Christian Maltecca

Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost

BackgroundCommercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations.MethodsPedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated.ResultsImputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP).ConclusionsAmong the selection candidates, 95.5 % and 93.5 % of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively,

Genomic selection for producer-recorded health event data in US dairy cattle

24.74 and

Calf birth weight, gestation length, calving ease, and neonatal calf mortality in Holstein, Jersey, and crossbred cows in a pasture system

20.58 per candidate.

BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection

Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended H matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation = 0.005) for lameness to 0.36 (standard deviation = 0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.

Incidence validation and relationship analysis of producer-recorded health event data from on-farm computer systems in the United States

Holstein (HH), Jersey (JJ), and crosses of these breeds were mated to HH or JJ bulls to form purebreds, reciprocal crosses, backcrosses, and other crosses in a rotational mating system. The herd was located at the Center for Environmental Farming Systems in Goldsboro, North Carolina. Data for calf birth weight (CBW), calving ease (0 for unassisted, n=1,135, and 1 for assisted, n=96), and neonatal calf mortality (0 for alive, n=1,150, and 1 for abortions recorded after mid-gestation, stillborn, and dead within 48 h, n=81) of calves (n=1,231) were recorded over 9 calving seasons from 2003 through 2011. Gestation length (GL) was calculated as the number of days from last insemination to calving. Linear mixed models for CBW and GL included fixed effects of sex, parity (first vs. later parities), twin status, and 6 genetic groups: HH, JJ, reciprocal F(1) crosses (HJ, JH), crosses >50% Holsteins (HX) and crosses >50% Jerseys (JX), where sire breed is listed first. The CBW model also included GL as a covariate. Logistic regression for calving ease and neonatal calf mortality included fixed effects of sex, parity, and genetic group. Genetic groups were replaced by linear regression using percentage of HH genes as coefficients on the above models and included as covariates to determine various genetic effects. Year and dam were included as random effects in all models. Female calves (27.57±0.54 kg), twins (26.39±1.0 kg), and calves born to first-parity cows (27.67±0.56 kg) had lower CBW than respective male calves (29.53±0.53 kg), single births (30.71±0.19 kg), or calves born to multiparous cows (29.43±0.52 kg). Differences in genetic groups were observed for CBW and GL. Increased HH percentage in the calf increased CBW (+9.3±0.57 kg for HH vs. JJ calves), and increased HH percentage in the dams increased CBW (+1.71±0.53 kg for calves from HH dams vs. JJ dams); JH calves weighed 1.33 kg more than reciprocal HJ calves. Shorter GL was observed for twin births (272.6±1.1 d), female calves (273.9±0.6 d), and for first-parity dams (273.8±0.6 d). Direct genetic effects of HH alleles shortened GL (-3.5±0.7 d), whereas maternal HH alleles increased GL (2.7±0.6 d). Female calves had lower odds ratio (0.32, confidence interval=0.10-0.99) for neonatal calf mortality in second and later parities than did male calves. Maternal heterosis in crossbred primiparous dams was associated with reduced calf mortality.

Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation

Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits.

Genetic relationships among pig behavior, growth, backfat, and loin muscle area.

The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence rates of on-farm recorded health event data were compared with incidence rates reported in the literature. Putative relationships among common health events were examined using logistic regression for each of 3 timeframes: 0 to 60, 61 to 90, and 91 to 150 d in milk. Health events occurring on average before the health event of interest were included in each model as predictors when significant. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events reported had incidence rates lower than the average incidence rate found in the literature. This may partially represent underreporting by dairy farmers who record disease events only when a treatment or other intervention is required. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. The results of this analysis provide evidence for the plausibility of on-farm recorded health information.

A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure

The objectives of this study were to estimate genetic parameters for fertility of Brown Swiss cattle, considering reproductive measures in different parities as different traits, and to estimate relationships between production traits of first lactation and fertility of heifers and first-parity and second-parity cows. Reproductive indicators were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception rate at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield (pMY), lactation milk yield, and lactation length (LL). Data included 37,546 records on heifers, and 24,098 and 15,653 records on first- and second-parity cows, respectively. Cows were reared in 2,035 herds, calved from 1999 to 2007, and were progeny of 527 AI bulls. Gibbs sampling was implemented to obtain (co)variance components using both univariate and bivariate threshold and censored linear sire models. Estimates of heritability for reproductive traits in heifers (0.016 to 0.026) were lower than those in first-parity (0.017 to 0.142) and second-parity (0.026 to 0.115) cows. Genetic correlations for fertility in first- and second-parity cows were very high (>0.920), whereas those between heifers and lactating cows were moderate (0.348 to 0.709). The latter result indicates that fertility in heifers is a different trait than fertility in lactating cows, and hence it cannot be used as robust indicator of cow fertility. Heifer fertility was not related to production traits in first lactation (genetic correlations between -0.215 and 0.251). Peak milk yield exerted a moderate and unfavorable effect on the interval from parturition to first service (genetic correlations of 0.414 and 0.353 after first and second calving, respectively), and a low and unfavorable effect on other fertility traits (genetic correlations between -0.281 and 0.295). Infertility after first calving caused a strong elongation of the lactation, and LL was negatively correlated with fertility of cows after second calving, so that LL can itself be regarded as a measure of fertility. Lactation milk yield depends on both pMY and LL, and, as such, is a cause and consequence of (in)fertility.

Genomic breeding value prediction using three Bayesian methods and application to reduced density marker panels

The objective of this study was to estimate repeatabilities and heritabilities for measures of pig behavior and their relationship with performance. Measures of behavior and performance included the backtest, resident-intruder test, human approach test (HAT), novel object test (NOT), d 1 BW, backfat depth (BF), loin muscle area (LMA), ADG in the farrowing house, ADG, 21-d BW, and 140-d BW (W). Each behavioral trait was measured twice. The study consisted of 95 litters from 31 sires with an average of 3 litters per sire (n >or= 457). Between 7 and 14 d of age, the backtest was conducted by placing each pig in a supine position for 60 s. Total time spent struggling (TTS) and total number of attempts to struggle (TAS) were recorded. The resident intruder test involved 2 nursery pigs, a resident pig and an unfamiliar intruder pig. The resident pen was divided in half by a solid partition. A resident pig was placed in the test area, and an intruder pig was then introduced. Latency until an attack occurred (LAT) and total number of attacks over 2 tests (RIS) were recorded. Amount of time taken for each finishing pig to make snout contact with an unfamiliar human or object was recorded. Dam and sire effects influenced all traits (P < 0.01). Sex and pen affected LAT, RIS, HAT, and NOT (P < 0.10). Repeatabilities of TTS, TAS, RIS, LAT, HAT, and NOT were 0.38, 0.21, 0.07, 0.08, 0.17, and 0.11, respectively. The phenotypic correlations of TTS with TAS and HAT with NOT were 0.61 and 0.34, respectively. Phenotypic correlation between RIS and LAT was -0.85. Total time spent struggling and TAS tended to be phenotypically correlated with 21-d BW and ADG in the farrowing house. Total attempts to struggle were phenotypically correlated with BF (0.15). Latency until an attack occurred was phenotypically correlated with LMA (0.23). Resident intruder score was phenotypically correlated with ADG (-0.13), W (-0.13), and LMA (-0.21) and estimated to be lowly heritable (h(2) = 0.12). Heritabilities of TTS and TAS were 0.31 and 0.53, respectively. Genetic correlation of TAS with ADG and W was 0.38. Genetic correlations of TTS with BF, W, and TAS were 0.14, 0.18, and 0.81, respectively. The backtest is a heritable and repeatable measure of a behavioral characteristic in pigs that is phenotypically and genetically correlated with performance.

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster

Clinical mastitis (CM) is one of the health disorders with large impacts on dairy farming profitability and animal welfare. The objective of this study was to perform a genome-wide association study (GWAS) for CM in first-lactation Holstein. Producer-recorded mastitis event information for 103,585 first-lactation cows were used, together with genotype information on 1,361 bulls from the Illumina BovineSNP50 BeadChip. Single-step genomic-BLUP methodology was used to incorporate genomic data into a threshold-liability model. Association analysis confirmed that CM follows a highly polygenic mode of inheritance. However, 10-adjacent-SNP windows showed that regions on chromosomes 2, 14 and 20 have impacts on genetic variation for CM. Some of the genes located on chromosome 14 (LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA) are part of the lymphocyte-antigen-6 complex (LY6) known for its neutrophil regulation function linked to the major histocompatibility complex. Other genes on chromosome 2 were also involved in regulating immune response (IFIH1, LY75, and DPP4), or are themselves regulated in the presence of specific pathogens (ITGB6, NR4A2). Other genes annotated on chromosome 20 are involved in mammary gland metabolism (GHR, OXCT1), antibody production and phagocytosis of bacterial cells (C6, C7, C9, C1QTNF3), tumor suppression (DAB2), involution of mammary epithelium (OSMR) and cytokine regulation (PRLR). DAVID enrichment analysis revealed 5 KEGG pathways. The JAK-STAT signaling pathway (cell proliferation and apoptosis) and the ‘Cytokine-cytokine receptor interaction’ (cytokine and interleukines response to infectious agents) are co-regulated and linked to the ‘ABC transporters’ pathway also found here. Gene network analysis performed using GeneMania revealed a co-expression network where 665 interactions existed among 145 of the genes reported above. Clinical mastitis is a complex trait and the different genes regulating immune response are known to be pathogen-specific. Despite the lack of information in this study, candidate QTL for CM were identified in the US Holstein population.