Relationship Matrix Research Articles

An improved interval‐grey‐probability QFD method for prioritizing engineering characteristics under vague environment

AbstractAt present, the interval‐grey quality function deployment (QFD) has been widely used to address a vague information at the early stage of product design. However, the existing interval‐grey QFD method suffers from a problem that it is incapable of comparing specific values from any two intervals within overlapping segments associated with the importance of engineering characteristics (ECs) during the process of prioritizing their importance. This problem consequently impedes the effective prioritization of their magnitudes, thus hindering the prioritization of the importance of ECs. Aiming at addressing the problem, an improved interval‐grey‐probability QFD method is proposed. The proposed method integrates interval grey numbers, grey relational analysis (GRA), and interval‐probability ranking method into the interval‐grey QFD framework to prioritize the importance of ECs under a vague environment. Specifically, the interval grey numbers are used to assess the importance of customer requirements (CRs) and the GRA method is adopted to construct the relationship matrix of CRs‐ECs, respectively. Based on the importance of CRs and the relationship matrix of CRs‐ECs, the interval‐probability ranking method is introduced to measure the prioritization of the importance of ECs. The proposed method is validated by a multi‐function flash drive. Results show that the proposed method is more accurate than existing methods in terms of the deviation degree.

Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials' optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial's allocation considering the counties' economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.

Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery

AbstractThis study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Assessment of inbreeding coefficients and inbreeding depression on complex traits from genomic and pedigree data in Nelore cattle.

Nelore cattle play a key role in tropical production systems due to their resilience to harsh conditions, such as heat stress and seasonally poor nutrition. Monitoring their genetic diversity is essential to manage the negative impacts of inbreeding. Traditionally, inbreeding and inbreeding depression are assessed by pedigree-based coefficients (F), but recently, genetic markers have been preferred for their precision in capturing the inbreeding level and identifying animals at risk of reduced productive and reproductive performance. Hence, we compared the inbreeding and inbreeding depression for productive and reproductive performance traits in Nelore cattle using different inbreeding coefficient estimation methods from pedigree information (FPed), the genomic relationship matrix (FGRM), runs of homozygosity (FROH) of different lengths (> 1Mb (genome), between 1 and 2Mb - FROH 1-2; 2-4 Mb FROH 2-4 or > 8 Mb FROH >8) and excess homozygosity (FSNP). The correlation between FPed and FROH was lower when the latter was based on shorter segments (r = 0.15 with FROH 1-2, r = 0.20 with FROH 2-4 and r = 0.28 with FROH 4-8). Meanwhile, the FPed had a moderate correlation withFSNP (r = 0.47) and high correlation withFROH >8 (r = 0.58)and FROH-genome (r = 0.60). The FROH-genome was highly correlated with inbreeding based on FROH>8 (r = 0.93) and FSNP (r = 0.88). The FGRM exhibited a high correlation with FROH-genome (r = 0.55)and FROH >8 (r = 0.51) and a lower correlation with other inbreeding estimators varying from 0.30 for FROH 2-4 to 0.37 for FROH 1-2. Increased levels of inbreeding had a negative impact on the productive and reproductive performance of Nelore cattle. The unfavorable inbreeding effect on productive and reproductive traitsranged from 0.12 to 0.51 for FPed, 0.19-0.59 for FGRM, 0.21-0.58 for FROH-genome, and 0.19-0.54 for FSNP per 1% of inbreeding scaled on the percentage of the mean. When scaling the linear regression coefficients on the standard deviation, the unfavorable inbreeding effect varied from 0.43 to 1.56% for FPed, 0.49-1.97% for FGRM, 0.34-2.2% for FROH-genome, and 0.50-1.62% for FSNP per 1% of inbreeding.The impact of the homozygous segments on reproductive and performance traits varied based on the chromosomes. This shows that specific homozygous chromosome segments can be signs of positive selection due to their beneficial effects on the traits. The low correlation observed between FPed and genomic-based inbreeding estimates suggests that the presence of animals with one unknown parent (sire or dam) in the pedigree does not account for ancient inbreeding. The ROH hotspots surround genes related to reproduction, growth, meat quality, and adaptation to environmental stress. Inbreeding depression has adverse effects on productive and reproductive traits in Nelore cattle, particularly on age at puberty in young bulls and heifer calving at 30 months, as well as on scrotal circumference and body weight when scaled on the standard deviation of the trait.

Characterization of fine geographic scale population genetics in sugar kelp (Saccharina latissima) using genome-wide markers

BackgroundKelps are not only ecologically important, being primary producers and habitat forming species, they also hold substantial economic potential. Expansion of the kelp cultivation industry raises the interest for genetic improvement of kelp for cultivation, as well as concerns about genetic introgression from cultivated to wild populations. Thus, increased understanding of population genetics in natural kelp populations is crucial. Genotyping-by-sequencing (GBS) is a powerful tool for studying population genetics. Here, using Saccharina latissima (sugar kelp) as our study species, we characterize the population genetics at a fine geographic scale, while also investigating the influence of marker type (biallelic SNPs versus multi-allelic short read-backed haplotypes) and minor allele count (MAC) thresholds on estimated population genetic metrics.ResultsWe examined 150 sporophytes from 10 locations within a small area in Mid-Norway. Employing GBS, we detected 20,710 bi-allelic SNPs and 42,264 haplotype alleles at 20,297 high quality GBS loci. We used both marker types as well as two MAC filtering thresholds (3 and 15) in the analyses. Overall, higher genetic diversity, more outbreeding and stronger substructure was estimated using haplotypes compared to SNPs, and with MAC 15 compared to MAC 3. The population displayed high genetic diversity (HE ranging from 0.18–0.37) and significant outbreeding (FIS ≤ − 0.076). Construction of a genomic relationship matrix, however, revealed a few close relatives within sampling locations. The connectivity between sampling locations was high (FST ≤ 0.09), but subtle, yet significant, genetic substructure was detected, even between sampling locations separated by less than 2 km. Isolation-by-distance was significant and explained 15% of the genetic variation, while incorporation of predicted currents in an “isolation-by-oceanography” model explained a larger proportion (~ 27%).ConclusionThe studied population is diverse, significantly outbred and exhibits high connectivity, partly due to local currents. The use of genome-wide markers combined with permutation testing provides high statistical power to detect subtle population substructure and inbreeding or outbreeding. Short haplotypes extracted from GBS data and removal of rare alleles enhances the resolution. Careful consideration of marker type and filtering thresholds is crucial when comparing independent studies, as they profoundly influence numerical estimates of population genetic metrics.

Dominant discourses framing the reuse of industrial heritage in the context of mega-events: a relationship matrix approach

The reuse of industrial heritage sites has become increasingly prevalent in the context of hosting mega-events. This paper aims to investigate this trend, specifically in the context of Olympic Games or World Expos, and its impact on urban regeneration strategies. It introduces a methodological tool, namely, the relationship matrix, to identify key factors from ‘best practices’ criteria for managing industrial sites and planning mega-event legacies. We use this matrix to address two research objectives, namely, to determine the role of industrial heritage reuse in mega-event urban projects and to evaluate whether this creates a favourable environment for place-making. This matrix enables us to compare and analyse the Shanghai 2010 World Expo and the London 2012 Summer Olympic Games as illustrative case studies. While guidelines and strategies for mega-events and industrial heritage have evolved towards a proactive people-centred approach, our findings still reveal a bias towards material renewal in dominant discourses during the planning phase. This bias often overlooks or even excludes the memories and experiences of local communities.

Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip.

By assessing the genetic diversity and associated selective traits of Kirghiz sheep (KIR), we aim to uncover the mechanisms that contribute to sheep's adaptability to the Pamir Plateau environment. This study utilized Illumina Ovine SNP50 BeadChip data from KIR residing in the Pamir Plateau, Qira Black sheep (QBS) inhabiting the Taklamakan Desert, and commonly introduced breeds including Dorper sheep (DOR), Suffolk sheep (SUF), and Hu sheep (HU). The data was analyzed using principal component analysis, phylogenetic analysis, population admixture analysis, kinship matrix analysis, linkage disequilibrium analysis, and selective signature analysis. We employed four methods for selective signature analysis: fixation index (Fst), cross-population extended homozygosity (XP-EHH), integrated haplotype score (iHS), and nucleotide diversity (Pi). These methods aim to uncover the genetic mechanisms underlying the germplasm resources of Kirghiz sheep, enhance their production traits, and explore their adaptation to challenging environmental conditions. The test results unveiled potential selective signals associated with adaptive traits and growth characteristics in sheep under harsh environmental conditions, and annotated the corresponding genes accordingly. These genes encompass various functionalities such as adaptations associated with plateau, cold, and arid environment (ETAA1, UBE3D, TLE4, NXPH1, MAT2B, PPARGC1A, VEGFA, TBX15 and PLXNA4), wool traits (LMO3, TRPS1, EPHA5), body size traits (PLXNA2, EFNA5), reproductive traits (PPP3CA, PDHA2, NTRK2), and immunity (GATA3). Our study identified candidate genes associated with the production traits and adaptation to the harsh environment of the Pamir Plateau in Kirghiz sheep. These findings provide valuable resources for local sheep breeding programs. The objective of this study is to offer valuable insights for the sustainable development of the Kirghiz sheep industry.

Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle.

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

Evaluating the Efficacy of Target Capture Sequencing for Genotyping in Cattle.

(1) Background: Target capture sequencing (TCS) is potentially a cost-effective way to detect single-nucleotide polymorphisms (SNPs) and an alternative to SNP array-based genotyping. (2) Methods: We evaluated the effectiveness and reliability of TCS in cattle breeding scenarios using 48 female and 8 male samples. DNA was extracted from blood samples, targeted for 71,746 SNPs with TWIST probes, and sequenced on an MGI platform. GATK and BCFtools were evaluated for the best genotyping calling tool. The genotypes were compared to existing genotypes from the Versa50K SNP array of the same animals by measuring accuracy as concordance (%) and R2. (3) Results: In this study, 71,553 SNPs and 166 indels were identified. The genotype comparison of 37,130 common SNPs between TCS and SNP arrays yielded high agreement, with a mean concordance of 98%, R2 of 0.98 and Cohen's kappa of 0.97. The concordances of sex prediction, parent verification and validation of five genotype markers of interest important for Wagyu breeding were 100% between TCS and SNP array. The elements of the genomic relationship matrix (GRM) constructed from the SNP array and TCS data demonstrated a correlation coefficient approaching unity (r = 0.9998). (4) Conclusions: Compared to the SNP array, TCS is a comparable, cost-effective and flexible platform for genotyping SNPs, including non-model organisms and underrepresented commercial animal populations.

PSI-1 Genetic and non-genetic factors influencing beef cattle attractiveness to horn fly

Abstract Horn flies are the most common summer pests of cattle, leading to substantial economic losses due to impaired production and decreased animal welfare. Genetic and non-genetic factors have been reported to influence cattle susceptibility to horn flies. However, the wide range of heritability estimates, and few identified genomic regions associated with cattle resistance to horn fly may be due to subjective assessment in collecting the fly load phenotype and the small size of the population assessed. Here, beef calves from the UF purebred Brahman (BRA) and Multibreed Angus-Brahman (MAB) populations were video recorded at weaning using a GoPro camera mounted on the top of the chute. Fly infestation was determined as the average fly count obtained from five longitudinal images of each animal using ImageJ software. A total of 748 crossbred and 415 purebred calves born from 2021 to 2023 were evaluated during summer (August or September). Analysis of variance (ANOVA) was performed within herd by fitting linear models that included collection date, age, skin color, coat color, and pre-weaning average daily gain (ADG) as fixed effects, and residual as a random effect. For MAB calves, breed groups (BG) determined based on the percentage of Brahman breed, with BG1 ranging from 0 to 19%, BG2 from 20 to 39%, BG3 from 40 to 59%, BG4 from 60 to 79%, BG5 from 80 to 100%, were also fit in the model. Fly count was transformed to a natural log value (log[count +1]) ensuring normally distribute residuals. As expected, darker colors were significantly more attractive to horn flies, while animals with greaterr percentage of Brahman were less susceptible than animals with greater percentage of Angus breed. Considerable variability in fly load was observed within purebred Brahmans, indicating that additional genetic factors, other than breed, influence cattle resistance to flies. Positive estimates were observed between horn fly load and average daily gain in both herds, which can be due to the fly load below the previously reported economic injury level of 200 flies (BRA: 28.78 ± 38.20, MAB: 19.31 ± 26.72). Variance components were estimated within herds by fitting the same aforementioned effects and a genomic-based relationship matrix (G) constructed with genotypes from the GeneSeek Genomic Profiler F250 SNP chip. The moderate heritability estimate demonstrates the feasibility of selecting cattle resistant to horn flies. Overall, our findings have the potential to contribute to reducing losses and costs in beef cattle operations, while reducing the impact on the environment of overuse of insecticides.

Genomic evaluation for body weight, length and growth estimates in meagre Argyrosomus regius

Meagre (Argyrosomus regius) is a fast-growing sciaenid species which recently received a rising interest in aquaculture diversification and industry sustainability. The present study illustrates the first complete genomic evaluation of meagre for body weight, length and growth parameters using 810 offspring genotyped with a double-digest RAD sequencing approach. Offspring were assigned to both parents using SNP markers and a QTL associated with growth explaining approximately 3.25 % of the phenotypic variance was found. Using pedigree and genomic relationship matrix, moderate to high heritability estimates along with high genetic/genomic correlations between all studied traits were detected with the exception of the initial body weight. The predicted ability of the breeding values was higher using the genomic than the pedigree relationship matrix for body weight and growth. Present findings provide a better genetic insight of the production traits, as well as evidence that the implementation of the genomic information in breeding programs could be beneficial for the parental assignment as well as for the aquaculture production increase through genetic improvement using marker assisted or genomic selection.

510 Utilizing python programming for modern genetic evaluation systems

Abstract Advancements in agricultural data collection and methodologies for data analysis offer opportunities for beef cattle producers to improve herd genetics. Genetic evaluation systems (GES) can benefit from modern programming approaches to keep up with these advancements. Python, a fast-developing programming language with scientific computing libraries such as NumPy and SciPy, offers flexibility that allows for quick adoption of superior algorithms and new data sources, as well as easy maintenance of the software over the long-term. This study evaluated the use of Python (v3.12.1) for developing a GES capable of executing all the necessary functions expected of modern implementation of GES. A simulated dataset was generated with the R package AlphaSimR to mimic characteristics of the Angus beef cattle breed. The dataset included pedigree information, phenotypic data, and true breeding values (TBVs) for each animal. Phenotypic traits included birth weight (BWT), weaning gain (WG), and post-weaning gain (PWG), with a total of 976,400 animals simulated across 15 generations. The GES software was developed in house, and included components such as data import, pedigree and data preprocessing, calculating inbreeding, constructing the inverse of the numerator relationship matrix, solver initialization, estimation of breeding value, and writing results. The genetic evaluation employed pedigree-based best linear unbiased prediction (BLUP) and a multiple-trait animal model, using a preconditioned conjugate gradient iteration algorithm to solve the mixed model equations. Convergence was assessed using the residual vector norm divided by the right-hand-side norm, with a threshold to declare convergence set to 1x10-5. The software was run on a Linux server with an Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz, 96 GB of RAM, and eight cores. Accuracy was measured using Pearson correlation coefficient between TBVs and estimated breeding values (EBVs). To benchmark computing time and solutions, the same dataset and model were input into a commonly used commercial GES, MiXBLUP (v3.0.1). The solver was the most time-consuming component of the GES, with a run time of 27 min. However, the application of multiprocessing reduced the run time by 72% to 8 min. The Pearson correlation coefficient between EBV and TBV was 0.86, 0.79, and 0.73 (P < 0.01) for BWT, WG, and PWG, respectively. In contrast, MiXBLUP completed breeding value prediction in less than 1 min. The Pearson correlation coefficient between Python and MiXBLUP predictions was 1.0 (P < 0.01). While Python is an ideal language for modern GES, further updates are necessary to optimize performance and operations. Future research will focus on refining computational algorithms, exploring parallel processing techniques, and enhancing user interfaces to ensure easy integration with industry practices.

121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.

Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis.

Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image-analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non-additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length-to-width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi-trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non-additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.

Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12.

Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10-6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.

Genome wide detection of CNV and their association with body size in Danzhou chickens

Copy number variation (CNV) is a crucial component of genetic diversity in the genome, serving as the foundation for the genetic architecture and phenotypic variability of complex traits. In this study, we examined CNVs in the Danzhou (DZ) chicken, an indigenous breed exclusive to Hainan Province, China. By employing whole-genome resequencing data from 200 DZ chickens, we conducted a comprehensive genome-wide analysis of CNVs using CNVpytor and performed CNV-based genome-wide association studies (GWAS) on six body size traits, including body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD) utilizing linear mixed model methods considering a genomic relationship matrix. We identified a total of 144,265 autosomal CNVs among the 200 individuals, comprising 67,818 deletions and 76,447 duplications. After merging these variants together, we obtained 4,824 distinct copy number variant regions, which accounted for approximately 20% of the chicken autosomal genome. Furthermore, we discovered several significantly associated CNV segments with body size traits located proximal to genes such as IHH, WNT6, WNT10A, LPR4, FZD2, WNT7B, and GNAS that have been extensively implicated in skeletal development and growth processes. These findings enhance our understanding of CNVs in chickens and their potential impact on body size traits by revealing candidate genes involved in the regulation of these traits. This establishes a solid framework for future studies and may prove particularly beneficial for exploring genetic structural variation in chickens.

Genome-wide assessment of runs of homozygosity to estimate inbreeding in a closed Nellore herd

The objective of this study was to compare the inbreeding coefficients estimated from pedigree data (FPED), traditionally used for internal monitoring by herds and breeding programs, with genomic estimates through the genomic relationship matrix (FGRM) and runs of homozygosity (FROH). Besides, we used the genotype data to obtain the linkage disequilibrium (LD), population effective size (Ne), runs of homozygosity (ROH) islands and functional analysis of the genes presented in ROH islands. Male calves and sires from a closed Nellore herd (Lemgruber line) were genotyped with the Z-chip v2 (Neogen, Lincoln, Nebraska, USA) that contains approximately 30 thousand SNP markers. After quality control, 1088 animals and 21,351 SNPs remained for analysis. Runs of homozygosity (ROH) segments were identified in all analyzed animals, with an average number of 57.93 and average length of 2.95 Mb. We observed a higher occurrence of ROH up to 9 Mb, suggesting a proper mating scheme, which corroborate with LD and Ne analysis results. Estimates of FPED, FGRM and FROH ranged from 0 to 0.2856, from 0.0705 to 0.3686 and from 0.0229 to 0.2762, respectively. Low to moderate correlations were observed between FPED and FGRM (0.17, P < 0.001); FPED and FROH (0.20, P < 0.001) and FGRM and FROH (0.92, P < 0.001). Nevertheless, considering our results we were successful in use runs of homozygosity for estimating autozygosity in a closed Nellore herd. Besides, from ROH islands, we were able to identify a candidate gene (HTR1A) for docility in this Nellore herd.

Genome-wide association study of the loci and candidate genes associated with agronomic traits in Amomum villosum Lour.

Amomum villosum Lour. (A. villosum) is a valuable herbaceous plant that produces the famous traditional Chinese medicine Amori Fructus. Identifying molecular markers associated with the growth of A. villosum can facilitate molecular marker-assisted breeding of the plant. This study employed 75 A. villosum accessions as the test material and utilized 71 pairs of polymorphic simple sequence repeat (SSR) molecular markers to genotype the population. The study analyzed the association between SSR markers and phenotypic traits through the linkage imbalance and population structure analysis. Candidate genes associated with the molecular markers were also identified. The results showed that the phenotypic diversity index range of the 12 agronomic traits was 4.081-4.312 and conformed to a normal distribution. Moreover, 293 allelic variations were detected in the 75 accessions, with an average of 5.32 amplified alleles per loci, ranging from 3 to 8. The maximum number of amplified alleles for AVL12 was 8. The population structure and cluster analysis indicated that the accessions could be divided into two subgroups. Using the mixed linear model (MLM) model of population structure (Q)+kinship matrix (K) for association analysis, three SSR molecular markers significantly associated with the agronomic traits were detected. Fluorescence quantification was used to analyze the expression levels of six candidate genes, and it was found that three of the genes were differentially expressed in phenotypically different accessions. This study is the first to use SSR markers for genome-wide association study (GWAS) mapping and identification of the associated agronomic traits in A. villosum. The results of this study provide a basis for identifying genetic markers for growth traits for marker-assisted breeding in A. villosum.

Genetic and environmental factors shaping goat milk oligosaccharide composition

Oligosaccharides (OS) in milk have been suggested to influence the health and development of the newborn by promoting growth of beneficial gut bacteria, stimulating brain development, and enhancing immune functions. Goat milk is a natural source of specific OS, which could be a potential beneficial ingredient for infant formula. In this study, goat milk oligosaccharide (gMOS) content from approximately 1,000 dairy goats across 18 commercial farms was studied. A genomic relationship matrix was used to unravel genetic and environmental factors shaping gMOS content. The most abundant gMOS identified was 3'-NGL, with a concentration of 32.05 mg/kg, while 3-FL exhibited the lowest concentration at 1.85 mg/kg. Acidic OS had a notably higher content (81.67 mg/kg) than neutral OS (24.88 mg/kg). High variability in gMOS content was observed among individual goats, which could for a large extent be attributed to genetic differences. Heritability estimates ranged from 31% for 3'-GL to 85% for 3-FL. High positive genetic correlations (>0.57) were estimated between 3'-SL and 6'-SL, and between 6'-GL and 3'-GL. The contribution of differences between farms to variation in milk OS content varied from 3% for 3'-NGL to 45% for 6'-SL. While gMOS like 3'-GL, 6'-GL, and 6'-NGL, were significantly influenced by systematic environmental factors such as the lactation stage, the impact of these factors was relatively minor compared with the importance of genetic and farm effects. This research, which stands out due to its relatively large sample size, underscores the pivotal role of genetics, and to a smaller extent farm practices like feed ration, in determining gMOS composition.