Large-Sample Genome-Wide Association Study of Resistance to Retained Placenta in U.S. Holstein Cows.

A genome-wide association study of resistance to retained placenta (RETP) using 632,212 Holstein cows and 74,747 SNPs identified 200 additive effects with p-values < 10-8 on thirteen chromosomes but no dominance effect was statistically significant. The regions of 87.61-88.74 Mb of Chr09 about 1.13 Mb in size had the most significant effect in LOC112448080 and other highly significant effects in CCDC170 and ESR1, and in or near RMND1 and AKAP12. Four non-ESR1 genes in this region were reported to be involved in ESR1 fusions in humans. Chr23 had the largest number of significant effects that peaked in SLC17A1, which was involved in urate metabolism and transport that could contribute to kidney disease. The PKHD1 gene contained seven significant effects and was downstream of another six significant effects. The ACOT13 gene also had a highly significant effect. Both PKHD1 and ACOT13 were associated with kidney disease. Another highly significant effect was upstream of BOLA-DQA2. The KITLG gene of Chr05 that acts in utero in germ cell and neural cell development, and hematopoiesis was upstream of a highly significant effect, contained a significant effect, and was between another two significant effects. The results of this study provided a new understanding of genetic factors underlying RETP in U.S. Holstein cows.

A Million-Cow Validation of a Chromosome 14 Region Interacting with All Chromosomes for Fat Percentage in U.S. Holstein Cows.

A genome-wide association study (GWAS) of fat percentage (FPC) using 1,231,898 first lactation cows and 75,198 SNPs confirmed a previous result that a Chr14 region about 9.38 Mb in size (0.14-9.52 Mb) had significant inter-chromosome additive × additive (A×A) effects with all chromosomes and revealed many new such effects. This study divides this 9.38 Mb region into two sub-regions, Chr14a at 0.14-0.88 Mb (0.74 Mb in size) with 78% and Chr14b at 2.21-9.52 Mb (7.31 Mb in size) with 22% of the 2761 significant A×A effects. These two sub-regions were separated by a 1.3 Mb gap at 0.9-2.2 Mb without significant inter-chromosome A×A effects. The PPP1R16A-FOXH1-CYHR1-TONSL (PFCT) region of Chr14a (29 Kb in size) with four SNPs had the largest number of inter-chromosome A×A effects (1141 pairs) with all chromosomes, including the most significant inter-chromosome A×A effects. The SLC4A4-GC-NPFFR2 (SGN) region of Chr06, known to have highly significant additive effects for some production, fertility and health traits, specifically interacted with the PFCT region and a Chr14a region with CPSF1, ADCK5, SLC52A2, DGAT1, SMPD5 and PARP10 (CASDSP) known to have highly significant additive effects for milk production traits. The most significant effects were between an SNP in SGN and four SNPs in PFCT. The CASDSP region mostly interacted with the SGN region. In the Chr14b region, the 2.28-2.42 Mb region (138.46 Kb in size) lacking coding genes had the largest cluster of A×A effects, interacting with seventeen chromosomes. The results from this study provide high-confidence evidence towards the understanding of the genetic mechanism of FPC in Holstein cows.

Participatory investigation of goat farmers' breeding practices, trait preference, and selection criteria in Burkina Faso.

The community-based breeding program (CBBP) is an innovative approach recommended for genetic improvement and sustainable use of animal genetic resources in extensive farming systems. Successful implementation of this approach requires an understanding of the characteristics of production systems, breeding objectives, and farmers' trait preference. This study aimed to identify the selection criteria of goat farmers in rural areas of Burkina Faso and their potential implications in establishing CBBP. Following focus group discussions, a well-structured questionnaire was designed and administered to 372 randomly selected goat farmers in two different agro-ecological zones. A list of traits obtained during focus group discussions was provided to farmers individually, and they were asked to rank the ones they preferentially use to select breeding animals. Statistical tests were conducted to compare data between the two agro-ecological zones. The results showed that the average goat flock per household was higher (P < 0.05) in the Sudanian (15.68 ± 13.76), compared to the Sudano-Sahelian area (12.93 ± 13.3). Adult females were the dominant age-sex group in both areas. Reasons for culling, keeping breeding bucks, and castration practice were significantly different (P < 0.05) among agro-ecological zones. The most important common criterion for selection in the two zones was body size, coat color, and growth rate for the bucks and does, while fertility (0.06) parameters including twining ability (0.18), kidding frequency (0.11), and mothering ability (0.15) were furthermore considered for breeding does selection. These findings provide valuable insights for developing CBBPs tailored to goat production in the study areas.

A Pilot Detection and Associate Study of Gene Presence-Absence Variation in Holstein Cattle.

Presence-absence variations (PAVs) are important structural variations, wherein a genomic segment containing one or more genes is present in some individuals but absent in others. While PAVs have been extensively studied in plants, research in cattle remains limited. This study identified PAVs in 173 Holstein bulls using whole-genome sequencing data and assessed their associations with 46 economically important traits. Out of 28,772 cattle genes (from the longest transcripts), a total of 26,979 (93.77%) core genes were identified (present in all individuals), while variable genes included 928 softcore (present in 95-99% of individuals), 494 shell (present in 5-94%), and 371 cloud genes (present in <5%). Cloud genes were enriched in functions associated with hormonal and antimicrobial activities, while shell genes were enriched in immune functions. PAV-based genome-wide association studies identified associations between gene PAVs and 16 traits including milk, fat, and protein yields, as well as traits related to health and reproduction. Associations were found on multiple chromosomes, illustrating important associations on cattle chromosomes 7 and 15, involving olfactory receptor and immune-related genes, respectively. By examining the PAVs at the population level, the results of this research provided crucial insights into the genetic structures underlying the complex traits of Holstein cattle.

Systemic interindividual DNA methylation variants in cattle share major hallmarks with those in humans.

BACKGROUND: We recently identified ~ 10,000 correlated regions of systemic interindividual epigenetic variation (CoRSIVs) in the human genome. These methylation variants are amenable to population studies, as DNA methylation measurements in blood provide information on epigenetic regulation throughout the body. Moreover, establishment of DNA methylation at human CoRSIVs is labile to periconceptional influences such as nutrition. Here, we analyze publicly available whole-genome bisulfite sequencing data on multiple tissues of each of two Holstein cows to determine whether CoRSIVs exist in cattle. RESULTS: Focusing on genomic blocks with ≥ 5 CpGs and a systemic interindividual variation index of at least 20, our approach identifies 217 cattle CoRSIVs, a subset of which we independently validate by bisulfite pyrosequencing. Similar to human CoRSIVs, those in cattle are strongly associated with genetic variation. Also as in humans, we show that establishment of DNA methylation at cattle CoRSIVs is particularly sensitive to early embryonic environment, in the context of embryo culture during assisted reproduction. CONCLUSIONS: Our data indicate that CoRSIVs exist in cattle, as in humans, suggesting these systemic epigenetic variants may be common to mammals in general. To the extent that individual epigenetic variation at cattle CoRSIVs affects phenotypic outcomes, assessment of CoRSIV methylation at birth may become an important tool for optimizing agriculturally important traits. Moreover, adjusting embryo culture conditions during assisted reproduction may provide opportunities to tailor agricultural outcomes by engineering CoRSIV methylation profiles.

Current challenges and future of agricultural genomes to phenomes in the USA.

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 genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies 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. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. 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.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.