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Optimizing cheese yield and quality is of central importance to cheese manufacturing. The yield is associated with the time it takes before the gel has an optimal consistency for further processing, and it is well known that gel formation differs between individual milk samples. By identifying genomic regions affecting traits related to rennet-induced gelation, the aim of this study was to identify potential candidate genes affecting these traits. Hence, rennet-induced gelation, including rennet coagulation time, gel strength, and yield stress, was measured in skim milk samples collected from 379 animals of the Swedish Red breed using low-amplitude oscillation measurements. All animals had genotypes for almost 621,000 segregating single nucleotide polymorphisms (SNP), identified using the Bovine HD SNPChip (Illumina Inc., San Diego, CA). The genome was scanned for associations, haplotypes based on SNP sets comprising highly associated SNP were inferred, and the effects of the 2 most common haplotypes within each region were analyzed using mixed models. Even though the number of animals was relatively small, a total of 21 regions were identified, with 4 regions showing association with more than one trait. A major quantitative trait locus for all traits was identified around the casein cluster explaining between 9.3 to 15.2% of the phenotypic variation of the different traits. In addition, 3 other possible candidate genes were identified; that is, UDP-N-acetyl-α-D-galactosamine:polypeptide N-acetylgalactosaminyl-transferase 1 (GALNT1), playing a role in O-glycosylation of κ-casein, and 2 cathepsins, CTSZ and CTSC, possibly involved in proteolysis of milk proteins. We have shown that other genes than the casein genes themselves may be involved in the regulation of gelation traits. However, additional analysis is needed to confirm these results. To our knowledge, this is the first study identifying quantitative trait loci affecting rennet-induced gelation of skim milk through a high-density genome-wide association study. [ABSTRACT FROM AUTHOR]

Summary [ABSTRACT FROM AUTHOR]

Osteochondrosis is a growth disorder in the cartilage of young animals and is characterised by lesions found in the cartilage and bone. This study identified quantitative trait loci (QTLs) associated with six osteochondrosis lesion traits in the elbow joint of finishing pigs. The traits were: thickening of the cartilage, lesion in the subchondral bone, irregular cartilage surface, fissure under the cartilage, an irregular sagittal central groove and depression of the proximal edge of the radius. The study comprised 7172 finishing pigs from crossing 12 Duroc boars with 600 crossbred Landrace×Large White sows and included 462 single nucleotide polymorphism markers. The results showed 18 QTLs exceeding the 5% genome-wide threshold. The QTLs associated with lesions in the medial part of the condylus humeri (assumed to be the four main osteochondrosis traits) were, in most cases, at common locations, whereas the QTLs associated with depression of the proximal edge of the radius in general were on the same chromosomes but at separate locations. The detected QTLs explain a large part of the genetic variation, which is promising for incorporating osteochondrosis into a breeding programme using marker-assisted selection. [ABSTRACT FROM AUTHOR]

This study investigated the reliability of genomic estimated breeding values (GEBV) in the Danish Holstein population. The data in the analysis included 3,330 bulls with both published conventional EBV and single nucleotide polymorphism (SNP) markers. After data editing, 38,134 SNP markers were available. In the analysis, all SNP were fitted simultaneously as random effects in a Bayesian variable selection model, which allows heterogeneous variances for different SNP markers. The response variables were the official EBV. Direct GEBV were calculated as the sum of individual SNP effects. Initial analyses of 4 index traits were carried out to compare models with different intensities of shrinkage for SNP effects; that is, mixture prior distributions of scaling factors (standard deviation of SNP effects) assuming 5, 10, 20, or 50% of SNP having large effects and the others having very small or no effects, and a single prior distribution common for all SNP. It was found that, in general, the model with a common prior distribution of scaling factors had better predictive ability than any mixture prior models. Therefore, a common prior model was used to estimate SNP effects and breeding values for all 18 index traits. Reliability of GEBV was assessed by squared correlation between GEBV and conventional EBV (r2 GEBV, EBV), and expected reliability was obtained from prediction error variance using a 5-fold cross validation. Squared correlations between GEBV and published EBV (without any adjustment) ranged from 0.252 to 0.700, with an average of 0.418. Expected reliabilities ranged from 0.494 to 0.733, with an average of 0.546. Averaged over 18 traits, r2 GEBV, EBV was 0.13 higher and expected reliability was 0.26 higher than reliability of conventional parent average. The results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls compared with traditional selection based on parent average information. [ABSTRACT FROM AUTHOR]

The production of fermented milk products has increased worldwide during the last decade and is expected to continue to increase during the coming decade. The quality of these products may be optimized through breeding practices; however, the relations between cow genetics and technological properties of acid milk gels are not fully known. Therefore, the aim of this study was to identify chromosomal regions affecting acid-induced coagulation properties and possible candidate genes. Skim milk samples from 377 Swedish Red cows were rheologically analyzed for acid-induced coagulation properties using low-amplitude oscillation measurements. The resulting traits, including gel strength, coagulation time, and yield stress, were used to conduct a genome-wide association study. Single nucleotide polymorphisms (SNP) were identified using the BovineHD SNPChip (Illumina Inc., San Diego, CA), resulting in almost 621,000 segregating markers. The genome was scanned for putative quantitative trait loci (QTL) regions, haplotypes based on highly associated SNP were inferred, and the additive genetic effects of haplotypes within each QTL region were analyzed using mixed models. A total of 8 genomic regions were identified, with large effects of the significant haplotype explaining between 4.8 and 9.8% of the phenotypic variance of the studied traits. One major QTL was identified to overlap between gel strength and yield stress, the QTL identified with the most significant SNP closest to the gene coding for κ-casein (CSN3). In addition, a chromosome-wide significant region affecting yield stress on BTA 11 was identified to be colocated with PAEP, coding for β-lactoglobulin. Furthermore, the coagulation properties of the genetic variants within the 2 genes were compared with the coagulation properties identified by the patterns of the haplotypes within the regions, and it was discovered that the haplotypes were more diverse and in one case slightly better at explaining the phenotypic variance. Besides these significant QTL comprising the 2 milk proteins, 3 additional genes are proposed as possible candidates, namely RAB22A, CDH13, and STAT1, and all have previously been found to be expressed in the mammary gland. To our knowledge, this is the first attempt to map QTL regions for acid-induced coagulation properties. [ABSTRACT FROM AUTHOR]

Background: Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications., Materials and Methods: Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values., Results: Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples., Conclusions: Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available., Competing Interests: Disclosure XZW is an employee of EMD Serono Research and Development Institute. JN, DF, and ESS are all employees of Foundation Medicine, and ESS is a shareholder in Roche. VF and LKB are employees of Neogenomics and stockholders in NeoGenomics Inc. S-JC and J-HC are employees of ACT Genomics and stockholder in ACT Genomics. JB is employed with BMS, shareholder in BMS, and a shareholder in Johnson & Johnson. JC and SP are employed by OmniSeq, Inc. and hold restricted stock in OmniSeq, Inc. DC and WT are employed with Thermo Fisher Scientific and stockholder in Thermo Fisher Scientific. KE is an employee of Intermountain Genome Diagnostics. GG is employed by BMS and a stockholder in BMS. VRG and R. Samara are employed with QIAGEN. LAK and KCV are employed with Personal Genome Diagnostics. PS is employed by Caris Life Sciences. AS serves on advisory boards and/or receives speech honoraria from AIGnostics, Bayer, Thermo Fisher, Illumina, Astra Zeneca, Novartis, Pfizer, Roche, Seattle Genetics, MSD, BMS, Takeda, Janssen, and Eli-Lily; and research funding from: Chugai and Bristol Myers Squibb. MB is employed by LGC SeraCare. VW is employed with Q Squared Solutions. JCB and MX are employed by AstraZeneca. JCB is employed and holds shares of AstraZeneca. KM and CZ are employees of Illumina Inc and stockholders in Illumina Inc. HM and GP are employees and shareholders in Biodesix Inc. MDH has stock and other ownership interests in Shattuck Labs, Immunai, and Arcus Biosciences; reports honoraria from AstraZeneca and Bristol Myers Squibb; has a consulting or advisory role with Bristol Myers Squibb, Merck, Genentech/Roche, AstraZeneca, Nektar, Syndax, Mirati Therapeutics, Shattuck Labs, Immunai, Blueprint Medicines, Achilles Therapeutics, and Arcus Biosciences; receives research funding from Bristol Myers Squibb (Inst); has patents, royalties, and other intellectual property [a patent has been filed by Memorial Sloan Kettering (PCT/US2015/062208) for the use of TMB for prediction of immunotherapy efficacy, which is licensed to Personal Genome Diagnostics]; and receives travel and accommodation expense reimbursement from AstraZeneca, Bristol Myers Squibb, and Eli Lilly. All other authors have declared no conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)