Univariate Linear Mixed Models Research Articles

Genome-wide association study identifies a novel candidate gene for egg production traits in chickens.

Qingyuan partridge chicken is a renowned indigenous yellow broiler breed in China. Egg production traits are important economic traits for chickens. With the decreasing cost of whole genome resequencing, identifying candidate genes with more precision has become possible. In order to identify molecular markers and candidate genes associated with egg production traits, we conducted genome-wide association studies based on the resequencing data of 287 female Qingyuan partridge chickens. For each hen, age at first egg and egg laying rate were recorded and calculated, respectively. With a univariate linear mixed model, we detected one genome-wide significant single nucleotide polymorphism (SNP) and three chromosome-wide significant SNPs associated with egg laying rate. MTA2 is highly likely to be a functional gene for egg laying rate. Our study identifies MTA2 as the first time to be associated with egg laying rate. Findings in our study will advance our understanding of the genetic basis of egg production and have the potential to improve the efficiency of genomic selection in chickens.

Realised genetic gains from past Finnish silver birch seed orchards

Genetic gains realised through silver birch (Betula pendula Roth) seed orchards were studied using data from common-garden trials established at 34 sites in southern and central Finland. The test materials include seedlots representing 19 commercial seed orchards that operated between 1972 and 2009, and 21 natural stands. All the trials were assessed for several growth and quality traits between the ages of 9 and 24. Realised gains were estimated based on univariate linear mixed models with corrections for latitudinal seed transfer effects. Overall, seed orchard materials outperformed unimproved reference materials in all the traits but results for individual seed orchards varied substantially. Stem volume gains ranged from 1.0% to 31.1%. Improved trees had, on average, 6.8% (up to 26.7%) fewer ramicorn branches and 16.2% (up to 57.6%) fewer forks than unimproved trees. Branch and overall quality showed consistently positive gains. More recently established seed orchards performed better than older ones, and seed orchards with fewer clones outperformed those with dozens of clones. "JR-1" and "JR-2" bi-clonal seed orchards fared differently, with "JR-1" showing modest genetic gains and "JR-2" emerging as the top overall performer across all seed orchards. An alternative statistical concept, the D-value, was utilised to assess the magnitude of genetic gain for different scaled, normally distributed traits. Average D-values implied a similar level of improvement for stem volume, branch quality, and forking, and a smaller gain for stem slenderness and the number of ramicorn branches. The results for individual seed orchards suggest a slight trade-off between stem volume growth and branch quality.

Using PROM(I)S to measure health-related quality of life in patients with a bone fracture: An observational cohort study

IntroductionThe incidence of patients with fractures is increasing and so is the impact on health care systems and society. To improve patient care, measurement of disabilities and impaired health experiences after traumatic musculoskeletal injuries are important. Next to objective clinical parameters, PROM(I)S can be used to map health domains important to patients. We aimed to objectify different aspects of (health-related) quality of life in fracture patients, including the ability to participate in social roles and activities using PROMIS among other PROMs. MethodsAn observational cohort study was performed in which health-related quality of life in fracture patients was measured. Patients aged 18 year and older either treated conservatively or surgically between November 2020 and June 2022 were included. Participants were followed for a maximum of one year and completed the following PROMs: PROMIS-CAT physical function, PROMIS-CAT pain interference, PROMIS-CAT ability to participate in social roles and activities and LEFS or QDASH. We applied a univariate linear mixed model to evaluate significance of improvement. ResultsSeven hundred-forty six patients with a mean age of 54.4 years were included. Mean PROMIS scores were structurally inferior in the lower extremity (LE) fracture group in comparison with the upper extremity (UE) fracture group. For “PROMIS physical function”, UE fracture patients performed better and showed physical progression earlier. For “PROMIS pain interference”, UE fracture patients experienced fewer limitations, but it took longer to experience improvement in this group. For “PROMIS ability to participate in social roles”, significant improvement was only seen in the UE fracture group at one year follow up. ConclusionUpper -and lower extremity fractures can have a significant impact on physical function and social health. Patients with UE fractures tend to have fewer limitations compared to LE fracture patients. Physical function and pain interference is most impaired shortly after the injury in all fracture patients and show significant changes over time, social health improves less over time. Moment of measurement should be based on type of fracture and can differ between individual patients, but when generic measures and outcomes are desirable, PROMIS questionnaires can potentially be used measurement.

In Silico Analysis of Changes in Predicted Metabolic Capabilities of Intestinal Microbiota after Fecal Microbial Transplantation for Treatment of Recurrent Clostridioides difficile Infection.

Although highly effective in treating recurrent Clostridioides difficile infection (RCDI), the mechanisms of action of fecal microbial transplantation (FMT) are not fully understood. The aim of this study was to explore microbially derived products or pathways that could contribute to the therapeutic efficacy of FMT. Stool shotgun metagenomic sequencing data from 18 FMT-treated RCDI patients at 4 points in time were used for the taxonomic and functional profiling of their gut microbiome. The abundance of the KEGG orthology (KO) groups was subjected to univariate linear mixed models to assess the significance of the observed differences between 0 (pre-FMT), 1, 4, and 12 weeks after FMT. Of the 59,987 KO groups identified by shotgun metagenomic sequencing, 27 demonstrated a statistically significant change after FMT. These KO groups are involved in many cellular processes, including iron homeostasis, glycerol metabolism, and arginine regulation, all of which have been implicated to play important roles in bacterial growth and virulence in addition to modulating the intestinal microbial composition. Our findings suggest potential changes in key KO groups post-FMT, which may contribute to FMT efficacy beyond the restored microbial composition/diversity and metabolism of bile acids and short-chain fatty acids. Future larger studies that include a fecal metabolomics analysis combined with animal model validation work are required to further elucidate the molecular mechanisms.

Genome-wide association study for the primary feather color trait in a native Chinese duck.

Background: To reveal candidate genes and the molecular genetic mechanism underlying primary feather color trait in ducks, a genome-wide association study (GWAS) for the primary feather color trait was performed based on the genotyping-by-sequencing (GBS) technology for a native Chinese female duck, Longyan Shan-ma ducks. Methods: Blood genomic DNA from 314 female Longyan Shan-ma duck were genotyped using GBS technology. A GWAS for the primary feather color trait with genome variations was performed using an univariate linear mixed model based on all SNPs in autosomes. Results: Seven genome-wide significant single nucleotide polymorphisms (SNPs, Bonferroni-adjusted p-value <8.03 × 10-7) within the introns of the genes STARD9, ZNF106, SLC7A5, and BANP genes were associated with the primary feather color trait. Twenty-two genome-wide suggestive SNPs (Bonferroni-adjusted p-value <1.61 × 10-5) of 17 genes (besides ZNF106 and SLC7A5) were also identified. Seven SNPs were located at one 0.22Mb region (38.65-38.87Mb) on chromosome 5, and six SNPs were located at one 0.31Mb region (19.53-19.84Mb) on chromosome 11. The functions of STARD9, SLC7A5, BANP, LOC101798015, and IPMK were involved pigmentation and follicle development, especially, STARD9 upregulated expression in black feather (haplotype-CCCC) bulb tissue compared with in pockmarked feather (haplotype-TGTT) bulb tissue, implicating these genes as candidate genes for primary feather color trait. Conclusion: The preliminarily findings suggested candidate genes and regions, and the genetic basis of primary feather color trait in a female duck.

Power analyses for stepped wedge designs with multivariate continuous outcomes.

Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this article, we present computationally efficient power and sample size procedures for stepped wedge cluster randomized trials (SW-CRTs) with multivariate outcomes that differentiate the within-period and between-period intracluster correlation coefficients (ICCs). Under a multivariate linear mixed model, we derive the joint distribution of the intervention test statistics which can be used for determining power under different hypotheses and provide an example using the commonly utilized intersection-union test for co-primary outcomes. Simplifications under a common treatment effect and common ICCs across endpoints and an extension to closed-cohort designs are also provided. Finally, under the common ICC across endpoints assumption, we formally prove that the multivariate linear mixed model leads to a more efficient treatment effect estimator compared to the univariate linear mixed model, providing a rigorous justification on the use of the former with multivariate outcomes. We illustrate application of the proposed methods using data from an existing SW-CRT and present extensive simulations to validate the methods.

Genetic Resistance of Switchgrass to Rust Evaluated in a Composite Upland × Lowland Population in Lab and Field Settings

Maintaining low levels of rust incidence (caused by Puccinia novopanici) in switchgrass (Panicum virgatum L.) breeding populations is a priority for the USDA-ARS program engaged in improving cultivars for high biomass yield and quality. Essential to this goal is the unbiased and accurate estimation of genetic parameters to predict the merits of parents and progeny. Spores of the fungus were inoculated in greenhouse-grown seedling progeny of 31 half-sib families in generation 2 (Gen 2) of a composite Summer × Kanlow population for evaluation of rust incidence on the leaves with a 0–9 rating scale. Two parents were later chosen to cross and develop a linkage mapping population as Gen 3. The Gen 2, 3, and Kanlow seedlings were transplanted into the field located near Mead, NE, in early June 2020 and laid out as a replicated row–column design with six blocks of single-row plots of five plants each. The field trial was rated in September 2021 and 2022 with a 0–4 scale. Lab and field data were subjected to univariate linear mixed models via the restricted maximum likelihood to extract the variance components needed to predict the breeding values. The additive genetic variation was substantial (p &lt; 0.01), enough to result in high heritability estimates ranging from 0.42 ± 14 to 0.73 ± 0.09 at the individual and family mean levels. This result implies that rust resistance is under strong genetic control to use mass selection for obtaining satisfactory gains. A possible rust incidence x year interaction was detected with a Spearman correlation of breeding values of −0.38, caused by significant rank changes of the Gen 3 genotypes in 2022 (a high heat and drought year). Genetic gains were predicted to reduce rust incidence scores by at least two points on the rating scale when selecting backwards, and by one point when selecting individual candidates as parents of the next generation. Faster gains (31 and 59%) were realized relative to the second generation by respectively selecting the top 10% of the families in Gen 3 or the top 10% of genotypes within this group. Based on these results, strategies for controlling the incidence of rust will be developed to optimize gains in the other traits of economic importance.

Genetic parameters and genome-wide association study of digital cushion thickness in Holstein cows.

The digital cushion is linked to the development of claw horn lesions (CHL) in dairy cattle. The objectives of this study were to (1) estimate genetic parameters for digital cushion thickness (DCT), (2) estimate the genetic correlation between DCT and CHL, and (3) identify candidate genes associated with DCT. A cohort of 2,352 Holstein dairy cows were prospectively enrolled on 4 farms and assessed at 4 time points: before calving, immediately after calving, in early lactation, and in late lactation. At each time point, CHL was recorded by veterinary surgeons, and ultrasonographic images of the digital cushion were stored and retrospectively measured at 2 anatomical locations. Animals were genotyped and pedigree details extracted from the national database. Genetic parameters were estimated following a single-step approach implemented in AIREMLF90. Four traits were analyzed: the 2 DCT measurements, sole lesions (sole hemorrhage and sole ulcers), and white line lesions. All traits were analyzed with univariate linear mixed models; bivariate models were fit to estimate the genetic correlation between traits within and between time points. Single-marker and window-based genome-wide association analyses of DCT traits were conducted at each time point; candidate genes were mapped near (<0.2 Mb) or within the genomic markers or windows with the largest effects. Heritability estimates of DCT ranged from 0.14 to 0.44 depending on the location of DCT measurement and assessment time point. The genetic correlation between DCT and sole lesions was generally negative, notably between DCT immediately after calving and sole lesions in early or late lactation, and between DCT in early or late lactation and sole lesion severity in early or late lactation. Digital cushion thickness was not genetically correlated with white line lesions. A polygenic background to DCT was found; genes associated with inflammation, fat metabolism, and bone development were mapped near or within the top markers and windows. The moderate heritability of DCT provides an opportunity to use selective breeding to change DCT in a population. The negative genetic correlation between DCT and sole lesions at different stages of production lends support to current hypotheses of sole lesion pathogenesis. Highlighted candidate genes provide information regarding the complex genetic background of DCT in Holstein cows, but further studies are needed to explore and corroborate these findings.

Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle

BackgroundGenotypic 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.ResultsModerate 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.ConclusionsSixteen 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.

Above‐ and belowground drivers of intraspecific trait variability across subcontinental gradients for five ubiquitous forest plants in North America

AbstractIntraspecific trait variability (ITV) provides the material for species' adaptation to environmental changes. To advance our understanding of how ITV can contribute to species' adaptation to a wide range of environmental conditions, we studied five widespread understorey forest species exposed to both continental‐scale climate gradients, and local soil and disturbance gradients. We investigated the environmental drivers of between‐site leaf and root trait variation, and tested whether higher between‐site ITV was associated with increased trait sensitivity to environmental variation (i.e. environmental fit).We measured morphological (specific leaf area: SLA, specific root length: SRL) and chemical traits (Leaf and Root N, P, K, Mg, Ca) of five forest understorey vascular plant species at 78 sites across Canada. A total of 261 species‐by‐site combinations spanning ~4300 km were sampled, capturing important abiotic and biotic environmental gradients (neighbourhood composition, canopy structure, soil conditions, climate). We used multivariate and univariate linear mixed models to identify drivers of ITV and test the association of between‐site ITV with environmental fit.Between‐site ITV of leaf traits was primarily driven by canopy structure and climate. Comparatively, environmental drivers explained only a small proportion of variability in root traits: these relationships were trait specific and included soil conditions (Root P), canopy structure (Root N) and neighbourhood composition (SRL, Root K). Between‐site ITV was associated with increased environmental fit only for a minority of traits, primarily in response to climate (SLA, Leaf N, SRL).Synthesis. By studying how ITV is structured along environmental gradients among species adapted to a wide range of conditions, we can begin to understand how individual species might respond to environmental change. Our results show that generalisable trait–environment relationships occur primarily aboveground, and only accounted for a small proportion of variability. For our group of species with broad ecological niches, variability in traits was only rarely associated with higher environmental fit, and primarily along climatic gradients. These results point to promising research avenues on the various ways in which trait variation can affect species' performance along different environmental gradients.

Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment

Grasslands contribute considerably to the global carbon cycle and livestock production. However, many of the world grasslands suffer from degradation caused mainly by overgrazing. Remote sensing methods are effective tools for monitoring and estimating grassland vegetation parameters. In this study, we compared the performance of vegetation indices (VIs) obtained from two different sensors to estimate grassland vegetation parameters under different vegetation and soil conditions based on typical grassland biomass gradients formed by long-term controlled grazing experiments. Sentinel-2 and Landsat 8 were selected as data sources to estimate two vegetation parameters, fresh aboveground biomass (AGB) and leaf area index (LAI). Field-measured fresh AGB and LAI data were collected from experimental grasslands with different grazing intensities (GI) in Hulunber, Inner Mongolia, China in 2019. Univariate linear mixed models were established between VIs and field measurements, and grazing intensities were considered as random factors. The results confirmed that vegetation parameters (AGB and LAI) and VIs decreased with increasing GI; however, the decreasing trend was insignificant when the GI exceeded 0.69 Au/ha. VIs derived from Sentinel-2 and Landsat 8 estimated fresh AGB and LAI at 80% accuracy. Sentinel-2 derived VIs yielded higher predictive accuracy than Landsat 8 for both fresh AGB and LAI. Comparing with the other VI inversion models, the normalised difference phenology index derived from Sentinel-2 images estimated the vegetation parameters the most effectively and accurately, with a coefficient of determination (R2) of 0.625 and relative root mean square error (RMSE%) of 18.105% for fresh AGB estimation and R2 of 0.559 and RMSE% of 14.953% for LAI estimation.

Multivariate genome-wide association study of leaf shape in a Populus deltoides and P. simonii F1 pedigree.

Leaf morphology exhibits tremendous diversity between and within species, and is likely related to adaptation to environmental factors. Most poplar species are of great economic and ecological values and their leaf morphology can be a good predictor for wood productivity and environment adaptation. It is important to understand the genetic mechanism behind variation in leaf shape. Although some initial efforts have been made to identify quantitative trait loci (QTLs) for poplar leaf traits, more effort needs to be expended to unravel the polygenic architecture of the complex traits of leaf shape. Here, we performed a genome-wide association analysis (GWAS) of poplar leaf shape traits in a randomized complete block design with clones from F1 hybrids of Populus deltoides and Populus simonii. A total of 35 SNPs were identified as significantly associated with the multiple traits of a moderate number of regular polar radii between the leaf centroid and its edge points, which could represent the leaf shape, based on a multivariate linear mixed model. In contrast, the univariate linear mixed model was applied as single leaf traits for GWAS, leading to genomic inflation; thus, no significant SNPs were detected for leaf length, measures of leaf width, leaf area, or the ratio of leaf length to leaf width under genomic control. Investigation of the candidate genes showed that most flanking regions of the significant leaf shape-associated SNPs harbored genes that were related to leaf growth and development and to the regulation of leaf morphology. The combined use of the traditional experimental design and the multivariate linear mixed model could greatly improve the power in GWAS because the multiple trait data from a large number of individuals with replicates of clones were incorporated into the statistical model. The results of this study will enhance the understanding of the genetic mechanism of leaf shape variation in Populus. In addition, a moderate number of regular leaf polar radii can largely represent the leaf shape and can be used for GWAS of such a complicated trait in Populus, instead of the higher-dimensional regular radius data that were previously considered to well represent leaf shape.

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

BackgroundBackfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds.MethodsData comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10–6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model.ResultsWe found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN.ConclusionsOur results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

Analysis of multivariate longitudinal immuno-epidemiological data using a pairwise joint modelling approach

BackgroundImmuno-epidemiologists are often faced with multivariate outcomes, measured repeatedly over time. Such data are characterised by complex inter- and intra-outcome relationships which must be accounted for during analysis. Scientific questions of interest might include determining the effect of a treatment on the evolution of all outcomes together, or grouping outcomes that change in the same way. Modelling the different outcomes separately may not be appropriate because it ignores the underlying relationships between outcomes. In such situations, a joint modelling strategy is necessary. This paper describes a pairwise joint modelling approach and discusses its benefits over more simple statistical analysis approaches, with application to data from a study of the response to BCG vaccination in the first year of life, conducted in Entebbe, Uganda.MethodsThe study aimed to determine the effect of maternal latent Mycobacterium tuberculosis infection (LTBI) on infant immune response (TNF, IFN-γ, IL-13, IL-10, IL-5, IL-17A and IL-2 responses to PPD), following immunisation with BCG. A simple analysis ignoring the correlation structure of multivariate longitudinal data is first shown. Univariate linear mixed models are then used to describe longitudinal profiles of each outcome, and are then combined into a multivariate mixed model, specifying a joint distribution for the random effects to account for correlations between the multiple outcomes. A pairwise joint modelling approach, where all possible pairs of bivariate mixed models are fitted, is then used to obtain parameter estimates.ResultsUnivariate and pairwise longitudinal analysis approaches are consistent in finding that LTBI had no impact on the evolution of cytokine responses to PPD. Estimates from the pairwise joint modelling approach were more precise. Major advantages of the pairwise approach include the opportunity to test for the effect of LTBI on the joint evolution of all, or groups of, outcomes and the ability to estimate association structures of the outcomes.ConclusionsThe pairwise joint modelling approach reduces the complexity of analysis of high-dimensional multivariate repeated measures, allows for proper accounting for association structures and can improve our understanding and interpretation of longitudinal immuno-epidemiological data.

A genome-wide association study for clinical mastitis in the dual-purpose German Black Pied cattle breed

The dual-purpose German Black Pied Cattle (DSN) has become an endangered breed of approximately 2,550 registered cows in Germany. The breed is genetically related to Holstein-Friesian cattle because the old DSN breed contributed to the selection of the modern Holstein dairy cow. In dairy farms, breeders aim to improve animal health and well-being by reducing the number of mastitis cases, which would also reduce milk losses and treatment costs. On the genomic level, no markers associated with clinical mastitis have been reported in DSN. Therefore, we performed a genome-wide association study on 1,062 DSN cows using a univariate linear mixed model that included a relatedness matrix to correct for population stratification. Although the statistical power was limited by the small population size, 3 markers were significantly associated, and 2 additional markers showed a suggestive association with clinical mastitis. Those markers accounted for 1 to 3% of the variance of clinical mastitis in the examined DSN population. One marker was found in the intragenic region of NEURL1 on BTA26, and the other 4 markers in intergenic regions on BTA3, BTA6, and BTA9. Further analyses identified 23 positional candidate genes. Among them is BMPR1B, which has been previously associated with clinical mastitis in other dairy cattle breeds. The markers presented here can be used for selection for mastitis-resistant animals in the endangered DSN population, and can broadly contribute to a better understanding of mastitis determinants in dairy cattle breeds.

PSIII-10 Effect of WUR genotype on resilience to a polymicrobial natural disease challenge in pigs

Abstract Porcine Reproductive and Respiratory Syndrome (PRRS) is caused by a virus that poses a threat to the global swine industry, costing the U.S. industry over 664 million dollars annually. Vaccination has limited effectiveness due to the virus’ antigenic and genetic shifts. Leveraging genetics to develop more resilient swine can, however, mitigate the effects of PRRS. Previous studies identified a Single Nucleotide Polymorphism (SNP) near the GBP5 gene (WUR) that was associated with resistance and resilience to PRRS, with the G allele being favorable over A. The objective of this study was to determine whether the WUR SNP is also associated with resilience to a polymicrobial natural disease challenge. Using a continuous flow system, a new batch of 60-75 naïve Yorkshire x Landrace nursery piglets was introduced every three weeks into a natural challenge facility that was initially seeded with multiple diseases, including PRRS. Traits recorded were growth rate, feed intake, backfat, loin depth, veterinary treatments, and mortality. Pigs were genotyped using a 600K SNP chip. Data from 2133 pigs were analyzed using a univariate linear mixed model that included, pen, litter, and animal genetics as random effects and WUR genotype as a fixed effect. Frequencies were 0.85, 0.14, and 0.01 for AA, AG, and GG. The G allele was favorable for most traits, with the contrast of AA vs AG significant for average daily gain in the nursery (0.339 vs 0.365 kg/d, p = 0.013) and number of treatments (2.48 vs 2.16 over 180 days, p = 0.072). Mortality rate was 26.0% for AA and 23.8% for AG (not significantly different). In conclusion, the G allele at the WUR SNP is potentially also associated with resilience to multi-factorial disease. Funded by Genome Canada, Genome Alberta, Genome Prairie, PigGen Canada, and USDA-NIFA.

Efficient multivariate analysis algorithms for longitudinal genome-wide association studies.

Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary data are available at Bioinformatics online.

Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.

Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset.

Anchorage loss assessment of the indirect anchor tooth during adjunctive orthodontic treatment.

This study quantitatively assessed movement of anchor teeth connected to a miniscrew (indirect anchor tooth) and investigated factors affecting movement during adjunctive orthodontic treatment. Dental plaster models of 28 patients whose treatment included an indirect anchor tooth on one side were collected before and after treatment. The casts were digitally scanned, and 2 groups were constituted: the indirect anchor teeth (experimental group; n=52) and the untreated teeth (control group; the first and second premolars opposing the indirect anchor tooth to which no orthodontic force was applied; n=55). Pretreatment and posttreatment models were superimposed and the amount and direction of indirect anchor tooth movement were evaluated with the use of a univariate linear mixed model. Possible factors affecting movement of the indirect anchor tooth and its significance were also evaluated with the use of a multiple linear mixed model. The indirect anchor tooth moved 0.91±0.50mm and did not exhibit significant differences in the transverse, vertical, or sagittal directions. The location of the indirect anchor tooth affected movement and the tooth moved significantly more in the mandible than in the maxilla. The indirect anchor tooth can move during adjunctive orthodontic treatment and thus requires careful monitoring for occlusal changes.

Genome wide association study identifies novel potential candidate genes for bovine milk cholesterol content

This study aimed to identify single nucleotide polymorphisms (SNPs) associated with milk cholesterol (CHL) content via a genome wide association study (GWAS). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). GWAS was performed with 1,183 cows and 40,196 SNPs using a univariate linear mixed model. Two and 20 SNPs were significantly associated with CHL_fat and CHL_milk, respectively. The important regions for CHL_fat and CHL_milk were at 41.9 Mb on chromosome (BTA) 17 and 1.6–3.2 Mb on BTA 14, respectively. DGAT1, PTPN1, INSIG1, HEXIM1, SDS, and HTR5A genes, also known to be associated with human plasma CHL phenotypes, were identified as potential candidate genes for bovine milk CHL. Additional new potential candidate genes for milk CHL were RXFP1, FAM198B, TMEM144, CXXC4, MAML2 and CDH13. Enrichment analyses suggested that identified candidate genes participated in cell-cell signaling processes and are key members in tight junction, focal adhesion, Notch signaling and glycerolipid metabolism pathways. Furthermore, identified transcription factors such as PPARD, LXR, and NOTCH1 might be important in the regulation of bovine milk CHL content. The expression of several positional candidate genes (such as DGAT1, INSIG1 and FAM198B) and their correlation with milk CHL content were further confirmed with RNA sequence data from mammary gland tissues. This is the first GWAS on bovine milk CHL. The identified markers and candidate genes need further validation in a larger cohort for use in the selection of cows with desired milk CHL content.