to calibrate sampling and genotyping efforts necessary to reach a given level of precision. Our results suggest that the El Pachón cave population is currently very small, of an order of magnitude of a few hundreds of individuals, and is distributed in a relatively isolated area. The probable decline in population size in the El Pachón cave since the last census in 1971 raises serious conservation issues.

Innovative Advances in Plant Genotyping.

Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.

Molecular identification and genotyping of Toxoplasma gondii isolated from sheep and cattle in northern Iran.

Toxoplasmosis, a foodborne disease, in human occurs commonly after the ingestion of tissue cysts via the raw and/or undercooked meat of different infected intermediate hosts such as sheep and cattle. The current study aimed to detect the genetic structure of Toxoplasma gondii isolated from various organs of sheep and cattle in the north of Iran. Conventional PCR was carried out by B1 and REP-529 genes of T. gondii. Nested and RFLP-PCR were performed for all positive samples using SAG2 and GRA6 genetic markers. Amplicons from second round of nested-PCR were sequenced and analyzed with NCBI database. Among of 179 examined samples, 38(21.20%) were positive. The highest of positive cases were found in kidney (28.60%). PCR-RFLP of SAG2 and GRA6 genes demonstrated the alleles of clonal type III in the all of isolates. Sequence analysis of the amplicons revealed the alleles of clonal type III and atypical isolates (Tg-67, Tg-100 and Tg-106). Phylogenetic analyses showed separate clade for the atypical isolates from others in the present study and the reference strains clades. In conclusion, the genetic characterization of T. gondii isolates from sheep and cattle showed high genetic diversity compared with standard type I, II and III genotypes. These results support the hypothesis of the existence of polymorphic and overlapping strains within livestock in Iran. It also suggested the necessity of increased genotyping and sampling efforts to accurately estimate T. gondii intra specific genetic diversity.

Evaluating sources of bias in pedigree-based estimates of breeding population size.

Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS ) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS , is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS . We applied results of the simulation analysis to evaluate S for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that NS is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.

Metrics for optimum allocation of resources on the composition and characterization of crop collections: The CIMMYT wheat collection as a proof of concept

Crop genebank collections are important resources for preserving genetic diversity to face the worldwide demand for food and coping with crop diseases and climate change. However, genebanks tend to accumulate materials without systematic collection growth. Thus, tools for optimizing collections are expected to help improvement of genebanks quality. Furthermore, the genotyping efforts of genebanks would benefit from tools that can help to sample the accessions. A set of parameters to aid the optimization of genebanks are defined, in which Relative Balance is central. In this study, the foundation of our mathematical approach was Kullback-Leibler divergence, providing formulas with consistent properties. Two examples were used as proof of concept. The first one was the comparison between actual and putative optimal numbers of accessions in the Triticum set of the CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo) Wheat Germplasm Bank, with 135,236 entries classified into ten groups. The second one was based on a set containing Triticum plus eight related genera, with 159,741 accessions classified into 217 end-groups, with the goal of illustrating the use of the analytical tools to optimize the ongoing genotyping process. The first example shows a scenario with a well-balanced allocation of accessions. The second example illustrates the optimized choice of end-groups to add 10,000 accessions to the genotyping process. The proof of concept showed the consistency and usefulness of the proposed methods for the improvement of composition in collections and their characterization.

Genotyping-By-Sequencing diversity analysis of international Vanilla collections uncovers hidden diversity and enables plant improvement

Genomics-based diversity analysis of natural vanilla populations is important in order to guide conservation efforts and genetic improvement through plant breeding. Vanilla is a cultivated, undomesticated spice that originated in Mesoamerica prior to spreading globally through vegetative cuttings. Vanilla extract from the commercial species, mainly V. planifolia and V. × tahitensis, is used around the world as an ingredient in foods, beverages, cosmetics, and pharmaceuticals. The global reliance on descendants of a few foundational clones in commercial production has resulted in an industry at heightened risk of catastrophic failure due to extremely narrow genetic diversity. Conversely, national and institutional collections including those near the center of cultivation contain previously undiscovered diversity that could bolster the genetic improvement of vanilla and guide conservation efforts. Towards this goal, an international vanilla genotyping effort generated and analyzed 431,204 single nucleotide polymorphisms among 412 accessions and 27 species from eight collections. Phylogenetic and STRUCTURE analysis sorted vanilla by species and identified hybrid accessions. Principal Component Analysis and the Fixation Index (FST) were used to refine relationships among accessions and showed differentiation among species. Analysis of the commercial species split V. planifolia into three types with all V. × tahitensis accessions being most similar to V. planifolia type 2. Finally, an in-depth analysis of V. × tahitensis identified seven V. planifolia and six V. odorata accessions as most similar to the estimated parental genotypes providing additional data in support of the current hybrid theory. The prevalence of probable V. × tahitensis parental accessions from Belize suggests that V. × tahitensis could have originated from this area and highlights the need for vanilla conservation throughout Central and South America. The genetic groupings among accessions, particularly for V. planifolia, can now be used to focus breeding efforts on fewer accessions that capture the greatest diversity.

Recommendations for accurate genotyping of SARS-CoV-2 using amplicon-based sequencing of clinical samples

ObjectivesGenotyping of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been instrumental in monitoring viral evolution and transmission during the pandemic. The quality of the sequence data obtained from these genotyping efforts depends on several factors, including the quantity/integrity of the input material, the technology, and laboratory-specific implementation. The current lack of guidelines for SARS-CoV-2 genotyping leads to inclusion of error-containing genome sequences in genomic epidemiology studies. We aimed to establish clear and broadly applicable recommendations for reliable virus genotyping. MethodsWe established and used a sequencing data analysis workflow that reliably identifies and removes technical artefacts; such artefacts can result in miscalls when using alternative pipelines to process clinical samples and synthetic viral genomes with an amplicon-based genotyping approach. We evaluated the impact of experimental factors, including viral load and sequencing depth, on correct sequence determination. ResultsWe found that at least 1000 viral genomes are necessary to confidently detect variants in the SARS-CoV-2 genome at frequencies of ≥10%. The broad applicability of our recommendations was validated in over 200 clinical samples from six independent laboratories. The genotypes we determined for clinical isolates with sufficient quality cluster by sampling location and period. Our analysis also supports the rise in frequencies of 20A.EU1 and 20A.EU2, two recently reported European strains whose dissemination was facilitated by travel during the summer of 2020. ConclusionsWe present much-needed recommendations for the reliable determination of SARS-CoV-2 genome sequences and demonstrate their broad applicability in a large cohort of clinical samples.

Adapting to supply-and-demand emerging trends for antigen-negative red blood cell units.

A global downtrend in blood usage has been observed by many countries, while the demand for antigen-negative red blood cell (RBC) units used in antigen-matched transfusions keeps increasing. The declining number of units collected exposes blood providers to a rapidly evolving supply challenge. This study was conducted retrospectively with use of internal data analysis to weigh Québec's situation regarding global and antigen-negative RBC demand, to measure the effects of community-directed recruitment and blood drives, and to evaluate the benefits of mass-scale RBC genotyping. Our findings confirm a global RBC usage downtrend of over 20% total in the past 10 years with a steady antigen-negative usage and highlight the most requested negative antigen combinations. Our data also show our +39.5% progress regarding the number of Black donors recruited for antigen matching of patients with sickle cell disease in the past 3 years, as well as a constantly growing number of just-in-time blood collection for complex orders. Finally, our data summarize the efficiency of our mass-scale RBC genotyping efforts. Altogether, this study confirms the demand trends for regular and antigen-negative RBC units in Québec and the efficient effects of our recruitment and typing strategies.

Prediction accuracy of single-step BLUP for growth and wood quality traits in the lodgepole pine breeding program in British Columbia

Single-step best linear unbiased prediction (HBLUP) is a method used to predict breeding values by combining pairwise relatedness information derived from a pedigree with the realized relationships estimated from DNA markers. It is an ideal approach for the lodgepole pine (Pinus contorta Dougl. ex. Loud.) breeding program which has an extensive progeny testing program but a small proportion of trees that are genotyped. However, it is unclear what level of genotyping is required to affect prediction accuracy and genetic parameters, the performance across test sites and test cycles of different ages, and the ability to accurately rank trees within half-sib and full-sib families. Lodgepole pine trees were sampled from four progeny test sites in British Columbia, Canada. A SNP array was used to genotype 1569 trees which resulted in 19,584 high-quality SNPs. The prediction accuracy of HBLUP was compared to (1) an uncorrected relationship matrix (ABLUP) and (2) BLUP using a realized relationship matrix based on SNP markers (GBLUP) using various cross-validation scenarios for height growth at age 10 and 5 wood quality traits. Combining average and realized pairwise relationship information through the H-matrix resulted in heritability and Type B genetic correlation estimates that were generally a compromise between estimates for ABLUP (0% genotyping) and GBLUP (100% genotyping). The highest heritability was for average wood density (0.57 for ABLUP; 0.51 for HBLUP; 0.47 for GBLUP) and the lowest was for height (0.24 for ABLUP; 0.27 for HBLUP; 0.25 for GBLUP). GBLUP always had the lowest Type B genetic correlations (except for earlywood density) of the three models (0.46 to 1.0) assessed. The prediction accuracy for HBLUP increased slightly for genotyped trees (0.77 to 0.80), but not for non-genotyped trees as genotyping effort increased. Furthermore, prediction accuracy was high when predicting between environments (0.46 to 0.85) and test cycles (0.33 to 0.76) when connected through pedigree, and prediction was more accurate when using older first-cycle tests to predict breeding values for younger second-cycle tests for all traits, except microfibril angle. Rank correlations for trees within half-sib and full-sib families when predicting values across test cycles (the training population is phenotyped and the validation population is genotyped) were very low using HBLUP (0.08) compared to GBLUP (0.38) but increased to 0.25 when 40% of the trees in the training population were genotyped (HBLUP40). HBLUP should be regarded as an effective way to combine average and realized relationship information in a breeding program for more precise estimates of genetic parameters and breeding values and can be used for predicting and ranking trees within families without phenotypic data when genotyped trees from the same families are included in the training population (genotyped and phenotyped).

Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications.

An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.

The Colorado Twin Registry: 2019 Update.

The Colorado Twin Registry (CTR) is a population-based registry formed from birth and school records including twins born between 1968 and the present. Two previous reports on the CTR [Rhea et al., (2006). Twin Research and Human Genetics, 9, 941-949; Rhea et al., (2013).Twin Research and Human Genetics, 16, 351-357] covered developments in the CTR through 2012. This report briefly summarizes previously presented material on ascertainment and recruitment and the relationships between samples and studies, discusses developments since 2012 for four previously described twin samples, describes two new samples and their complementary studies and expands on two subjects briefly mentioned in the last report: a history of genotyping efforts involving CTR samples, and a survey of collaborations and consortia in which CTR twins have been included. The CTR remains an active resource for both ongoing, longitudinal research and the recruitment of new twin samples for newly identified research opportunities.

PIMA: A population informative multiplex for the Americas

We describe an ancestry-informative autosomal SNP multiplex designed to be a small-scale, flexible panel that can complement uniparental markers in assessing the American variability (i.e. pre-Colombian) found in contemporary indigenous American populations. This study centered on choosing SNPs with the specific characteristics of: 1) extreme allele frequency differences between indigenous Americans and the African, European and East Asian population groups that contribute to present-day population variation in the Americas; 2) high informativeness-for-assignment In values; and 3) well-spaced genomic distribution and chromosomal separation from existing small-scale forensic ancestry marker sets. The resulting capillary electrophoresis SNaPshot single base extension test was named: PIMA (Population Informative Multiplex for the Americas), comprising 26 autosomal SNPs, a single X-chromosome SNP plus the amelogenin sex marker adapted for SNaPshot. PIMA complements the established 34plex forensic ancestry panel to provide a powerful and simple tool for the analysis of American populations, including those with admixed histories, commonly encountered in America. Comparing the results obtained with the combined marker panels of PIMA and 34plex to SNP data from a much larger ancestry panel allowed us to gauge their relative efficiency. PIMA+34plex gives equivalent power to the 314-SNP ‘LACE’ genomic ancestry control panel, while requiring a much smaller genotyping effort. The ancestry profiles and genetic structure of 22 populations spread across the American continent were estimated using PIMA+34plex data, and those estimates were contrasted with information provided by uniparental markers (mtDNA and Y-chromosome loci) for a small set of admixed individuals from Venezuela. Our results indicate that an American genetic component is efficiently detected in contemporary American populations using a small set of ancestry informative SNPs, and these co-ancestry estimates are consistent with the known history and demography of the Americas. The small scale and high population differentiation power of PIMA, particularly when combined with 34plex, provides a practical and powerful tool for genetic studies of American populations as well as forensic DNA analyses.

Economic optimization of full-sib test group size and genotyping effort in a breeding program for Atlantic salmon

BackgroundBreeding companies may want to maximize the rate of genetic gain from their breeding program within a limited budget. In salmon breeding programs, full-sibs of selection candidates are subjected to performance tests for traits that cannot be recorded on selection candidates. While marginal gains in the aggregate genotype from phenotyping and genotyping more full-sibs per candidate decrease, costs increase linearly, which suggests that there is an optimum in the allocation of the budget among these activities. Here, we studied how allocation of the fixed budget to numbers of phenotyped and genotyped test individuals in performance tests can be optimized.MethodsGain in the aggregate genotype was a function of the numbers of full-sibs of selection candidates that were (1) phenotyped in a challenge test for sea lice resistance (2) phenotyped in a slaughter test (3) genotyped in the challenge test, and (4) genotyped in the slaughter test. Each of these activities was subject to budget constraints. Using a grid search, we optimized allocation of the budget among activities to maximize gain in the aggregate genotype. We performed sensitivity analyses on the maximum gain in the aggregate genotype and on the relative allocation of the budget among activities at the optimum.ResultsMaximum gain in the aggregate genotype was €386/ton per generation. The response surface for gain in the aggregate genotype was rather flat around the optimum, but it curved strongly near the extremes. Maximum gain was sensitive to the size of the budget and the relative emphasis on breeding goal traits, but less sensitive to the accuracy of genomic prediction and costs of phenotyping and genotyping. The relative allocation of budget among activities at the optimum was sensitive to costs of phenotyping and genotyping and the relative emphasis on breeding goal traits, but was less sensitive to the accuracy of genomic prediction and the size of the budget.ConclusionsThere is an optimum allocation of budget to the numbers of full-sibs of selection candidates that are phenotyped and genotyped in performance tests that maximizes gain in the aggregate genotype. Although potential gains from optimizing group sizes and genotyping effort may be small, they come at no extra cost.