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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.)

Rennet-induced milk coagulation is an important trait for cheese production. Recent studies have reported an alarming frequency of cows producing poorly coagulating milk unsuitable for cheese production. Several genetic factors are known to affect milk coagulation, including variation in the major milk proteins; however, recent association studies indicate genetic effects from other genomic regions as well. The aim of this study was to detect genetic variation affecting milk coagulation properties, measured as curd-firming rate (CFR) and milk pH. This was achieved by examining allele frequency differences between pooled whole-genome sequences of phenotypically extreme samples (pool-seq).. Curd-firming rate and raw milk pH were measured for 415 Danish Holstein cows, and each animal was sequenced at low coverage. Pools were created containing whole genome sequence reads from samples with "extreme" values (high or low) for both phenotypic traits. A total of 6,992,186 and 5,295,501 SNP were assessed in relation to CFR and milk pH, respectively. Allele frequency differences were calculated between pools and 32 significantly different SNP were detected, 1 for milk pH and 31 for CFR, of which 19 are located on chromosome 6. A total of 9 significant SNP, which were selected based on the possible function of proximal candidate genes, were genotyped in the entire sample set ( = 415) to test for an association. The most significant SNP was located proximal to , explaining 33% of the phenotypic variance. , coding for κ-casein, is the most studied in relation to milk coagulation due to its position on the surface of the casein micelles and the direct involvement in milk coagulation. Three additional SNP located on chromosome 6 showed significant associations explaining 7, 3.6, and 1.3% of the phenotypic variance of CFR. The significant SNP on chromosome 6 were shown to be in linkage disequilibrium with the SNP peaking proximal to ; however, after accounting for the genotype of the peak SNP within this QTL, significant effects (-value < 0.1) could still be detected for 2 of the SNP accounting for 2 and 1% of the phenotypic variance. These 2 interesting SNP were located within introns or proximal to the candidate genes-solute carrier family 4 (sodium bicarbonate cotransporter), member 4 () and LIM and calponin homology domains 1 (), respectively-making them interesting targets for further analysis.

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., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Ventro-cranial chronic pleuritis can be a result of pleuropneumonia and enzootic pneumonia. These diseases cause severe losses in intensive pig production worldwide, but host resistance is difficult to breed for. It could be beneficial to use marker-assisted selection, and a step towards this is to identify genomic regions associated with the trait. For this purpose, 7304 pigs from 11 boar families were analysed for associations between single nucleotide polymorphisms and ventro-cranial chronic pleuritis. The pigs were genotyped by the use of the iSelect Custom 7 K porcine SNP Chip. Quantitative trait loci (QTL), significant at the chromosome-wide level, were identified on Sus scrofa chromosomes (SSC) 2, 4, 11, 12 and 13 in four different boar families. The QTL on SSC 4 in family G was also significant at the genome-wide threshold according to Bonferroni correction. We have identified a number of candidate genes, but the causative mutations still need to be identified. Markers closely associated with the resistance traits have a strong potential for use in breeding towards animals with improved characteristics concerning ventro-cranial chronic pleuritis., (© 2011 Blackwell Verlag GmbH.)

Pleuropneumonia is a major problem in pig production. At the time of slaughter, chronic pleuritis (CP) developed from pleuropneumonia is a common finding, and breeding for a reduced incidence of CP using marker-assisted selection (MAS) would be advantageous. Before applying MAS, quantitative trait loci (QTL) or markers associated with the prevalence of CP should be identified. In this study, 7470 pigs from crosses between 12 Danish Duroc boars and 604 sows (Danish Landrace × Danish Large White) were evaluated for CP located on the dorso-caudal part of the lungs. Quantitative trait loci were identified within boar families using both a Binomial logistic regression method and a chi-square test of association. Significant QTL for CP were detected on Sus scrofa chromosomes (SSC) 2, 8, 12, 13, 14 and 18 using both methods. One QTL on SSC 8 was also detected across families. For the QTL identified within families, the odds-ratio of having CP was approximately twice as high for the unfavourable allele compared to the favourable one. These QTL and closely linked markers show promise for the development of gene-specific markers associated with a reduced incidence of CP located on the dorso-caudal part of the lungs., (© 2010 Aarhus University, Animal Genetics © 2010 Stichting International Foundation for Animal Genetics.)

A QTL study was performed in large half-sib families to characterize the genetic background of variation in pork quality traits as well as to examine the possibilities of including QTL in a marker-assisted selection scheme. The quality traits included ultimate pH in LM and the semimembranosus, drip loss, and the Minolta color measurements L*, a*, and b* representing meat lightness, redness, and yellowness, respectively. The families consist of 3,883 progenies of 12 Duroc boars that were evaluated to identify the QTL. The linkage map consists of 462 SNP markers on 18 porcine autosomes. Quantitative trait loci were mapped using a linear mixed model with fixed factors (sire, sex, herd, month, sow age) and random factors (polygenic effect, QTL effects, and litter). Chromosome-wide and genome-wide significance thresholds were determined by Peipho's approach, and 95% Bayes credibility intervals were estimated from a posterior distribution of the QTL position. In total, 31 QTL for the 6 meat quality traits were found to be significant at the 5% chromosome-wide level, among which 11 QTL were significant at the 5% genome-wide level and 5 of these were significant at the 0.1% genome-wide level. Segregation of the identified QTL in different families was also investigated. Most of the identified QTL segregated in 1 or 2 families. For the QTL affecting ultimate pH in LM and semimembranosus and L* and b* value on SSC6, the positions of the QTL and the shapes of the likelihood curves were almost the same. In addition, a strong correlation of the estimated effects of these QTL was found between the 4 traits, indicating that the same genes control these traits. A similar pattern was seen on SSC15 for the QTL affecting ultimate pH in the 2 muscles and drip loss. The results from this study will be helpful for fine mapping and identifying genes affecting meat quality traits, and tightly linked markers may be incorporated into marker-assisted selection programs.

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 (r(2)(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, r(2)(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.

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.

The European bison (Bison bonasus) has recovered successfully after a severe bottleneck about 90 years ago but has been left with low genetic variability that may substantially hinder parentage and identity analysis. According to pedigree analysis, over 80% of the genes in the contemporary population descend from just two founder animals and inbreeding coefficients averaged almost 0.5, whereas microsatellite heterozygosity does not exceed 0.3. We present a comparison of the effectiveness of 17 microsatellite and 960 single nucleotide polymorphism (SNP) markers for paternity and identity analysis in the European bison. Microsatellite-based paternity and identity analysis was unsuccessful because of low marker heterozygosity and is not a practical approach in this species. Simulations using SNP markers suggest that 80-90 randomly selected loci, or just 50-60 of the most heterozygous loci, would be sufficient to ensure successful paternity and identity analysis in this species. For the purpose of standardizing future analysis, a panel of 50-60 bovine SNPs characterized by high heterozygosity and an even distribution in the genome could be selected. This panel of markers could be typed using VeraCode (Illumina) or similar SNP genotyping systems. The low cost of these SNP genotyping methods compared with a 16 locus microsatellite survey means that off-the-shelf SNP genotyping systems developed for domestic species represent powerful tools for genetic analysis in related species, and can be effective even in bottlenecked species in which heterozygosity of other markers such as microsatellites may be very low.