Genome-wide association study for postweaning weight traits in Lori-Bakhtiari sheep.

Marker-assisted selection is an effective method in novel animal breeding programs. This study was conducted to perform a genome-wide association study to detect candidate genes and quantitative trait loci associated with postweaning weight traits in meat-type sheep. Body weight records were collected during 29 years (1989 to 2017) in Lori-Bakhtiari sheep flock of the Shooli Breeding Station in Iran. A total of 132 animals were selected based on estimates of breeding values (EBVs) for body weight, using two-tailed and random selection strategies. Genomic DNA was extracted from whole blood samples. The samples were genotyped using Illumina OvineSNP50 BeadChip. De-regressed EBVs for postweaning body weight traits were used as pseudo-phenotypes in a genome-wide association study. One SNP on chromosome 10 (rs406324209) and two SNPs on chromosome 13 (rs401963094 and rs418761613) were significantly (Bonferroni-adjusted p-values < 0.05) associated with postweaning body weight traits. The significant variants accounted for 0.20% and 0.48% of the total genetic variances for 6- and 9-month body weights, respectively. Genomic heritabilities estimated for 6-, 9- and 12-month weights and postweaning weight gain were 0.28 ± 0.34, 0.35 ± 0.29, 0.37 ± 0.34, and 0.16 ± 0.33, respectively. Two significant SNPs were located within the ATP8A2 and PLXDC2 genes, on chromosomes 10 and 13, respectively. Based on the known gene ontologies, both ATP8A2 and PLXDC2 could be considered as candidate genes for postweaning body weight traits.

A fast-linear mixed model for genome-wide haplotype association analysis: application to agronomic traits in maize.

BACKGROUND: Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS). In the haplotype association analysis, both haplotype alleles and blocks are tested. Haplotype alleles can be inferred with the same statistics as SNPs in the linear mixed model, while blocks require the formulation of unified statistics to fit different genetic units, such as SNPs, haplotypes, and copy number variations. RESULTS: Based on the FaST-LMM, the fastLmPure function in the R/RcppArmadillo package has been introduced to speed up genome-wide regression scans by a re-weighted least square estimation. When large or highly significant blocks are tested based on EMMAX, the genome-wide haplotype association analysis takes only one to two rounds of genome-wide regression scans. With a genomic dataset of 541,595 SNPs from 513 maize inbred lines, 90,770 haplotype blocks were constructed across the whole genome, and three types of markers (SNPs, haplotype alleles, and haplotype blocks) were genome-widely associated with 17 agronomic traits in maize using the software developed here. CONCLUSIONS: Two SNPs were identified for LNAE, four haplotype alleles for TMAL, LNAE, CD, and DTH, and only three blocks reached the significant level for TMAL, CD, and KNPR. Compared to the R/lm function, the computational time was reduced by ~ 10-15 times.

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens.

BACKGROUND: Poultry breeding programs have been focused on improvement of growth and carcass traits, however, this has resulted in correlated changes in internal organ weights and increased incidence of metabolic disorders. These disorders can affect feed efficiency or even cause death. We used a high density SNP array (600 K, Affymetrix) to estimate genomic heritability, perform genome-wide association analysis, and identify genomic regions and positional candidate genes (PCGs) associated with internal organ traits in an F2 chicken population. We integrated knowledge of haplotype blocks, selection signature regions and sequencing data to refine the list of PCGs. RESULTS: Estimated genomic heritability for internal organ traits in chickens ranged from low (LUNGWT, 0.06) to high (GIZZWT, 0.45). A total of 20 unique 1 Mb windows identified on GGA1, 2, 4, 7, 12, 15, 18, 19, 21, 27 and 28 were significantly associated with intestine length, and weights or percentages of liver, gizzard or lungs. Within these windows, 14 PCGs were identified based on their biological functions: TNFSF11, GTF2F2, SPERT, KCTD4, HTR2A, RB1, PCDH7, LCORL, LDB2, NR4A2, GPD2, PTPN11, ITGB4 and SLC6A4. From those genes, two were located within haplotype blocks and three overlapped with selection signature regions. A total of 13,748 annotated sequence SNPs were in the 14 PCGs, including 156 SNPs in coding regions (124 synonymous, 26 non-synonymous, and 6 splice variants). Seven deleterious SNPs were identified in TNFSF11, NR4A2 or ITGB4 genes. CONCLUSIONS: The results from this study provide novel insights to understand the genetic architecture of internal organ traits in chickens. The QTL detection performed using a high density SNP array covered the whole genome allowing the discovery of novel QTL associated with organ traits. We identified PCGs within the QTL involved in biological processes that may regulate internal organ growth and development. Potential functional genetic variations were identified generating crucial information that, after validation, might be used in poultry breeding programs to reduce the occurrence of metabolic disorders.

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach.

BACKGROUND: Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations. RESULTS: Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1-4, 6-7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. CONCLUSIONS: The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.

A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens.

BACKGROUND: Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken's carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds. RESULTS: ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs. CONCLUSIONS: This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.

The relationship between serum anti-Müllerian hormone concentrations and fertility, and genome-wide associations for anti-Müllerian hormone in Holstein cows.

The objectives of this study were to (1) evaluate factors associated with variation in circulating anti-Müllerian hormone (AMH) concentrations, (2) establish an optimum AMH threshold predictive of pregnancy to first artificial insemination (P/AI), (3) examine the relationship between AMH and fertility (P/AI, pregnancy loss between 30 and 60 d after artificial insemination, and pregnancy risk up to 250 d postpartum), and (4) identify quantitative trait loci associated with phenotypic variation of AMH concentrations in dairy cows. Serum AMH concentrations (pg/mL) were determined at 7 ± 2.4 d postpartum in 647 lactating Holstein cows (213 primiparous, 434 multiparous) from 1 research and 6 commercial dairy herds in Alberta, Canada. Of these, 589 cows were genotyped on the 26K Bovine BeadChip (Neogen Inc., Lincoln, NE) and subsequently imputed to the Illumina Bovine High Density BeadChip (Illumina, San Diego, CA) for genome-wide association analysis for variation in serum AMH concentrations. Factors associated with variation in serum AMH concentrations and the relationship between categories of AMH and aforementioned fertility outcomes were evaluated only in a subset of 460 cows that had a complete data set available. The overall mean (±standard error of the mean), median, minimum, and maximum AMH concentrations were 191.1 ± 6.3, 151.7, 13.9, and 1,879.0 pg/mL, respectively. The AMH concentrations were not associated with herd, precalving body condition score, postpartum week, and season of sampling; the lactation number, however, had a quadratic relationship with serum AMH concentrations (116.2, 204.9 204.5, and 157.9 pg/mL for first, second, third, and ≥fourth lactation, respectively). The optimum AMH threshold predictive of P/AI could not be established because the receiver operating characteristic curve analysis model was nonsignificant. Categories of AMH [low (<83.0 pg/mL; n = 92), intermediate (≥83.0 to ≤285.0 pg/mL; n = 276), and high (>285.0 pg/mL; n = 92) based on lowest 20%, intermediate 60%, and highest 20% serum AMH) had no associations with P/AI (34, 43, and 40%), pregnancy loss between 30 and 60 d after artificial insemination (20, 12, and 8%), or pregnancy risk up to 250 d postpartum. One candidate gene associated with AMH production [AMH gene on Bos taurus autosome (BTA) 7] and 4 candidate genes related to embryo development (SCAI and PPP6C genes on BTA11 and FGF18 and EEF2K genes on BTA20 and BTA25, respectively) were in linkage disequilibrium with single nucleotide polymorphisms associated with phenotypic variation in serum AMH in dairy cows.

Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids.

BACKGROUND: Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA). RESULTS: Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions. CONCLUSIONS: The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.

Genomic variance estimates: With or without disequilibrium covariances?

Whole-genome regression methods are often used for estimating genomic heritability: the proportion of phenotypic variance that can be explained by regression on marker genotypes. Recently, there has been an intensive debate on whether and how to account for the contribution of linkage disequilibrium (LD) to genomic variance. Here, we investigate two different methods for genomic variance estimation that differ in their ability to account for LD. By analysing flowering time in a data set on 1,057 fully sequenced Arabidopsis lines with strong evidence for diversifying selection, we observed a large contribution of covariances between quantitative trait loci (QTL) to the genomic variance. The classical estimate of genomic variance that ignores covariances underestimated the genomic variance in the data. The second method accounts for LD explicitly and leads to genomic variance estimates that when added to error variance estimates match the sample variance of phenotypes. This method also allows estimating the covariance between sets of markers when partitioning the genome into subunits. Large covariance estimates between the five Arabidopsis chromosomes indicated that the population structure in the data led to strong LD also between physically unlinked QTL. By consecutively removing population structure from the phenotypic variance using principal component analysis, we show how population structure affects the magnitude of LD contribution and the genomic variance estimates obtained with the two methods.

Short communication: Genetic variation of riboflavin content in bovine milk.

Riboflavin (vitamin B2) is an essential water-soluble vitamin; elderly people and adolescents in particular can have poor riboflavin status. In Western diets, milk and dairy products are primary sources of riboflavin, but little is known about the natural variation within and among bovine breeds, and how genetic and environmental factors can affect the riboflavin content in milk. As a part of the Danish-Swedish Milk Genomics Initiative, the aim of the study was to quantify milk riboflavin content using reverse-phase HPLC in 2 major Danish dairy breeds. The results showed substantial interbreed differences in milk riboflavin content. Milk from Danish Jersey cows contained significantly higher levels of riboflavin (1.93mg/L of milk) than milk from Danish Holstein cows (1.40mg/L of milk). Furthermore, genetic analyses revealed high heritabilities in both breeds (0.52 for Danish Holstein and 0.31 for Danish Jersey). A genomic association study found 35 significant single nucleotide polymorphisms (false discovery rate<0.10) to be associated with riboflavin content in milk in Jersey cows (all on BTA14 and BTA17), and 511 significant single nucleotide polymorphisms in Holstein cows spread over 25 different autosomes with BTA13 and BTA14 having the most promising quantitative trait loci. The best candidate gene found within the identified quantitative trait loci was SLC52A3, a riboflavin transporter gene, which was among the significant markers on BTA13 in Holstein cows.

Phenotypic and genetic associations of milk traits with milk coagulation properties.

The aim of this study was to examine milk composition and rennet-induced coagulation properties of milk from 892 individual Danish Holstein and Danish Jersey cows and determine the genetic influences on these properties by determining heritability and genomic correlations with single nucleotide polymorphisms identified by the bovine HD Beadchip (Illumina Inc., San Diego, CA). Despite no signs of clinical mastitis, milk from cows with somatic cell counts >500,000 cells/mL showed altered milk composition, indicating impaired barrier between the milk and the blood. Curd-firming rate (CFR) and rennet coagulation time (RCT) were used to describe milk coagulation properties (MCP). These traits describe the second phase of milk coagulation and were mutually negatively correlated, but only to some extent associated with the same compositional traits. In both breeds, CFR were highly correlated with protein content, whereas longer RCT were primarily associated with lower milk pH. Estimated heritabilities for milk production and compositional traits ranged from 0.09 for yield to 0.82 for citric acid in Danish Jersey cows, and from 0.21 for yield to 0.59 for citric acid in Danish Holstein cows. Heritabilities for MCP traits varied considerably between breeds, and were estimated to be 0.28 for RCT and 0.75 for CFR in Danish Holstein cows and 0.45 for RCT and 0.15 for CFR in Danish Jersey cows. This difference was further reflected in the genomic correlations between RCT and CFR which was -0.90 in Danish Holstein and 0.06 in Danish Jersey. These data suggest that potential for changing MCP through breeding exists, but the genetic background of the MCP traits might be different in different breeds; therefore, using Danish Holstein as background for Danish Jersey is not trivial. Thereby, the study underlines the need for breed-specific models.

Genome-Wide Association Studies of Live Weight at First Breeding at Eight Months of Age and Pregnancy Status of Ewe Lambs.

This study estimated genetic parameters and identified candidate genes associated with live weight, and the occurrence of pregnancy in 1327 Romney ewe lambs using genome-wide association studies. Phenotypic traits considered were the occurrence of pregnancy in ewe lambs and live weight at eight months of age. Genetic parameters were estimated, and genomic variation was assessed using 13,500 single-nucleotide polymorphic markers (SNPs). Ewe lamb live weight had medium genomic heritability and was positively genetically correlated with occurrence of pregnancy. This suggests that selection for heavier ewe lambs is possible and would likely improve the occurrence of pregnancy in ewe lambs. No SNPs were associated with the occurrence of pregnancy; however, three candidate genes were associated with ewe lamb live weight. Tenascin C (TNC), TNF superfamily member 8 (TNFSF8) and Collagen type XXVIII alpha 1 chain (COL28A1) are involved in extracellular matrix organization and regulation of cell fate in the immune system. TNC may be involved in ewe lamb growth, and therefore, could be of interest for selection of ewe lamb replacements. The association between ewe lamb live weight and TNFSF8 and COL28A1 is unclear. Further research is needed using a larger population to determine whether the genes identified can be used for genomic selection of replacement ewe lambs.

Genomic heritability and correlation between carcass traits in Japanese Black cattle evaluated under different ceilings of relatedness among individuals.

The investigation of carcass traits to produce meat with high efficiency has been in focus on Japanese Black cattle since 1972. To implement a successful breeding program in carcass production, a comprehensive understanding of genetic characteristics and relationships between the traits is of paramount importance. In this study, genomic heritability and genomic correlation between carcass traits, including carcass weight (CW), rib eye area (REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate (YI), and beef marbling score (BMS) were estimated using the genomic data of 9,850 Japanese Black cattle (4,142 heifers and 5,708 steers). In addition, we investigated the effect of genetic relatedness degree on the estimation of genetic parameters of carcass traits in sub-populations created based on different GRM-cutoff values. Genome-based restricted maximum likelihood (GREML) analysis was applied to estimate genetic parameters. Using all animal data, the heritability values for carcass traits were estimated as moderate to relatively high magnitude, ranging from 0.338 to 0.509 with standard errors, ranging from 0.014 to 0.015. The genetic correlations were obtained low and negative between SFT and REA [-0.198 (0.034)] and between SFT and BMS [-0.096 (0.033)] traits, and high and negative between SFT and YI [-0.634 (0.022)]. REA trait was genetically highly correlated with YI and BMS [0.811 (0.012) and 0.625 (0.022), respectively]. In sub-populations created based on the genetic-relatedness ceiling, the heritability estimates ranged from 0.212 (0.131) to 0.647 (0.066). At the genetic-relatedness ceiling of 0.15, the correlation values between most traits with low genomic correlation were overestimated while the correlations between the traits with relatively moderate to high correlations, ranging from 0.380 to 0.811, were underestimated. The values were steady at the ceilings of 0.30-0.95 (sample size of 5,443-9,850) for most of the highly correlated traits. The results demonstrated that there is considerable genetic variation and also favorable genomic correlations between carcass traits. Therefore, the genetic improvement for the traits can be simultaneously attained through genomic selection. In addition, we observed that depending on the degree of relationship between individuals and sample size, the genomic heritability and correlation estimates for carcass traits may be different.

Genetic Architecture of Abdominal Fat Deposition Revealed by a Genome-Wide Association Study in the Laying Chicken.

Fat has a high energy density, and excessive fatness has been recognized as a problem for egg production and the welfare of chickens. The identification of a genetic polymorphism controlling fat deposition would be helpful to select against excessive fatness in the laying hen. This study aimed to estimate genomic heritability and identify the genetic architecture of abdominal fat deposition in a population of chickens from a Dongxiang blue-shelled local breed crossbred with the White Leghorn. A genome-wide association study was conducted on abdominal fat percentage, egg production and body weights using a sample of 1534 hens genotyped with a 600 K Chicken Genotyping Array. The analysis yielded a heritability estimate of 0.19 ± 0.04 for abdominal fat percentage; 0.56 ± 0.04 for body weight at 72 weeks; 0.11 ± 0.03 for egg production; and 0.24 ± 0.04 for body weight gain. The genetic correlation of abdominal fat percentage with egg production between 60 and 72 weeks of age was -0.35 ± 0.18. This implies a potential trade-off between these two traits related to the allocation of resources. Strong positive genetic correlations were found between fat deposition and weight traits. A promising locus close to COL12A1 on chromosome 3, associated with abdominal fat percent, was found in the present study. Another region located around HTR2A on chromosome 1, where allele substitution was predicted to be associated with body weight gain, accounted for 2.9% of phenotypic variance. Another region located on chromosome 1, but close to SOX5, was associated with egg production. These results may be used to influence the balanced genetic selection for laying hens.

Heritability estimates of distichiasis in Staffordshire bull terriers using pedigrees and genome-wide SNP data.

BACKGROUND: Distichiasis is the most frequently recorded eye disorder in the Norwegian Staffordshire bull terrier (SBT). The condition is often mild but can, in severe cases, lead to pain and blindness. The current study's main purpose was to estimate the heritability based on pedigree information as well as single nucleotide polymorphisms (SNPs) to evaluate whether it is realistic to reduce the frequency by systematic breeding. The majority of the dogs had only one examination as a young puppy. To evaluate whether this early screening gave a reliable representation of the disease burden in the population, we compared the diagnosis in puppies and adult dogs. RESULTS: Our material consisted of data from 4177 dogs with an overall prevalence of distichiasis of 8.38% (CI 7.56-9.26). The prevalence in puppies examined around eight weeks of age was significantly lower than in dogs examined after 52 weeks (2.87%, CI 2.29-3.54 versus 18.72%, CI 16.71-20.87). The heritability was estimated in dogs examined after 52 weeks. We used both pedigree (1391 dogs) and genotype (498 dogs) information for the estimates. The pedigree-based heritability was ~ 0.22 (on the underlying scale ~ 0.48), while the genomic-based heritability (on the underlying scale) was ~ 0.47, and ~ 0.37 when excluding close relatives with equal affection status. CONCLUSIONS: Screening for distichiasis in puppies before eight weeks of age is not sufficient to give an accurate estimate of the prevalence, and an additional examination after one year is recommended. The heritability of distichiasis is medium to high, showing that it should be possible to reduce the prevalence by selective breeding.

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus.

Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.

Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries' genetic values and predict unobserved entries' genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.

Genetic Contribution to Variation in Blood Calcium, Phosphorus, and Alkaline Phosphatase Activity in Pigs.

Blood values of calcium (Ca), inorganic phosphorus (IP), and alkaline phosphatase activity (ALP) are valuable indicators for mineral status and bone mineralization. The mineral homeostasis is maintained by absorption, retention, and excretion processes employing a number of known and unknown sensing and regulating factors with implications on immunity. Due to the high inter-individual variation of Ca and P levels in the blood of pigs and to clarify molecular contributions to this variation, the genetics of hematological traits related to the Ca and P balance were investigated in a German Landrace population, integrating both single-locus and multi-locus genome-wide association study (GWAS) approaches. Genomic heritability estimates suggest a moderate genetic contribution to the variation of hematological Ca (N = 456), IP (N = 1049), ALP (N = 439), and the Ca/P ratio (N = 455), with values ranging from 0.27 to 0.54. The genome-wide analysis of markers adds a number of genomic regions to the list of quantitative trait loci, some of which overlap with previous results. Despite the gaps in knowledge of genes involved in Ca and P metabolism, genes like THBS2, SHH, PTPRT, PTGS1, and FRAS1 with reported connections to bone metabolism were derived from the significantly associated genomic regions. Additionally, genomic regions included TRAFD1 and genes coding for phosphate transporters (SLC17A1-SLC17A4), which are linked to Ca and P homeostasis. The study calls for improved functional annotation of the proposed candidate genes to derive features involved in maintaining Ca and P balance. This gene information can be exploited to diagnose and predict characteristics of micronutrient utilization, bone development, and a well-functioning musculoskeletal system in pig husbandry and breeding.

Do Molecular Markers Inform About Pleiotropy?

The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural.

Genomic heritability estimation for the early life-history transition related to propensity to migrate in wild rainbow and steelhead trout populations.

A previous genomewide association study (GWAS) identified SNP markers associated with propensity to migrate of rainbow and steelhead trout (Oncorhynchus mykiss) in a connected population with free access to the ocean in Upper Yakima River (UYR) and a population in Upper Mann Creek (UMC) that has been sequestered from its access to the ocean for more than 50 years. Applying genomic heritability estimation using the same dataset, we found that smoltification in the UYR population were almost completely determined by additive effects, with 95.5% additive heritability and 4.5% dominance heritability, whereas smoltification in the UMC population had substantial dominance effects, with 0% additive heritability and 39.3% dominance heritability. Dominance test detected one SNP marker (R30393) with significant dominance effect on smoltification (P = 1.98 × 10(-7)). Genomic-predicted additive effects completely separated migratory and nonmigratory fish in the UYR population, whereas genomic-predicted dominance effects achieved such complete separation in the UMC population. The UMC population had higher genomic additive and dominance correlations than the UYR population, and fish between these two populations had the least genomic correlations. These results suggested that blocking the free access to the ocean may have reduced genetic diversity and increased genomic similarity associated with the early life-history transition related to propensity to migrate.