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We estimate a hedonic pricing model to determine producers’ value for bull expected progeny differences (EPDs), genomic-enhanced EPDs, and phenotypic traits. Birth weight EPD, ribeye area EPD, sale weight, age, frame score, and other factors had a statistically significant impact on bull prices. GE-EPDs were not associated with a change in the bull sales prices expect for weaned calf value and birth weight EPDs. Including weaned calf value and GE-EPDs in a bull hedonic pricing model provides a unique contribution. The results from this work will inform educational programming for bull purchasers on using new economic selection indices and GE-EPDs.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in the area of genome to phenome (G2P) research is the focus of this paper. Previously (Tuggle et al. 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with US federal government agencies, including USDA, expand public–private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the “Thinking Big: Visualizing the Future of AG2PI” two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture’s National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome-to-phenome (AG2P) work and envision the future of the AG2P field. To tackle some of these big challenges, we designed this workshop to include diverse voices with the intention of hearing the different perspectives important to solving these challenges. The topic summaries composing this paper are distilled from two 1.5-hour small group discussions that included 6-8 participants, each representing a mix of crop researchers and livestock researchers, data scientists, social scientists, engineers, and other diverse stakeholders. Subsequent to the conference, additional writing, idea exchanges, and e-mails were shared among participants and the output of those conversations is summarized in this report. Challenges and solutions were identified across multiple topics at the workshop and are reported here. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: 1) innovate in genetic improvement methods development and evaluation and 2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results.These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations on chromosomes 5 and 23. Genotype-by- environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multi-breed datasets. Loci related to metabolism and light- sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight for both agriculture production and basic science.

Feral populations, those which successfully persist outside of cultivation or husbandry, provide unique opportunities to study the genomic impacts of domestication and local adaptation. We argue that by leveraging genomic resources designed for domestic counterparts, powerful phylogenetic and population genomic data collection and analyses can be designed to disentangle complex demographic processes.

Background Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated. Results Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight. Conclusions Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress.

In recent decades, the U.S. beef herd has seen massive gains in efficiency, driven by innovations in genetics, nutrition, reproduction, and resource management. Despite these advances, the industry remains under pressure to further reduce its environmental footprint. Perhaps the greatest opportunity to drive increases in sustainability is through targeted genetic improvement of forage-based cow-calf systems. While the industry has long focused on selecting for pre- and post-weaning performance, improvements in mature cow efficiency are more challenging to attain. One major impediment to directly selecting for cow efficiency is a lack of easily measured indicator phenotypes, leading some segments of the industry to focus exclusively on moderating mature size. The optimal solution to this problem will likely require a more complex understanding of the underlying biology of cow efficiency. As we consider how genomics might increase the environmental sustainability of forage-based beef production, we will need to focus on developing and measuring novel phenotypes and accounting for the complex interplay between an animal’s genetics, environment, and management. Using stochastic genetic simulations, we demonstrate the potential environmental and economic impacts of genetic selection for more efficient and well-adapted cows. We also describe possible solutions for measuring a cow’s efficiency on grass (e.g., forage intake to weaned calf pounds) that could be used to develop novel EPDs and economic selection indexes. Finally, we discuss the concept of precision genomic predictions that account for how genotypes interact with environmental stressors and management differences. We propose two potential solutions to address this concern: 1) Selecting robust cattle that perform well across all environments and management levels or 2) Developing precision predictions for precisely matching animals to their ideal production scheme.

The Animal Science Extension team at the University of Tennessee Institute of Agriculture provides a yearly joint in-service training for county agents. In previous years, in-service trainings were held on a regional basis, with one one-day training in each region of the state for a total of 3 trainings. In 2021, the Animal Science team took a difference approach by offering a centrally located, conference style in-service training that lasted for 3 days. There were separate tracks for 4-H and Agriculture and Natural Resources (ANR) with concurrent sessions, and a few combined sessions with general program updates. A range of topics were covered in species-specific sessions, covering beef, dairy, small ruminant, equine, and poultry. On day 3, to increase agent interaction during the in-service training, the Animal Science Extension team created a mini-academic quadrathlon (AQ) to test the agents in a friendly competition. The AQ had species-specific hands-on lab sections and a quiz bowl competition. Teams were created by Specialists to ensure that each team comprised agents with varying levels of knowledge and experience. In December 2021, fifty-two agents participated in the training. An evaluation was administered at the end of the training to gauge effectiveness of each session. Of the 28 respondents, 15 were ANR agents, 7 were 4-H agents, 5 were ANR/4-H agents, and 1 was an area specialist. When asked to give an overall rating for the quiz bowl session of AQ on scale of 1 to 5, with 1 being poor and 5 being excellent, 71% of respondent responded “5: excellent.” When asked the same for hands-on lab portions of AQ, 95% of agents responded “5: excellent.” Selected comments regarding the AQ during in-service training included: “Day 3 by far was the most fun I have had at an in-service. I loved it.” and “I thought the stations and quiz bowl was a great way to tie in everything that was covered in the first two days. Fun and educational!” Specialists concluded that the AQ was a highly effective way to deliver in-service training to agents in an exciting way. Due to the success of the program, the 2022 in-service training taking place in December, will follow the same format.

Selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally-stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal’s generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (< 10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed characteristics, such as fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (

Understanding the underlying effects of ongoing selection is of interest to livestock breeding programs. Commercial livestock genotyping has generated datasets well-suited to explore how strong artificial selection has impacted the genome over very short time periods. We have developed a method for mapping ongoing polygenic selection in populations with temporally stratified genomic data called Generation Proxy Selection Mapping (GPSM). GPSM utilizes a genome-wide linear mixed model to identify allelic associations with an animal's generation number or some proxy (i.e., birth date). We applied GPSM to two large contemporary beef cattle datasets from Red Angus (n = 46,454) and Simmental (n = 90,580) breeds, with 11,759,568 high-quality imputed variants per animal. This analysis identified 294 distinct loci actively under selection. Due to the high power of these datasets, we found that GPSM could detect very small and recent(< 10 years) allele frequency changes consistent with polygenic selection. These analyses identified variants within genomic regions associated with known breed characteristics, like fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of GPSM loci resided in or near (< 50kb) annotated genes. We leveraged the resolution of our imputed sequence variants to overlay known epigenetic marks and possibly functional regions. 36% of GPSM loci overlapped these putatively functional regions genomic intervals. In addition to GPSM, we used two other more traditional selective sweep methods, the number of segregating loci (nSL) and Raised Accuracy in Sweep Detecting (RAiSD), to detect selection in this data. We observed minimal overlap (< 10% of loci) between traditional selective sweep mapping and GPSM. This suggests that selection mapping with GPSM can complement traditional sweep mapping methods when temporal genomic data exists. In addition to better understanding the biological processes and features that underlie artificial selection, GPSM signatures might serve as important genomic annotations.

The Neosho madtom (Noturus placidus) is a small catfish, generally less than 3 inches in length, unique to the Neosho-Spring River system within the Arkansas River Basin. It was federally listed as threatened in 1990, largely due to habitat loss. For conservation efforts, we generated whole-genome sequence data from 10 Neosho madtom individuals originating from 3 geographically separated populations to evaluate genetic diversity and population structure. A Neosho madtom genome was de novo assembled, and genome size and content were assessed. Single nucleotide polymorphisms were assessed from de Bruijn graphs, and via reference alignment with both the channel catfish (Ictalurus punctatus) reference genome and Neosho madtom reference genome. Principal component analysis and structure analysis indicated weak population structure, suggesting fish from the 3 locations represent a single population. Using a novel method, genome-wide conservation and divergence between the Neosho madtom, channel catfish, and zebrafish (Danio rerio) was assessed by pairwise contig alignment, which demonstrated that genes important to embryonic development frequently had conserved sequences. This research in a threatened species with no previously published genomic resources provides novel genetic information to guide current and future conservation efforts and demonstrates that using whole-genome sequencing provides detailed information of population structure and demography using only a limited number of rare and valuable samples.

The Caribbean is a genetically diverse region with heterogeneous admixture compositions influenced by local island ecologies, migrations, colonial conflicts, and demographic histories. The Commonwealth of Dominica is a mountainous island in the Lesser Antilles historically known to harbor communities with unique patterns of migration, mixture, and isolation. This community-based population genetic study adds biological evidence to inform post-colonial narrative histories in a Dominican horticultural village. High density single nucleotide polymorphism data paired with a previously compiled genealogy provide the first genome-wide insights on genetic ancestry and population structure in Dominica. We assessed family-based clustering, inferred global ancestry, and dated recent admixture by implementing the fastSTRUCTURE clustering algorithm, modeling graph-based migration with TreeMix, assessing patterns of linkage disequilibrium decay with ALDER, and visualizing data from Dominica with Human Genome Diversity Panel references. These analyses distinguish family-based genetic structure from variation in African, European, and indigenous Amerindian admixture proportions, and analyses of linkage disequilibrium decay estimate admixture dates 5–6 generations (~160 years) ago. African ancestry accounts for the largest mixture components, followed by European and then indigenous components; however, our global ancestry inferences are consistent with previous mitochondrial, Y chromosome, and ancestry marker data from Dominica that show uniquely higher proportions of indigenous ancestry and lower proportions of African ancestry relative to known admixture in other French- and English-speaking Caribbean islands. Our genetic results support local narratives about the community’s history and founding, which indicate that newly emancipated people settled in the steep, dense vegetation along Dominica’s eastern coast in the mid-19th century. Strong genetic signals of post-colonial admixture and family-based structure highlight the localized impacts of colonial forces and island ecologies in this region, and more data from other groups are needed to more broadly inform on Dominica’s complex history and present diversity.

The GeneMax (GMX) Advantage test, developed by Zoetis, uses approximately 50,000 single nucleotide polymorphisms (SNP) to predict the genomic potential of a commercial Angus heifer. Genetic predictions are provided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, and Yield. Indices of economically important traits are estimated on an index score (1-100 scale) and are divided into three indices; Cow Advantage index, Feeder Advantage index, and Total Advantage index. The indices provide a genomic prediction of the profitability of the cow’s calves. Therefore, test results can inform selection and culling decisions made by commercial beef cattle producers. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center was utilized to analyze weaning weight, milk, marbling, fat, ribeye area, and carcass weight. Progeny phenotypic data was matched to the respective dam, then the cow’s genomic predictions were compared to the calf’s age-adjusted phenotypes using correlation and linear models. All tested GeneMax scores of the dam were significantly correlated with and predicted calf performance. Our predicted effect sizes, except for fat thickness, were similar to those reported by Zoetis. In conclusion, the GeneMax Advantage test accurately ranks animals based on their genetic merit and is an effective selection tool in commercial cowherds.

Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

Author(s): Rosen, Benjamin D; Bickhart, Derek M; Schnabel, Robert D; Koren, Sergey; Elsik, Christine G; Tseng, Elizabeth; Rowan, Troy N; Low, Wai Y; Zimin, Aleksey; Couldrey, Christine; Hall, Richard; Li, Wenli; Rhie, Arang; Ghurye, Jay; McKay, Stephanie D; Thibaud-Nissen, Francoise; Hoffman, Jinna; Murdoch, Brenda M; Snelling, Warren M; McDaneld, Tara G; Hammond, John A; Schwartz, John C; Nandolo, Wilson; Hagen, Darren E; Dreischer, Christian; Schultheiss, Sebastian J; Schroeder, Steven G; Phillippy, Adam M; Cole, John B; Van Tassell, Curtis P; Liu, George; Smith, Timothy PL; Medrano, Juan F | Abstract: BackgroundMajor advances in selection progress for cattle have been made following the introduction of genomic tools over the past 10-12 years. These tools depend upon the Bos taurus reference genome (UMD3.1.1), which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies.ResultsWe present the new reference genome for cattle, ARS-UCD1.2, based on the same animal as the original to facilitate transfer and interpretation of results obtained from the earlier version, but applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly includes 2.7 Gb and is g250× more continuous than the original assembly, with contig N50 g25 Mb and L50 of 32. We also greatly expanded supporting RNA-based data for annotation that identifies 30,396 total genes (21,039 protein coding). The new reference assembly is accessible in annotated form for public use.ConclusionsWe demonstrate that improved continuity of assembled sequence warrants the adoption of ARS-UCD1.2 as the new cattle reference genome and that increased assembly accuracy will benefit future research on this species.

BackgroundUnderstanding the genetic basis of genotype-by-environment interactions (GxE) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. In addition, GxE information could also be useful to predict the vulnerability of populations to climate change.ResultsHere, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that GxE contribute 10%, 4%, and 3% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. GxE genome-wide association analysis (GWAA) detected a large number of GxE loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects between genotype classes regardless of differences in genotypic means. We also showed that variance-heterogeneity GWAA can detect loci enriched with GxE effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of GxE genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neural signaling, metabolic, hypoxia-induced, and immune system pathways. Knowledge of these pathways will be important as climate change becomes a burden on animal health and productivity. In addition, ecoregion-specific GxE SNPs detected in this study may play a crucial role in identifying resilient and adapted beef cattle across divergent environments.ConclusionsWe detected novel trait associations with large GxE effects for birth weight, weaning weight, and yearling weight. Functional annotation and pathway analysis uncovered genomic regions involved in response to environmental stimuli. We unraveled the relevance and complexity of the genetic basis of GxE underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals

BackgroundSingle nucleotide polymorphism (SNP) arrays have facilitated discovery of genetic markers associated with complex traits in domestic cattle; thereby enabling modern breeding and selection programs. Genome-wide association analyses (GWAA) for growth traits were conducted on 10,837 geographically diverse U.S. Gelbvieh cattle using a union set of 856,527 imputed SNPs. Birth weight (BW), weaning weight (WW), and yearling weight (YW) were analyzed using GEMMA and EMMAX (via imputed genotypes). Genotype-by-environment (GxE) interactions were also investigated.ResultsGEMMA and EMMAX produced moderate marker-based heritability estimates that were similar for BW (0.36–0.37, SE = 0.02–0.06), WW (0.27–0.29, SE = 0.01), and YW (0.39–0.41, SE = 0.01–0.02). GWAA using 856K imputed SNPs (GEMMA; EMMAX) revealed common positional candidate genes underlying pleiotropic QTL for Gelbvieh growth traits on BTA6, BTA7, BTA14, and BTA20. The estimated proportion of phenotypic variance explained (PVE) by the lead SNP defining these QTL (EMMAX) was larger and most similar for BW and YW, and smaller for WW. Collectively, GWAAs (GEMMA; EMMAX) produced a highly concordant set of BW, WW, and YW QTL that met a nominal significance level (P ≤ 1e-05), with prioritization of common positional candidate genes; including genes previously associated with stature, feed efficiency, and growth traits (i.e.,PLAG1,NCAPG,LCORL,ARRDC3,STC2). Genotype-by-environment QTL were not consistent among traits at the nominal significance threshold (P ≤ 1e-05); although some shared QTL were apparent at less stringent significance thresholds (i.e.,P ≤ 2e-05).ConclusionsPleiotropic QTL for growth traits were detected on BTA6, BTA7, BTA14, and BTA20 for U.S. Gelbvieh beef cattle. Seven QTL detected for Gelbvieh growth traits were also recently detected for feed efficiency and growth traits in U.S. Angus, SimAngus, and Hereford cattle. Marker-based heritability estimates and the detection of pleiotropic QTL segregating in multiple breeds support the implementation of multiple-breed genomic selection.

The ability of livestock to reproduce efficiently is critical to the sustainability of animal agriculture. Antral follicle count (AFC) and reproductive tract scores (RTS) can be used to estimate fertility in beef heifers, but the genetic mechanisms influencing variation in these measures are not well understood. Two genome-wide association studies (GWAS) were conducted to identify the significant loci associated with these traits. In total, 293 crossbred beef heifers were genotyped on the Bovine GGP 50K chip and genotypes were imputed to 836,121 markers. A GWAS was performed with the AFC phenotype for 217 heifers with a multi-locus mixed model, conducted using the year, age at time of sampling and principal component analysis groupings as the covariates. The RTS GWAS was performed with 289 heifers using an additive correlation/trend test comparing prepubertal to pubertal heifers. The loci on chromosomes 2, 3 and 23 were significant in the AFC GWAS and the loci on chromosomes 2, 8, 10 and 11 were significant in the RTS GWAS. The significant region on chromosome 2 was similar between both analyses. These regions contained genes associated with cell proliferation, transcription, apoptosis and development. This study proposes candidate genes for beef cattle fertility, although future research is needed to elucidate the precise mechanisms.

BackgroundThe use of array-based SNP genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data in the last decade. While low-density assays work exceptionally well in the context of genomic prediction, they are less useful in mapping and causal variant discovery. This project focuses on maximizing imputation accuracies to the marker set of two high-density research assays, the Illumina Bovine HD, and the GGP-F250 which contains a large proportion of rare and potentially functional variants (~850,000 total SNPs). This 850K SNP set is well-suited for both imputation to sequence-level genotypes and direct downstream analysis.ResultsWe find that a large multi-breed composite imputation reference comprised of 36,131 samples with either HD and/or F250 genotypes significantly increases imputation accuracy compared to a standard within-breed reference panel, particularly at low minor allele frequencies. Imputation accuracies were maximized when an individual’s ancestry was adequately represented in the composite reference, particularly with complete 850K genotypes. The addition of rare content from the F250 to our composite reference panel significantly increased the imputation accuracy of rare variants found exclusively on the HD. Additionally, we identify 50,000 variants as an ideal starting density for 850K imputation.ConclusionUsing high-density genotypes on all available individuals in a multi-breed reference panel maximizes imputation accuracy for all cattle populations. Admixed breeds or those sparsely represented in the composite reference are still imputed at high accuracy which will increase further as the reference panel grows. We expect that the addition of rare variation from the F250 will increase the accuracy of imputation at the sequence level.

The eukaryotic green alga Chlamydomonas reinhardtii has been studied extensively within the biofuel industry as a model organism, as researchers look towards algae to provide chemical feedstocks (i.e., lipids) for the production of liquid transportation fuels. C. reinhardtii, however, is unsuitable for high-level production of such precursors due to its relatively poor lipid accumulation and fresh-water demand. In this study we offer insight into the primary light harvesting and electron transfer reactions that occur during phototropic growth in a high-salt tolerant strain of Chlorella (a novel strain introduced here as NE1401), a single-celled eukaryotic algae also in the phylum Chlorophyta. Under nutrient starvation many eukaryotic algae increase dramatically the amount of lipids stored in lipid bodies within their cell interiors. Microscopy and lipid analyses indicate that Chlorella sp. NE1401 may become a superior candidate for algal biofuels production. We have purified highly active Photosystem 1 (PS1) complexes to study in vitro, so that we may understand further the photobiochemisty of this promising biofuel producer and how its characteristics compare and contrast with that of the better understood C. reinhardtii. Our findings suggest that the PS1 complex from Chlorella sp. NE1401 demonstrates similar characteristics to that of C. reinhardtii with respect to light-harvesting and electron transfer reactions. We also illustrate that the relative extent of the light state transition performed by Chlorella sp. NE1401 is smaller compared to C. reinhardtii, although they are triggered by the same dynamic light stresses.

One of the goals of the National Center of Applied Reproduction and Genomics (NCARG) is to demonstrate in real-world settings the potential of new technologies. To this end, we evaluated the predictive ability of the Zoetis GeneMax Advantage genomic prediction. The GeneMax Advantage test analyzes tens of thousands of single nucleotide polymorphisms (SNP) to predict the genetic potential of a commercial Angus female. Genetic predictions are provided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, and Yield. Indices of economically important traits are estimated on an index score (1–100 scale) and are reported in three indices; Cow Advantage Index, Feeder Advantage Index, and Total Advantage Index. Producers can use the scores and indices to make selection, culling, and mating decisions. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center was utilized to analyze Weaning Weight, Milk, Marbling, Fat Thickness, Ribeye Area and Carcass Weight. Progeny phenotypic data was matched to the respective dam, and the genomic predictions were then compared to the phenotypic data using correlation and linear models in R software. Linear models accounted for differences in sex, birth year, and the random effect of sire. Interestingly, all genomic predictions had correlations with progeny phenotypes that were significantly different from zero (P-value < 0.05). Likewise in the linear models, genomic predictions for all analyzed traits were significantly associated with calf performance (Table 1). Academics, farmers and ranchers, and extension professionals can trust the effectiveness of GeneMax Advantage genomic predictions in commercial Angus cattle.

Cattle poorly adapted to their environment result in lost revenue and jeopardize the stability of the food supply. Genomic data now allows us to rigorously analyze adaptations and avoid the generation of animals that will not thrive. We used selection scans for local adaptation, genotype-by-environment genome-wide association analyses, creation of hair shedding genomic predictions and environmental region-specific genomic predictions of growth traits to characterize and predict local adaptation in beef cattle. Analyzing ~40,000 cattle from three breed associations with ~850,000 high-accuracy imputed SNPs, we used novel selection mapping methods to identify genomic loci responsible for adaptation. We identify 19 different loci (harboring 24 annotated genes) as responding to selection to local adaptation. In cooperation with 74 producers across the United States, over 12,000 cattle were scored on a scale of 1–5 for the early-summer hair shedding phenotype in 2016, 2017, and 2018. Participating cattle were genotyped using the GGP-F250 SNP panel developed by the University of Missouri, which contains ~170,000 candidate functional variants and ~30,000 variants in common with beef cattle industry standard genotyping assays. Genomic breeding values were generated with a repeated records model using these phenotypes. Further, we identified loci with large allele substitution effects for hair shedding. When local adaptations exist, ranking animals using a regional genetic evaluation will be different from national cattle evaluations. We developed region-specific genomic predictions using a multivariate model in which phenotypes from different regions were fit as separate dependent variables. Genetic correlations between regions were moderate, indicating substantial re-ranking between environmental regions. These genomic predictions will allow rapid identification of cattle best suited to an environment.

The use of array-based SNP genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data in the last decade. While low-density assays work exceptionally well in the context of genomic prediction, they are less useful in mapping and causal variant discovery. This project focuses on maximizing imputation accuracies to the marker set of two high-density research assays, the Illumina Bovine HD, and the GGP-F250 which contains a large proportion of rare and potentially functional variants (~850,000 total SNPs). This 850K SNP set is well-suited for both imputation to sequence-level genotypes and direct downstream analysis. For testing, 310 animals from multiple breeds, all with observed HD and F250 genotypes, were downsampled to various commercial chip densities ranging from 8K–130K markers. We use both well-established and novel measures of imputation accuracy to categorize precisely where, why, and how imputation errors are made. These metrics provide insights into downstream interpretation and identify situations where caution should be exercised when analyzing imputed variants. We find that a large multi-breed composite imputation reference comprised of 36,131 samples with either HD and F250 genotypes significantly increases imputation accuracy compared to a standard within-breed reference panel, particularly at low minor allele frequencies. Breed composition information for each animal in our testing panel allowed us to identify how a breed’s representation in the reference panel affects the imputation accuracy of both purebred and admixed animals. Starting chip density also impacts imputation accuracy, but gains appear to plateau at around 50,000 markers. The addition of the F250’s rare variation to the reference panel increased the imputation accuracy of rare variants from the HD assay by an average of 4.32%. We expect this low MAF content from the F250 to have a similar positive impact on rare variant imputation at the sequence level. Early work using 850K imputed data in genomic predictions has shown substantial increases in both chip heritability and prediction accuracies. Using a large multi-breed reference and the best practices identified through this work will maximize imputation accuracies in virtually all cattle populations, particularly ones that are highly admixed with little or no available pedigree information.