A boosting method to select the random effects in linear mixed models.

This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal.

Estimation of heritability with genomic information by method R.

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI-REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI-REML without genomics, AI-REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI-REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used.

Genetic parameters for yearling male reproduction traits in tropical composite cattle population.

Fertility is economically important but is hard to quantify and measure in breeding programs which has led extensive breeding programs to ignore fertility in their selection criteria. While female fertility traits have been extensively researched, male fertility traits have been largely ignored. It is estimated that 20% to 40% of bulls have sub-fertility, reducing the number of calves born and profits, highlighting the importance of investigating bull fertility. The most practical measure of male fertility is a bull breeding soundness evaluation (BBSE) which assesses structure as well as semen quality and quantity. Generally, traits recorded in a BBSE are neither genetically evaluated nor used for selection in breeding programs. All traits recorded during a BBSE were analyzed through a series of univariate and bivariate linear mixed models using a genomic relationship matrix to estimate genetic parameters. All genotype and phenotype data were obtained from a tropical composite commercial cattle population and imputed to 27,638 single-nucleotide polymorphisms (SNPs) with a total of 2,613 genotyped animals with BBSE records ranging from 616 to 826 animals depending on the trait. The heritabilities of the 27 traits recorded during a BBSE ranged from 0.02 to 0.49. Seven of the male fertility traits were recommended to be included in a breeding program based on their heritability and their phenotypic and genetic correlations. These traits are scrotal circumference, percent normal sperm, proximal droplets, distal midpiece reflex, knobbed acrosomes, vacuoles/teratoids, and sheath score. Using these seven traits in a breeding program would result in higher calving rates, increasing production and profitability.

One of the key profit drivers in any animal breeding program is fertility as it contributes directly to the progeny produced. Typically, fertility traits are hard to quantify and lowly heritable so they are often ignored in breeding programs. The inclusion of male fertility traits could allow for selection on heritable traits that are easy to measure and implement in a commercial breeding program. The utilization of male fertility traits could improve overall fertility and production. Bull breeding soundness evaluation traits were heritable, ranging from low to high, allowing for genetic improvement in those traits. Seven traits were recommended as selection criteria in a breeding program, which included two physical traits and five sperm traits. Implementing these seven traits in a breeding program would allow for a higher calving rate and associated increased profits.

In vivo alkaline comet assay: Statistical considerations on historical negative and positive control data.

The alkaline comet assay is frequently used as in vivo follow-up test within different regulatory environments to characterize the DNA-damaging potential of different test items. The corresponding OECD Test guideline 489 highlights the importance of statistical analyses and historical control data (HCD) but does not provide detailed procedures. Therefore, the working group "Statistics" of the German-speaking Society for Environmental Mutation Research (GUM) collected HCD from five laboratories and >200 comet assay studies and performed several statistical analyses. Key results included that (I) observed large inter-laboratory effects argue against the use of absolute quality thresholds, (II) > 50% zero values on a slide are considered problematic, due to their influence on slide or animal summary statistics, (III) the type of summarizing measure for single-cell data (e.g., median, arithmetic and geometric mean) may lead to extreme differences in resulting animal tail intensities and study outcome in the HCD. These summarizing values increase the reliability of analysis results by better meeting statistical model assumptions, but at the cost of information loss. Furthermore, the relation between negative and positive control groups in the data set was always satisfactorily (or sufficiently) based on ratio, difference and quantile analyses.

Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley.

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Despite their role in plant physiological regulation, a complete overview of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Using a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as to identify new genetic variants by single and multi-population QTL analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected new QTLs for flowering time and plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect, or low-frequency genetic variants underlie the control of these two traits.

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits.

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits.

Genetic relationships between reproductive and production traits in Jersey crossbred cattle.

The study aimed to estimate the genetic parameters of different reproductive traits namely age at first calving (AFC), calving interval (CI), days open (DO) and number of service per conception (NSPC) and their associations with productive traits including 305-day milk yield (305DMY), total lactation milk yield (TLMY) and lactation length (LL) of Jersey crossbred cattle maintained at Kalyani, Nadia, West Bengal, India. Genetic parameters of reproductive traits and their correlations with productive traits were estimated by Restricted Maximum Likelihood method and Bayesian approach. Using both analytical approaches, the estimates of heritability for AFC, CI, DO and NSPC ranged from 0.12 -0.15, 0.05-0.08, 0.08-0.09 and 0.04-0.06, respectively. Low proportion of variances associated with permanent environmental effect of animals (c2 effect) were detected for CI (0.08-0.10), DO (0.09-0.11) and NSPC (0.05-0.06) in both the methods. Repeatability measures for all the reproductive traits considered in this study were low to moderate in nature, which ranged from 0.09 to 0.17. Genetic correlations between different reproductive traits were positive and low (0.05) to high (0.98) in magnitude except AFC-NSPC. Low and negative genetic correlations of AFC with 305DMY and TLMY were favourable and indicated animals with high milk yield had early age of maturity. Positive genetic correlations between CI, DO and NSPC with all production traits implied the antagonism relationships among these traits, therefore in any breeding program for improvement of production traits via selection, the reproductive traits should be taken into account as well.

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits.

Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves.

Precision Livestock Farming technologies have increased the availability of on-farm data collected from dairy operations, such as automatic milk feeding machines. We analyzed feeding records from AMF to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 pre-weaned female Holstein calves were collected daily over a period of 6 years (3 years included per-visit data) and daily milk consumption (DMC) and per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit (DDPV), daily number of rewarded visits (DNRV), and total number of visits per day (TNV) were recorded over a 60-d pre-weaning period. Additional traits were derived from these variables, including total consumption and duration variance (TDC and TDV), feeding interval, drinking speed (DS), and pre-weaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step GBLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 single nucleotide polymorphisms after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models [0.006 ± 0.0009 to 0.08 ± 0.004]. However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d pre-weaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full data set (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that random regression models capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Partitioning variance in reproductive success, within years and across lifetimes.

Variance in reproductive success (sk2, with k = number of offspring) plays a large role in determining the rate of genetic drift and the scope within which selection acts. Various frameworks have been proposed to parse factors that contribute to sk2, but none has focused on age-specific values of ϕ=sk2/k¯, which indicate the degree to which reproductive skew is overdispersed (compared to the random Poisson expectation) among individuals of the same age and sex. Instead, within-age effects are generally lumped with residual variance and treated as "noise." Here, an ANOVA sums-of-squares framework is used to partition variance in annual and lifetime reproductive success into between-group and within-group components. For annual reproduction, the between-age effect depends on age-specific fecundity (b x), but relatively few empirical data are available on the within-age effect, which depends on ϕ x. By defining groups by age-at-death rather than age, the same ANOVA framework can be used to partition variance in lifetime reproductive success (LRS) into between-group and within-group components. Analytical methods are used to develop null-model expectations for random contributions to within-group and between-group components. For analysis of LRS, random variation in longevity appears as part of the between-group variance, and effects (if any) of skip breeding and persistent individual differences contribute to the within-group variance. Simulations are used to show that the methods for variance partitioning are asymptotically unbiased. Practical application is illustrated with empirical data for annual reproduction in American black bears and lifetime reproduction in Dutch great tits. Results show that overdispersed within-age variance (1) dominates annual sk2 in both male and female black bears, (2) is the primary factor that reduces annual effective size to a fraction of the number of adults, and (3) represents most of the opportunity for selection. In contrast, about a quarter of the variance in LRS in great tits can be attributed to random variation in longevity, and most of the rest is due to modest differences in fecundity with age estimated for a single cohort of females. R code is provided that reads generic input files for annual and lifetime reproductive success and allows users to conduct variance partitioning with their own data.

Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty.

Measurement uncertainty has long been a concern in the characterizing and interpreting environmental and toxicological measurements. We compared statistical analysis approaches when there are replicates: a Naïve approach that omits replicates, a Hybrid approach that inappropriately treats replicates as independent samples, and a Measurement Error Model (MEM) approach in a random effects analysis of variance (ANOVA) model that appropriately incorporates replicates. A simulation study assessed the effects of sample size and levels of replication, signal variance, and measurement error on estimates from the three statistical approaches. MEM results were superior overall with confidence intervals for the observed mean narrower on average than those from the Naïve approach, giving improved characterization. The MEM approach also featured an unparalleled advantage in estimating signal and measurement error variance separately, directly addressing measurement uncertainty. These MEM estimates were approximately unbiased on average with more replication and larger sample sizes. Case studies illustrated analyzing normally distributed arsenic and log-normally distributed chromium concentrations in tap water and calculating MEM confidence intervals for the true, latent signal mean and latent signal geometric mean (i.e., with measurement error removed). MEM estimates are valuable for study planning; we used simulation to compare various sample sizes and levels of replication.

Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake.

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Estimation of genetic parameters and genetic change of first parity reproductive traits in Alpine × Beetal goats.

The study of reproductive traits is crucial for improving genetic potential of goats because of their significant utility in meat production. Hence, genetic analysis was conducted for reproductive traits on Alpine × Beetal goats using animal model for first parity data. Information on 1462 reproductive records were collected over five decades from ICAR-National Dairy Research Institute, Karnal, Haryana (1971-2021). Single-trait and multi-trait animal models were used for genetic analysis. Estimates of (co)variance components and genetic parameters were obtained using Gibbs Sampler for Animal Model due to non-normal distribution of data. Six single-trait animal models (including or excluding maternal and environmental effects) were fitted and best models were determined based on Deviance Convergence Criterion values. The prolificacy for the A × B goats for first parity data was 32%, having 68% single births, 31% twins and 1% triplets/quadruplets. The least squares mean for age at first service (AFS), age at first kidding (AFK), service period (SP), dry period (DP), gestation length (GL), kidding interval (KI), litter weight (LW), number of kids born (NKB) and number of females kids born (NFKB) in first parity were 546.15 ± 4.10 days, 679.05 ± 4.07 days, 226.51 ± 4.02 days, 67.96 ± 2.76 days, 150.74 ± 0.13 days, 362.53 ± 3.35 days, 3.99 ± 0.04 kg, 1.32 ± 0.02 and 0.64 ± 0.02, respectively. The heritability estimates obtained from best model for AFS, AFK, GL, KI, SP, and DP were 0.12 ± 0.00, 0.10 ± 0.00, 0.09 ± 0.01, 0.03 ± 0.00, 0.04 ± 0.00, and 0.05 ± 0.00, respectively. For NKB, NFKB and LW, heritability estimates were 0.16 ± 0.01, 0.03 ± 0.03, and 0.04 ± 0.00, respectively. These results imply lower heritability estimates for reproductive traits, and hence meagre scope for selection for further improvement. Maternal effects contributed significantly for traits such as GL, NKB and NFKB. Genetic correlation for number of female kids born was negative with SP and DP which is favourable. Furthermore, genetic correlation was negative for dry period and litter weight which is favourable as number of kids born and litter weight are traits of direct economic importance. Results reveal high genetic potential of this breed for meat industry owing to high prolificacy, provided consistent efforts are made for the genetic improvement of this germplasm.

Genetic analysis of longevity and their associations with fertility traits in Holstein cattle.

The increase of longevity is intended to reduce involuntary culling rates, not extend the life span, and it reflects the ability of animals to successfully cope with the environment and disease during production. Sire model, animal model and repeatability animal models were used to estimate the (co) variance components of longevity and fertility traits. Six longevity and thirteen fertility traits were analysed, including herd life (HL), productive life (PL), number of days between first calving and the end of first lactation or culling (L1); number of days between first calving and the end of the second lactation or culling (L2); number of days between first calving and the end of the third lactation or culling (L3); number of days between first calving and the end of the fourth lactation or culling (L4); age at first service, age at first calving (AFC), the interval from first to last inseminations in heifer (IFLh), conception rate of first insemination in heifer, days open (DO), calving interval, gestation length, interval from calving to first insemination (ICF), interval from first to last inseminations in cow (IFLc), conception rate of first insemination in cow, calving ease (CE), birth weight, and calf survival. The estimated heritabilities (±SE) were 0.018 (±0.003), 0.015 (±0.003), 0.049 (±0.004), 0.025 (±0.003), 0.009 (±0.002) and 0.011 (±0.002) for HL, PL, L1, L2, L3 and L4, respectively. Strong correlations were appeared in HL and PL; the genetic and phenotypic correlation coefficients were 0.998 and 0.985, respectively. There were high genetic and phenotypic correlations which were observed in L1 and L2, L2 and L3, L3 and L4, respectively. All fertility traits of heifer showed medium to high heritability, while the cow showed low heritability. All heifer fertility traits had low genetic associations with longevity traits, ranging from -0.018 (L2 and IFLh) to 0.257 (L3 and AFC). Most of the fertility traits showed negative correlations with longevity traits in different parities, and we recommend DO, ICF, IFLc and CE as indirect indicators of longevity traits in dairy cows, but we also need to take into account the differences between parities.

Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks.

BACKGROUND: The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT: We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION: For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. CONCLUSION: VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.

Variance component estimates, phenotypic characterization, and genetic evaluation of bovine congestive heart failure in commercial feeder cattle.

The increasing incidence of bovine congestive heart failure (BCHF) in feedlot cattle poses a significant challenge to the beef industry from economic loss, reduced performance, and reduced animal welfare attributed to cardiac insufficiency. Changes to cardiac morphology as well as abnormal pulmonary arterial pressure (PAP) in cattle of mostly Angus ancestry have been recently characterized. However, congestive heart failure affecting cattle late in the feeding period has been an increasing problem and tools are needed for the industry to address the rate of mortality in the feedlot for multiple breeds. At harvest, a population of 32,763 commercial fed cattle were phenotyped for cardiac morphology with associated production data collected from feedlot processing to harvest at a single feedlot and packing plant in the Pacific Northwest. A sub-population of 5,001 individuals were selected for low-pass genotyping to estimate variance components and genetic correlations between heart score and the production traits observed during the feeding period. At harvest, the incidence of a heart score of 4 or 5 in this population was approximately 4.14%, indicating a significant proportion of feeder cattle are at risk of cardiac mortality before harvest. Heart scores were also significantly and positively correlated with the percentage Angus ancestry observed by genomic breed percentage analysis. The heritability of heart score measured as a binary (scores 1 and 2 = 0, scores 4 and 5 = 1) trait was 0.356 in this population, which indicates development of a selection tool to reduce the risk of congestive heart failure as an EPD (expected progeny difference) is feasible. Genetic correlations of heart score with growth traits and feed intake were moderate and positive (0.289-0.460). Genetic correlations between heart score and backfat and marbling score were -0.120 and -0.108, respectively. Significant genetic correlation to traits of high economic importance in existing selection indexes explain the increased rate of congestive heart failure observed over time. These results indicate potential to implement heart score observed at harvest as a phenotype under selection in genetic evaluation in order to reduce feedlot mortality due to cardiac insufficiency and improve overall cardiopulmonary health in feeder cattle.

Analysis of the Genetic Parameters for Dairy Linear Appraisal and Zoometric Traits: A Tool to Enhance the Applicability of Murciano-Granadina Goats Major Areas Evaluation System.

Selection for zoometrics defines individuals' productive longevity, endurance, enhanced productive abilities and consequently, their long-term profitability. When zoometric analysis is aimed at large highly selected populations or in those at different levels of selection, linear appraisal systems (LAS) provide a timely response. This study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimating genetic and phenotypic correlations among all traits, and determining whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43, and the accuracy of estimation has improved after decades, rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggests that negative selection against thicker bones and higher rear insertion heights indirectly results in the optimization of selection practices in the rest of the traits, especially those in the structure, capacity and mammary system major areas. The integration and implementation of the strategies proposed within the Murciano-Granadina breeding program maximizes selection opportunities and the sustainable international competitiveness of the Murciano-Granadina goat in the dairy goat breed panorama.

A fast linkage method for population GWAS cohorts with related individuals.

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats.

The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.