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PubMed2002

Genetic parameters and trends for lean growth rate and its comvonents in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs.

Records on 361,300 Yorkshire, 154,833 Duroc, 99,311 Hampshire, and 71,097 Landrace pigs collected between 1985 and April of 2000 in herds on the National Swine Registry Swine Testing and Genetic Evaluation System were analyzed. Animal model and REML procedures were used to estimate random effects of animal genetic, common litter, maternal genetic, and the covariances between animal and maternal for lean growth rate (LGR), days to 113.5 kg (DAYS), backfat adjusted to 113.5 kg (BF), and loin eye area adjusted to 113.5 kg (LEA). Fixed effects of contemporary group and sex were also in the statistical model. Based on the single-trait model, estimates of heritabilities were 0.44, 0.44, 0.46, and 0.39 for LGR; 0.35, 0.40, 0.44, and 0.40 for DAYS; 0.48, 0.48, 0.49, and 0.48 for BF; and 0.33, 0.32, 0.35, and 0.31 for LEA in the Yorkshire, Duroc, Hampshire, and Landrace breeds, respectively. Estimates of maternal genetic effects were low and ranged from 0.01 to 0.05 for all traits across breeds. Estimates of common litter effects ranged from 0.07 to 0.16. A bivariate analysis was used to estimate the genetic correlations between lean growth traits. Average genetic correlations over four breeds were -0.83, -0.37, 0.44, -0.07, 0.08, and -0.37 for LGR with DAYS, BF, and LEA, DAYS with BF and LEA, and BF with LEA, respectively. Average genetic trends were 2.35 g/yr, -0.40 d/yr, -0.39 mm/yr, and 0.37 cm2/yr for LGR, DAYS, BF, and LEA, respectively. Results indicate that selection based on LGR can improve leanness and growth rate simultaneously and can be a useful biological selection criterion.

Adipose Tissue↗

PubMed2002

Estimates of genetic parameters and genetic change for reproduction, weight, and wool characteristics of Columbia sheep.

Genetic parameters from both single-trait and bivariate analyses for prolificacy, weight and wool traits were estimated using REML with animal models for Columbia sheep from data collected from 1950 to 1998 at the U.S. Sheep Experiment Station (USSES), Dubois, ID. Breeding values from both single-trait and seven-trait analyses calculated using the parameters estimated from the single-trait and bivariate analyses were compared with respect to genetic trends. Number of observations were 31,401 for litter size at birth and litter size at weaning, 24,741 for birth weight, 23,903 for weaning weight, 29,572 for fleece weight and fleece grade, and 2,449 for staple length. Direct heritability estimates from single-trait analyses were 0.09 for litter size at birth, 0.06 for litter size at weaning, 0.27 for birth weight, 0.16 for weaning weight, 0.53 for fleece weight, 0.41 for fleece grade, and 0.55 for staple length. Estimate of direct genetic correlation between littersize at birth and weaning was 0.84 and between birth and weaning weights was 0.56. Estimate of genetic correlation between fleece weight and staple length was positive (0.55) but negative between fleece weight and fleece grade (-0.47) and between staple length and fleece grade (-0.70). Estimates of genetic correlations were positive but small between birth weight and litter size traits and moderate and positive between weaning weight and litter size traits. Fleece weight was lowly and negatively correlated with both litter size traits. Fleece grade was lowly and positively correlated with both litter size traits, while staple length was lowly and negatively correlated with the litter size traits. Estimates of correlations between weight traits and fleece weight were positive and low to moderate. Estimates of correlations between weight traits and fleece grade were negative and small. Estimates of correlations between staple length and birth weight (0.05) and weaning weight were small (-0.04). Estimated breeding values averaged by year of birth from both the single-trait and multiple-trait analyses for the prolificacy and weight traits increased over time, but were unchanged for the wool traits. Estimated changes in breeding values over time did not differ substantially for single-trait and multiple-trait analyses, except for traits highly correlated with another trait that was responding to selection.

Animals↗

PubMed1992

Genetic parameters of conformation traits, milk yield, and herd life in Holsteins.

Genetic parameters were estimated simultaneously for 5 herd-life traits, 15 conformation (type) traits, and milk yield measured in first lactation for 128,601 Holstein cows. Heritabilities of all traits were higher in registered than in grade cows. Genetic correlations of linear type traits with first lactation yield ranged from -.48 for udder depth to .54 for dairy form. Genetic correlations among milk yield and herd-life traits were all positive except for milk-corrected herd life in grade cows. Udder traits had largest absolute genetic correlations with herd-life traits, followed by body traits and feet and leg traits. Some traits associated with body size and foot angle differed between registered and grade cows. Estimates of genetic trends from obtained parameters revealed greatest progress for milk yield from single-trait selection but also the largest changes for some type traits and milk-corrected herd life in an undesirable direction. Relative milk to type ratios of between 2:1 and 3:1 yielded 90% of the gain in milk yield with no change or slight improvement in type traits and functional herd life. Selection for type traits associated with herd life appears to be warranted to improve days of functional herd life or to decrease involuntary culling of dairy cows.

Animals↗

PubMed1994

A genetic study of estrus symptoms at puberty and their relationship to growth and leanness in gilts.

Puberty traits of 740 Yorkshire gilts from a selection experiment were analyzed. Heritabilities were estimated to be .32 for pubertal age, .23 for length of proestrus, .16 for length of standing estrus, .29 for ability to show standing reflex, and .24 for intensity of vulvar symptoms. Genetic correlations between length of proestrus, intensity of vulvar symptoms, and ability to show standing reflex were positive. There seemed to be a negative genetic correlation between percentage of lean and intensity of vulvar symptoms. There were negative genetic correlations between growth rate and length of standing estrus, and between growth rate and ability to show standing reflex.

Animals↗

PubMed2006

Genotype by environment interaction for somatic cell score across bulk milk somatic cell count and days in milk.

The objective of this paper was to investigate the importance of a genotype x environment interaction (G x E) for somatic cell score (SCS) across levels of bulk milk somatic cell count (BMSCC), number of days in milk (DIM), and their interaction. Variance components were estimated with a model including random regressions for each sire on herd test-day BMSCC, DIM, and the interaction of BMSCC and DIM. The analyzed data set contained 344,029 test-day records of 24,125 cows, sired by 182 bulls, in 461 herds comprising 13,563 herd test-days. In early lactation, considerable G x E effects were detected for SCS, indicated by 3-fold higher genetic variance for SCS at high BMSCC compared with SCS at low BMSCC, and a genetic correlation of 0.72 between SCS at low and at high BMSCC. Estimated G x E effects were smaller during late lactation. Genetic correlations between SCS at the same level of BMSCC, across DIM, were between 0.43 and 0.89. The lowest genetic correlation between SCS measures on any 2 possible combinations of BMSCC and DIM was 0.42. Correlated responses in SCS across BMSCC and DIM were, on some occasions, less than half the direct response to selection in the response environment. Responses to selection were reasonably high among environments in the second half of the lactation, whereas responses to selection between environments early and late in lactation tended to be low. Selection for reduced SCS yielded the highest direct response early in lactation at high BMSCC.

Animals↗

PubMed1999

Bayesian analysis of birth weight and litter size in Baluchi sheep using Gibbs sampling.

Variance and covariance components for birth weight (BWT), as a lamb trait, and litter size measured on ewes in the first, second, and third parities (LS1 through LS3) were estimated using a Bayesian application of the Gibbs sampler. Data came from Baluchi sheep born between 1966 and 1989 at the Abbasabad sheep breeding station, located northeast of Mashhad, Iran. There were 10,406 records of BWT recorded for all ewe lambs and for ram lambs that later became sires or maternal grandsires. All lambs that later became dams had records of LS1 through LS3. Separate bivariate analyses were done for each combination of BWT and one of the three variables LS1 through LS3. The Gibbs sampler with data augmentation was used to draw samples from the marginal posterior distribution for sire, maternal grandsire, and residual variances and the covariance between the sire and maternal grandsire for BWT, variances for the sire and residual variances for the litter size traits, and the covariances between sire effects for different trait combinations, sire and maternal grandsire effects for different combinations of BWT and LS1 through LS3, and the residual covariations between traits. Although most of the densities of estimates were slightly skewed, they seemed to fit the normal distribution well, because the mean, mode, and median were similar. Direct and maternal heritabilities for BWT were relatively high with marginal posterior modes of .14 and .13, respectively. The average of the three direct-maternal genetic correlation estimates for BWT was low, .10, but had a high standard deviation. Heritability increased from LS1 to LS3 and was relatively high, .29 to .37. Direct genetic correlations between BWT and LS1 and between BWT and LS3 were negative, -.32 and -.43, respectively. Otherwise, the same correlation between BWT and LS2 was positive and low, .06. Genetic correlations between maternal effects for BWT and direct effects for LS1 through LS3 were all highly negative and consistent for all parities, circa -.75. Environmental correlations between BWT and LS1 through LS3 were relatively low and ranged from .18 to .29 and had high standard errors.

Animal Husbandry↗

PubMed1998

Genetic variation and prediction of additive and nonadditive genetic effects for six carcass traits in an Angus-Brahman multibreed herd.

Estimates of covariances and sire expected progeny differences of additive and nonadditive genetic effects for six carcass traits were obtained using records from 486 straightbred and crossbred steers from 121 sires born between 1989 and 1995 in the Angus-Brahman multibreed herd of the University of Florida. Steers were slaughtered at a similar carcass composition end point. Covariances were estimated by REML procedures, using a generalized expectation-maximization algorithm applied to multibreed populations. Straightbred and crossbred estimates of heritabilities and additive genetic correlations were within ranges found in the literature for steers slaughtered on an age- or weight-constant basis for hot carcass weight, longissimus muscle area, and shear force but equal to or less than the lower bound of these ranges for fat-related traits. Maximum values of interactibilities (i.e., ratios of nonadditive variances to phenotypic variances in the F1) and nonadditive genetic correlations were smaller than heritabilities and additive genetic correlations in straightbreds and crossbred groups. Sire additive and total direct genetic predictions for longissimus muscle area, marbling, and shear force tended to decrease with the fraction of Brahman alleles, whereas those for hot carcass weight and fat thickness over the longissimus were higher, and those for kidney fat were lower in straightbreds and F1 than in other crossbred groups. Nonadditive genetic predictions were similar across sire groups of all Angus and Brahman fractions. These results suggest that slaughtering steers on a similar carcass composition basis reduces variability of fat-related traits while retaining variability for non-fat-related traits comparable to slaughtering steers on a similar age or weight basis. Selection for carcass traits within desirable (narrow) ranges and slaughter of steers at similar compositional end point seems to be a good combination to help produce meat products of consistent quality.

Abattoirs↗

PubMed2026

Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records.

Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome-wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure ('generic') versus minimally opioid-exposed control ('exposed'). First, at the phenotypic level, we conducted phenome-wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD-related comorbidities were more pronounced when using the generic group, 83% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 (pgeneric = 8.83E-03 vs. pexposed = 1.83E-02) and genetic correlations with prior OUD GWAS (rg-generic = 0.83 ± 0.26 vs. rg-exposed = 0.78 ± 0.27). Although GWASs were limited by sample size (Ngeneric = 6269, Nexposed = 6365), compared to an independent OUD GWAS (N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.

Humans↗

PubMed2006

Using simulation models to predict feed intake: phenotypic and genetic relationships between observed and predicted values in cattle.

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) x CFRmcg [R2 = 0.44, residual SD (s(y.x)) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) x DFRmcg (R2 = 0.53, s(y.x) = 0.612 kg/d). Heritability of OFI was 0.27 +/- 0.12, and heritabilities ranged from 0.33 +/- 0.12 to 0.41 +/- 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P < 0.001 for all phenotypic correlations); and 0.95 +/- 0.07, 0.82 +/- 0.13, 0.89 +/- 0.09, 0.95 +/- 0.07, 0.91 +/- 0.09, 0.96 +/- 0.07, 0.89 +/- 0.09, 0.88 +/- 0.09, 0.96 +/- 0.06, and 0.96 +/- 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P < 0.001 for all phenotypic correlations), and 0.99 +/- 0.004, 0.98 +/- 0.017, 0.99 +/- 0.004, 0.99 +/- 0.005, and 0.97 +/- 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency.

Animal Feed↗

PubMed2002

Microsatellite instability in a population of sporadic colorectal cancers: correlation between genetic and pathological profiles.

BACKGROUND: Tumours with high-frequency microsatellite instability exhibit unique genotype and phenotype features, whereas the difference between low-frequency microsatellite instability and apparently stable tumours is far from being clear. AIMS: To identify distinctive genetic and pathological characteristics of low-frequency microsatellite instability tumours. METHODS: Microsatellite instability status of 57 sporadic colorectal cancers and its correlation with genetic, pathological and clinical features was analysed. RESULTS: High frequency microsatellite instability and low-frequency microsatellite instability and apparently stable cancers were different in terms of tumour localisation (p=0.015), frequency of APC mutations (p=0.012), occurrence of Crohn's-like/lymphoid reaction (p=0.0353) and morphological evidence of origin from an adenoma (p=0.0338). Specifically, in low-frequency microsatellite instability cancers, APC mutations were very frequent (76.9%, 10/13) and a Crohn's-like/lymphoid reaction was common (38.5%, 5/13). High-frequency microsatellite instability tumours were preferentially located in the right colon and exhibited a higher frequency of loss of heterozygosity at the FHIT locus compared with low-frequency microsatellite instability and apparently stable cases (p=0.0243). Dukes' stage (p=0.0021), tumour localisation (p=0.0410) and pattern of cancer growth (p=0.0374), were the only factors affecting patient survival. However, a borderline improvement was noted in overall survival in high-frequency microsatellite instability and low-frequency microsatellite instability cancer patients (p=0.062). CONCLUSIONS: These results indicate that low-frequency microsatellite instability tumours have different genetics and histological features and suggest that they are a distinct group of colorectal cancers.

Adult↗

PubMed2003

Estimates of genetic parameters and genetic change for reproduction, weight, and wool characteristics of Targhee sheep.

Genetic parameters from both single-trait and bivariate analyses for prolificacy, weight, and wool traits were estimated using REML with animal models for Targhee sheep from data collected from 1950 to 1998 at the U.S. Sheep Experiment Station, Dubois, ID. Breeding values from both single-trait and seven-trait analyses calculated with the parameters estimated from the single-trait and bivariate analyses were compared across years of birth with respect to genetic trends. The numbers of observations were 38,625 for litter size at birth and litter size at weaning, 33,994 for birth weight, 32,715 for weaning weight, 36,807 for fleece weight and fleece grade, and 3,341 for staple length. Direct heritability estimates from single-trait analyses were 0.10 for litter size at birth, 0.07 for litter size at weaning, 0.25 for birth weight, 0.22 for weaning weight, 0.54 for fleece weight, 0.41 for fleece grade, and 0.65 for staple length. Estimate of direct genetic correlation between litter size at birth and weaning was 0.77 and between birth and weaning weights was 0.52. The estimate of genetic correlation between fleece weight and staple length was positive (0.54), but was negative between fleece weight and fleece grade (-0.47) and between staple length and fleece grade (-0.69). Estimates of genetic correlations were near zero between birth weight and litter size traits and small and positive between weaning weight and litter size traits. Fleece weight was slightly and negatively correlated with both litter size traits. Fleece grade was slightly and positively correlated with both litter size traits. Estimates of correlations between staple length and litter size at birth (-0.14) and litter size at weaning (0.05) were small. Estimates of correlations between weight traits and fleece weight were positive and low to moderate. Estimates of correlations between weight traits and fleece grade were negative and small, whereas estimates between weight traits and staple length were positive and small. Estimated breeding values averaged by year of birth from both the single- and seven-trait analyses for the prolificacy and weight traits increased over time, whereas those for fleece weight decreased slightly and those for the other wool traits were unchanged. Estimated changes in breeding values over time did not differ substantially for the single-trait and seven-trait analyses, except for traits highly correlated with another trait that was responding to selection.

Animals↗

PubMed1998

Covariance functions across herd production levels for test day records on milk, fat, and protein yields.

Multiple-trait BLUP evaluations of test day records require a large number of genetic parameters. This study estimated covariances with a reduced model that included covariance functions in two dimensions (stage of lactation and herd production level) and all three yield traits. Records came from all six states in Australia, were evenly distributed across the herd production levels, but decreased with increasing lactation stage from 9693 records for the 1st mo of lactation to 4199 records for the 10th mo. Using the variance component estimation package and a bivariate animal model, 1176 genetic (co)variances and 312 environmental (co)variances were estimated for 48 traits (1, 4, 7, and 10 mo of lactation; herd production levels of < 20, 20 to 22, 22 to 24, > 24 kg of milk/d; and milk, fat, and protein yields). The genetic (co)variances could be predicted by a multiplicative model that included 1) a term dependent on which yields (milk, fat, or protein) were involved in the covariance, 2) the covariance functions for month of lactation and herd production level, and 3) a covariance function for the interaction between these. This model required only 27 parameters instead of the 1176 (co)variances. For the environmental (co)variances, a model was fitted that contained several additional covariance functions. This model reduced the number of parameters from 312 to 71. For the same trait at the same production level, genetic correlations between test days ranged from 0.59 to 1, and environmental correlations ranged from 0.17 to 0.48. Genetic correlations between milk and fat, milk and protein, and fat and protein were 0.38, 0.83, 0.59, respectively, and correlations between the herd production levels ranged from 0.79 to 0.97. Failure to consider herd production level in a test day model evaluation might result, for instance, in overweighting of early lactation information from high production herds compared with information coming from bulls tested across all production levels.

Animals↗

PubMed2003

Exposure to traumatic events and experiences: aetiological relationships with personality function.

Empirical research has shown that the odds of experiencing traumatic events are influenced by genetic factors and the heritability of trauma exposure varies with the type of trauma. Traumatic events per se are unlikely to be heritable; more likely to be inherited are factors such as personality that influence the person's risk for entering into, or creating, potentially hazardous situations. With data from 406 twin pairs (222 monozygotic and 184 dizygotic twin pairs) from the urban general population, the present study used multiple regression analysis to identify personality variables associated with exposure to trauma, and estimated the degree to which these relationships were mediated by genetic factors. The experience of violent assaultive traumatic events was predicted by antisocial personality traits, specifically juvenile antisocial behavior, self-harming behavior, Psychoticism (e.g. adult antisocial behavior and substance misuse), and being open to new ideas and experiences. Genetic factors were found to partially mediate these relationships as indexed by the genetic correlation coefficient. The values of the genetic correlations were statistically significant and ranged from 0.14 to 0.36, accounting for 5-11% of the observed correlation between personality and trauma exposure. These findings suggest that heritable personality characteristics explain part of the variance in the likelihood of exposure to some classes of traumatic events.

Adolescent↗

PubMed2025

Cross-Phenotype Genome-Wide Association Study on the Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis.

OBJECTIVE: An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype genome-wide association study (GWAS) meta-analysis. METHODS: We performed cross-phenotype GWAS meta-analysis and Bayesian colocalization analysis for patients with SSc and patients with PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, Bayesian colocalization analyses with expression quantitative trait loci and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies. Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci. RESULTS: We detected a strong genetic correlation between SSc and PBC (global genetic correlation = 0.84, P = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 nonhuman leukocyte antigens loci that reached genome-wide significance (P < 5 × 10-8). Evidence of shared causal variants between patients with SSc and patients with PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function. CONCLUSION: Our study supports a strong shared genetic susceptibility between SSc and PBC. Using cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Humans↗

PubMed1995

Estimates using an animal model of (co)variances for yields of milk, fat, and protein for the first lactation of Holstein cows in California and New York.

First lactation yields of milk, fat, and protein from Holstein cows in New York and California were used to obtain REML estimates of (co)variances for yield traits using a multitrait animal model. Data from each state were split randomly into 10 samples, averaging 5504 cows per sample from California and 5078 from New York. Mean heritability estimates for milk, fat, and protein yields were .30 +/- .02, .31 +/- .01, and .29 +/- .01 for California data and .33 +/- .01, .35 +/- .01, and .30 +/- .01 for New York data. Averages of genetic correlation estimates for California and New York were .63 +/- .01 and .52 +/- .02 between milk and fat, .84 +/- .01 and .83 +/- .01 between milk and protein, and .73 +/- .01 and .68 +/- .01 between fat and protein. Estimates of environmental correlations were larger than estimates of genetic correlations. Mean estimates of phenotypic correlations for California and New York were .75 +/- .01 and .72 +/- .01 between milk and fat, .92 +/- .01 and .91 +/- .01 between milk and protein, and .81 +/- .01 and .79 +/- .01 between fat and protein yields. On average, these estimates agree with those obtained from animal models with limited rounds of iteration for small data files.

Animals↗

PubMed2003

Estimation of genetic parameters for concentrations of milk urea nitrogen.

The objective of this study was to use field data collected by dairy herd improvement programs to estimate genetic parameters for concentrations of milk urea nitrogen (MUN). Edited data were 36,074 test-day records of MUN and yields of milk, fat, and protein obtained from 6102 cows in Holstein herds in Ontario, Canada. Data were divided into three sets, for the first three lactations. Two analyses were performed on data from each lactation. The first procedure used ANOVA to estimate the significance of the effects of several environmental factors on MUN. Herd-test-day effects had the most significant impact on MUN. Effects of stage of lactation were also important, and MUN levels tended to increase from the time of peak yield until the end of lactation. The second analysis used a random regression model to estimate heritabilities and genetic correlations of MUN and the yield traits. Heritability estimates for MUN in lactations one, two, and three were 0.44, 0.59, and 0.48, respectively. Heritabilities for the yield traits were of a similar magnitude. Little relationship was observed between MUN and yield. Raw phenotypic correlations were all <0.10 (absolute value). Genetic correlations with production traits were close to zero in lactations one and three and only slightly positive in lactation two. The results indicate that selection on MUN is possible, but relationships between MUN and other economically important traits such as metabolic disease and fertility are needed.

Animals↗

PubMed2024

Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability.

Genomic correlates of impulsivity have been identified in several genome-wide association studies (GWAS) using cross-sectional designs, but no studies have investigated the molecular genetic correlates of impulsivity phenotypes using longitudinally constructed traits. In 3860 unrelated European participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), we constructed longitudinal phenotypes for delay discounting and impulsive personality traits (as measured by the UPPS-P impulsive behavior scales) via assessment at ages 24, 26, and 28. We conducted GWASs of impulsivity using both cross-sectional and longitudinal phenotypes, estimated heritability and their phenotypic and genetic correlations, and evaluated their association with recently-developed polygenic risk scores (PRSs) for the impulsivity indicators themselves and also related psychiatric conditions. Latent growth curve modeling revealed a stable intercept over time for all impulsivity phenotypes. High genetic correlation of cross-sectional measures over time suggested a stable genetic component for delay discounting (rg = 0.53-0.99) and sensation seeking (rg = 0.99). Heritability estimates of the stable longitudinal phenotypes substantively improved as compared to their cross-sectional counterparts, revealing a significant SNP-heritability for delay discounting (0.22; p = 0.03) and sensation seeking (0.35; p = 0.0007). Consistent with previous reports, GWAS and gene-based analyses revealed associations between specific longitudinal impulsivity indicators and CADM2 and NCAM1 genes. The PRSs for the impulsivity indicators and disorders related to self-regulation were also significantly associated with longitudinal impulsivity traits. Finally, we validated the associations between longitudinal impulsivity phenotypes and their PRSs in an independent 13-wave longitudinal study (n = 1019) and the benefit of longitudinal phenotypes in simulation studies. In this first longitudinal genetic study of impulsivity traits, the results revealed stable genomic correlates of delay discounting and sensation seeking over time and further validated the utility of recently-developed PRSs, both in relation to the observed traits and in connecting them to psychiatric disorders. More generally, these findings support using latent intercepts as novel longitudinal phenotypes to boost signal for heritability and genomic correlates of mechanisms contributing to psychiatric disease liability.

Humans↗

PubMed2007

Genetic (co)variance components for ewe productivity traits in Katahdin sheep.

The objective of this study was to estimate genetic parameters, in Katahdin sheep, for total weight of litter weaned per ewe lambing (TW) and its components, number of lambs born (NB), number of lambs weaned (NW), and average weight of lambs weaned (AW) measured as traits of the ewe. Weaning weights of lambs (WW) were adjusted to 60 d of age and for effects of ewe age, lamb sex, and type of birth and rearing and averaged over all lambs in the litter to obtain AW. The 60-d age-adjusted WW were adjusted for ewe age and lamb sex and summed over all lambs in the litter to obtain TW. A total of 2,995 NB and NW records, 2,622 AW, and 2,714 TW records were available from 1,549 ewes (progeny of 235 sires) over 4 yr. Heritabilities were initially estimated for each trait from univariate REML analyses. Estimates of genetic correlations were obtained from bi- and trivariate analyses. Models for NB, NW, AW, and TW included random ewe additive and permanent environmental effects. A random service sire effect was also fit for AW and TW. Heritabilities of TW, NB, NW, and AW from univariate analyses were 0.12, 0.12, 0.09, and 0.13 (all P < 0.01), respectively. Permanent environmental effects were significant (P < 0.01) for TW and AW. Genetic correlations of TW with NB, NW, and AW ranged from 0.27 to 0.33, 0.88 to 0.91, and 0.72 to 0.76, respectively; those of NB with NW and AW ranged from 0.70 to 0.75 and -0.01 to 0.02, respectively; and that between NW and AW ranged from 0.40 to 0.55. Genetic parameters were also obtained for lamb survival to weaning (LS) and WW measured as traits of the lamb, and the relationships between WW of the ewe as a lamb and her subsequent records for NB and NW were also estimated. A total of 5,107 LS and 5,444 WW records were available. Models for WW and LS included random animal and maternal genetic, maternal permanent environmental, and litter effects. Heritability of WW ranged from 0.15 to 0.20. There was no evidence of genetic effects on LS. Direct genetic correlations of WW with NB and NW were not significantly different from zero. The correlation between maternal genetic effects on WW, and animal genetic effects on NW, averaged 0.35. Results of this study indicate that there are no major antagonisms among TW and its components, so that selection for TW would not have adverse effects on any component traits and vice versa. Maternally superior ewes for WW appear to also be somewhat superior for NW.

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