Estimates Of Genetic Correlations Research Articles

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigs

BackgroundWith the introduction of digital phenotyping and high-throughput data, traits that were previously difficult or impossible to measure directly have become easily accessible, offering the opportunity to enhance the efficiency and rate of genetic gain in animal production. It is of interest to assess how behavioral traits are indirectly related to the production traits during the performance testing period. The aim of this study was to assess the quality of behavior data extracted from day-wise video recordings and estimate the genetic parameters of behavior traits and their phenotypic and genetic correlations with production traits in pigs. Behavior was recorded for 70 days after on-test at about 10 weeks of age and ended at off-test for 2008 female purebred pigs, totaling 119,812 day-wise records. Behavior traits included time spent eating, drinking, laterally lying, sternally lying, sitting, standing, and meters of distance traveled. A quality control procedure was created for algorithm training and adjustment, standardizing recording hours, removing culled animals, and filtering unrealistic records.ResultsProduction traits included average daily gain (ADG), back fat thickness (BF), and loin depth (LD). Single-trait linear models were used to estimate heritabilities of the behavior traits and two-trait linear models were used to estimate genetic correlations between behavior and production traits. The results indicated that all behavior traits are heritable, with heritability estimates ranging from 0.19 to 0.57, and showed low-to-moderate phenotypic and genetic correlations with production traits. Two-trait linear models were also used to compare traits at different intervals of the recording period. To analyze the redundancies in behavior data during the recording period, the averages of various recording time intervals for the behavior and production traits were compared. Overall, the average of the 55- to 68-day recording interval had the strongest phenotypic and genetic correlation estimates with the production traits.ConclusionsDigital phenotyping is a new and low-cost method to record behavior phenotypes, but thorough data cleaning procedures are needed. Evaluating behavioral traits at different time intervals offers a deeper insight into their changes throughout the growth periods and their relationship with production traits, which may be recorded at a less frequent basis.

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults.

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

Milk urea nitrogen is genetically associated with production and reproduction performance of dairy cows: A meta-analysis

This study aimed to conduct a meta-analysis using the random-effects model to integrate published estimates of genetic parameters for milk urea (MU) and milk urea nitrogen (MUN). The study used a total of 49 heritability and 147 genetic correlation estimates from 30 papers published between 2003 and 2023. The heritability estimates for MU and MUN were low and equal to 0.202 and 0.181, respectively. The genetic correlation estimates between MU and production traits were low and ranged from -0.039 (between MU and milk lactose percentage) to 0.102 (between MU and milk protein yield), but the genetic correlation estimates between MUN and production traits were low and varied from -0.070 (between MUN and somatic cell score) to 0.357 (between MUN and milk protein yield). The genetic correlation estimates between MUN and calving to first service, days open, and first service conception rate were low and equal to 0.093, 0.329, and 0.069, respectively. The results of present study showed that available additive genetic variation in MU and MUN could potentially be exploited in dairy cow genetic selection schemes. Although the correlations of MU and MUN with production and reproduction traits were low, there were some cases where they were unfavorable, and they should not be neglected when planning selection programs.

Estimation of genetic parameters for the implementation of selective breeding in commercial insect production

BackgroundThere is a burgeoning interest in using insects as a sustainable source of food and feed, particularly by capitalising on various waste materials and by-products that are typically considered of low value. Enhancing the commercial production of insects can be achieved through two main approaches: optimising environmental conditions and implementing selective breeding strategies. In order to successfully target desirable traits through selective breeding, having a thorough understanding of the genetic parameters pertaining to those traits is essential. In this study, a full-sib half-sib mating design was used to estimate variance components and heritabilities for larval size and survival at day seven of development, development time and survival from egg to adult, and to estimate correlations between these traits, within an outbred population of house flies (Musca domestica), using high-throughput phenotyping for data collection.ResultsThe results revealed low to intermediate heritabilities and positive genetic correlations between all traits except development time and survival to day seven of development and from egg to adulthood. Surprisingly, larval size at day seven exhibited a comparatively low heritability (0.10) in contrast to development time (0.25), a trait that is believed to have a stronger association with overall fitness. A decline in family numbers resulting from low mating success and high overall mortality reduced the amount of available data which resulted in large standard errors for the estimated parameters. Environmental factors made a substantial contribution to the phenotypic variation, which was overall high for all traits.ConclusionsThere is potential for genetic improvement in all studied traits and estimates of genetic correlations indicate a partly shared genetic architecture among the traits. All estimates have large standard errors. Implementing high-throughput phenotyping is imperative for the estimation of genetic parameters in fast developing insects, and facilitates age synchronisation, which is vital in a breeding population. In spite of endeavours to minimise non-genetic sources of variation, all traits demonstrated substantial influences from environmental components. This emphasises the necessity of thorough attention to the experimental design before breeding is initiated in insect populations.

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle.

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.

Heritability and genetic correlations of rumination time with milk-yield and milking traits in Holstein-Friesian cows using an automated milking system

Knowledge of the values of genetic parameters is a prerequisite for conducting a breeding program. This is especially important for rumination, which is considered an indicator of cow’s health. Exploring the genetic relations between rumination time, milk yield, and milking traits could make it a valuable tool in dairy cattle breeding strategies. The objective of the research was to estimate heritability, repeatability, and genetic and phenotypic correlations of rumination time (RT), as well as traits associated with milk yield and milking of dairy cows of the Polish Holstein-Friesian breed kept in herds equipped with an automatic milking system. The research takes into consideration daily results for milking in the first lactation and second lactation, from 1 486 cows of the breed milked between 2013 and 2015 year. Cows were housed in 24 free-stall barns and fed a Partial Mixed Ration feed. The barns had an automated milking system (Astronaut A4 – Lely Industry). The cows received a varied dose of the concentrate, either in the milking robot or the feeding station, depending on the level of their milk yield. Our research has shown that RT was a low heritable trait (0.140 ± 0.039) and had a medium repeatability (0.572 ± 0.007). We detected a positive genetic correlation between RT and milk yield (0.341); however, a statistically significant negative relationship was identified between RT and urea content (−0.418) in milk. Estimations of genetic correlations suggest that selecting for higher RT may correspond to reduced urea content in milk. Investigating the genetics aspect of RT and the relationship with milk yield and milking traits may turn this into one of the useful criterion selections for dairy cattle breeding strategies, but should be used carefully. Further analyses on larger data sets and different populations are necessary.

Warm‐season turfgrass species genotype‐by‐environment interaction for turfgrass quality under drought

AbstractOne of the biggest challenges the turfgrass industry is currently facing is limitations of available water for irrigation of turfgrass areas. Efforts on breeding for drought resistance have increased over the past several years across the United States. Thus, the objectives of this study were to evaluate the performance of bermudagrass (Cynodon spp. Rich.), St. Augustinegrass (Stenotaphrum secundatum (Walter) Kuntze), seashore paspalum (Paspalum vaginatum Swartz) and zoysiagrass (Zoysia spp. Willd.) breeding lines from five different breeding programs under drought and estimate genetic parameters in order to increase selection efficiency for drought resistance improvement in these breeding programs. The germplasm sources were bermudagrass from Oklahoma State University and University of Georgia (UGA); St. Augustinegrass from North Carolina State University, Texas A&M University System (TAMUS) and University of Florida (UF); zoysiagrass from UF and TAMUS; seashore paspalum from UGA. Field trials were conducted from 2016 to 2019 at research facilities in Citra, FL and Dallas, TX. The response variables evaluated were per cent living ground cover (%GC), and turfgrass quality under normal or non‐drought (TQND) and drought conditions (TQD). The genetic variance was significant for TQND and TQD in bermudagrass, TQD in St. Augustinegrass and all traits in zoysiagrass. The heritability estimates were higher for TQD than for TQND in bermudagrass and St. Augustinegrass. Genetic correlation estimates showed that indirect selection can be effective to select drought‐resistant genotypes. Several genotypes performed better than all commercial cultivars in both St. Augustinegrass and zoysiagrass.

Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis.

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, αs1 -casein, αs2 -casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between αs1 -casein and αs2 -casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Evolutionary relationships between metabolism and behaviour require genetic correlations.

As selection acts on multivariate phenotypes, the evolution of traits within populations not only depends on the genetic basis of each trait, but also on the genetic relationships among traits. As metabolic rate is often related to vital traits such as growth, physiology and behaviour, its variation and evolution is expected to have important repercussions on individual fitness. However, the majority of the correlations between metabolic rate and other traits has been based on phenotypic correlations, while genetic correlations, basis for indirect selection and evolution, have been overlooked. Using a case study, we explore the importance of properly estimating genetic correlations to understand and predict evolution of multivariate phenotypes. We show that selection on metabolic traits could result in indirect selection mainly on growth-related traits, owing to strong genetic correlations, but not on swimming or risk-taking and sociability behaviour even if they covary phenotypically. While phenotypic correlation can inform about genetic correlation direction, caution is needed in predicting the magnitude of genetic correlation. Therefore, even though phenotypic correlations among physiological and behavioural traits could be useful, deriving evolutionary conclusions based purely on them is not robust. In short, proper estimation of genetic correlations is needed when predicting evolutionary consequences. This article is part of the theme issue 'The evolutionary significance of variation in metabolic rates'.

Efeito da idade a primeira concepção como critério de seleção em características de crescimento e carcaça em bovinos Nelore

Abstract The aim was to identify predictive traits of relatively easy measurement and which can be recorded early in life (age at first conception - AFCo), besides estimating its genetic correlations with growth and carcass traits in Nellore cattle. Age at first conception was considered the age at which the female had the first positive diagnosis for pregnancy. The estimation of (co)variance components and genetic parameters was performed using a linear animal model in two-trait analysis. The estimates of heritability were moderate, enabling genetic selection for growth-, carcass-, and sexual precocityrelated traits. The genetic correlation obtained between AFCo and age at first calving (AFCa) was high (0.88), indicating the feasibility of using AFCo as a selection criterion for early calving heifers. Genetic correlation estimates between AFCo and AFCa with weight at 120, 210, 365, and 450 days of age and carcass traits were moderate and negative (-0.33 to -0.62). Thus, genetic selection for animals with early AFCo and AFCa would enhance carcass yield, fat deposition, and growth performance, despite not affecting birth weight or daily weight gain. The results of this study encourage the use of AFCo in Nellore cattle since this trait displayed enough genetic variability in Nellore cattle, and can be used as selection criteria to improve sexual precocity. When the objective of genetic selection is to increase heifer sexual precocity, we could use the first trait as a criterion, as the measurement of this trait occurs at a lower AFCa.

Estimation of Heritability and Genetic Correlation of Quantitative Traits as a Basis for Selection Program of Indonesian Native Chickens

Estimation of Heritability and Genetic Correlation of Quantitative Traits as a Basis for Selection Program of Indonesian Native Chickens

Longitudinal genetic dynamics of weaning index and implications for cow-calf production efficiency

In beef cattle, the selection for higher weights at young ages has been questioned with the argument that this criterion may increase the adult weight of cows, resulting in higher costs. Therefore, selection criteria should be employed to increase weights at young ages with minimal impact on the adult weight of cows. Additionally, the relationship between measures of cow production efficiency and other well-established selection criteria in breeding programs remains poorly understood. The objective of this study was to longitudinally evaluate the relationship between the weaning index (WIndex) as a measure of efficiency and growth traits of the cows. Possible changes over time in WIndex due to selection applied for yearling weight (YW) were also investigated. The WIndex was proposed to maximize genetic response in the weaning weight of the calf while maintaining genetic gain in BW of the cow at zero. A random regression model was adopted to estimate correlations between WIndex, BW, hip height (HH), and body condition score (BCS) using records of Nelore cows from three lines. Genetic trends were calculated for the control line (NeC) and lines selected for greater YW (NeS and NeT). The age of 3 years was the most critical for the weaning efficiency of the cows. At this stage, young cows are still growing and wean lighter calves than their adult counterparts. The genetic correlation estimates between WIndex and BW (−0.58 to 0.04), HH (−0.05 to −0.34), and BCS (−0.51 to −0.17) were close to zero or negative. BW and HH were strongly correlated genetically across all ages (0.73–0.76). In general, HH exhibited a weak and negative genetic relationship with BCS. The genetic correlation between BW and BCS was stronger for advanced ages (0.45–0.68). In lines selected for YW, important increases in WIndex were observed. However, NeS has been selected since the 1980s until the present for YW, and thus, it showed a more pronounced trend of increasing BW and, consequently, a more modest trend of increasing WIndex compared to NeT. In contrast, WIndex exhibited a trend close to zero for NeC. In this context, monitoring HH and BCS can be useful to avoid losses in the weaning efficiency of cows. Furthermore, we suggest that one way to mitigate efficiency losses in calf production could involve stabilizing the BW of cows and increasing the weaning weight of calves using the WIndex.

Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression models

BackgroundAutomatic and continuous recording of vaginal temperature (TV) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of TV in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for TV in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded TV.ResultsHeritability estimates for TV ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, − 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ˚C). Repeatability estimates of TV over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. TV measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording TV for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370–59.998 Mb) and16 (21.548–21.966 Mb) were identified. These regions harbor the genes CDC123, CAMK1d, SEC61A2, and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for TV. For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for TV, respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production.ConclusionsTV is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for TV exist, indicating potential re-ranking of selection candidates across EG. TV is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms.

A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment

BackgroundThrombolytic recombinant tissue plasminogen activator (r-tPA) treatment is the only pharmacologic intervention available in the ischemic stroke acute phase. This treatment is associated with an increased risk of intracerebral hemorrhages, known as hemorrhagic transformations (HTs), which worsen the patient’s prognosis. ObjectivesTo investigate the association between genetically determined natural hemostatic factors’ levels and increased risk of HT after r-tPA treatment. MethodsUsing data from genome-wide association studies on the risk of HT after r-tPA treatment and data on 7 hemostatic factors (factor [F]VII, FVIII, von Willebrand factor [VWF], FXI, fibrinogen, plasminogen activator inhibitor-1, and tissue plasminogen activator), we performed local and global genetic correlation estimation multitrait analyses and colocalization and 2-sample Mendelian randomization analyses between hemostatic factors and HT. ResultsLocal correlations identified a genomic region on chromosome 16 with shared covariance: fibrinogen-HT, P = 2.45 × 10−11. Multitrait analysis between fibrinogen-HT revealed 3 loci that simultaneously regulate circulating levels of fibrinogen and risk of HT: rs56026866 (PLXND1), P = 8.80 × 10−10; rs1421067 (CHD9), P = 1.81 × 10−14; and rs34780449, near ROBO1 gene, P = 1.64 × 10−8. Multitrait analysis between VWF-HT showed a novel common association regulating VWF and risk of HT after r-tPA at rs10942300 (ZNF366), P = 1.81 × 10−14. Mendelian randomization analysis did not find significant causal associations, although a nominal association was observed for FXI-HT (inverse-variance weighted estimate [SE], 0.07 [−0.29 to 0.00]; odds ratio, 0.87; 95% CI, 0.75-1.00; raw P = .05). ConclusionWe identified 4 shared loci between hemostatic factors and HT after r-tPA treatment, suggesting common regulatory mechanisms between fibrinogen and VWF levels and HT. Further research to determine a possible mediating effect of fibrinogen on HT risk is needed.

Genotype-by-environment interactions for feed efficiency traits in Nellore cattle based on bi-trait reaction norm models

BackgroundSelecting animals for feed efficiency directly impacts the profitability of the beef cattle industry, which contributes to minimizing the environmental footprint of beef production. Genetic and environmental factors influence animal feed efficiency, leading to phenotypic variability when exposed to different environmental conditions (i.e., temperature and nutritional level). Thus, our aim was to assess potential genotype-by-environment (G × E) interactions for dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle (Bos taurus indicus) based on bi-trait reaction norm models (RN) and evaluate the genetic association between RFI and DMI across different environmental gradient (EG) levels. For this, we used phenotypic information on 12,958 animals (young bulls and heifers) for DMI and RFI recorded during 158 feed efficiency trials.ResultsThe heritability estimates for DMI and RFI across EG ranged from 0.26 to 0.54 and from 0.07 to 0.41, respectively. The average genetic correlations (± standard deviation) across EG for DMI and RFI were 0.83 ± 0.19 and 0.81 ± 0.21, respectively, with the lowest genetic correlation estimates observed between extreme EG levels (low vs. high) i.e. 0.22 for RFI and 0.26 for DMI, indicating the presence of G × E interactions. The genetic correlation between RFI and DMI across EG levels decreased as the EG became more favorable and ranged from 0.79 (lowest EG) to 0.52 (highest EG). Based on the estimated breeding values from extreme EG levels (low vs. high), we observed a moderate Spearman correlation of 0.61 (RFI) and 0.55 (DMI) and a selection coincidence of 53.3% and 40.0% for RFI and DMI, respectively.ConclusionsOur results show evidence of G × E interactions on feed efficiency traits in Nellore cattle, especially in feeding trials with an average daily gain (ADG) that is far from the expected of 1 kg/day, thus increasing reranking of animals.

Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience

BackgroundDisease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3’mRNA sequencing and genotype data were from a 650 K genotyping array.ResultsHeritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding.ConclusionsThese results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.

Genetic parameters, reciprocal cross differences, and age-related heterosis of egg-laying performance in chickens

BackgroundEgg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average.ResultsEstimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn (3.5 eggs for White Leghorn, 3.8 and 3.9 eggs for crossbreds). Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age.ConclusionsThe heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.

A comprehensive meta-analysis of genetic parameters for resilience and productivity indicator traits in Holstein cattle

Selection for resilience indicator (RIND) traits in Holstein cattle is becoming an important breeding objective as the worldwide population is expected to be exposed to increased environmental stressors due to both climate change and changing industry standards. However, genetic correlations between RIND and productivity indicator (PIND) traits, which are already being selected for and have the most economic value, are often unfavorable. As a result, it is necessary to fully understand these genetic relationships when incorporating novel traits into selection indices, so that informed decisions can be made to fully optimize selection for both groups of traits. In the past 2 decades, there have been many estimates of RINDs published in the literature, albeit in small populations. To provide valuable pooled summary estimates, a random-effects meta-analysis was conducted for heritability and genetic correlation estimates for PIND and RIND traits in worldwide Holstein cattle. In total, 926 heritability estimates for 9 PIND and 27 RIND traits, along with 362 estimates of genetic correlation (PIND x RIND traits) were collected. Resilience indicator traits were grouped into the following sub-groups: Metabolic Diseases, Hoof Health, Udder Health, Fertility, Heat Tolerance, and Other. Pooled estimates of heritability for PIND traits ranged from 0.201 ± 0.05 (energy corrected milk) to 0.377 ± 0.06 (protein content) while pooled estimates of heritability for RIND traits ranged from 0.032 ± 0.02 (incidence of lameness, incidence of milk fever) to 0.497 ± 0.05 (measures of body weight). Pooled estimates of genetic correlations ranged from -0.360 ± 0.25 (protein content vs. milk acetone concentration) to 0.535 ± 0.72 (measures of fat-to-protein ratio vs. milk acetone concentration). Additionally, out of 243 potential genetic correlations between PIND and RIND traits that could have been reported, only 40 had enough published estimates to implement the meta-analysis model. Our results confirmed that the interactions between PIND and RIND traits are complex, and all relationships should be evaluated when incorporating novel traits into selection indices. This study provides a valuable reference for breeders looking to incorporate RIND traits for Holstein cattle into selection indices.

Pervasive biases in GWAS using family history of Alzheimer’s disease as proxy phenotypes

AbstractBackgroundUsing family health history as proxy phenotype has become a common approach in genome‐wide association studies (GWAS) of Alzheimer’s disease (AD). This approach leverages large samples in population biobanks where most mid‐aged participants do not have any AD diagnosis. However, methodological issues in GWAS‐by‐proxy (GWAX) and quality of its association results have not been carefully investigated.MethodWe performed GWAX on parental AD history in UK Biobank (N = 48,031 proxy cases and 315,286 controls). We then applied GWAS‐by‐subtraction to obtain the non‐AD component underlying AD GWAX. We computed genetic correlations of AD case‐control GWAS, AD GWAX, and the non‐AD component underlying GWAX with a suite of complex traits. We also explored the impact of various quality control procedures and additional covariates in AD GWAX analysis.ResultAD GWAS and GWAX showed drastically different genetic correlations with several other complex traits especially cognitive abilities (Figure 1). AD GWAX had a surprising genetic correlation with higher education (rg = 0.167, p = 1.7e‐11) as opposed to the negative AD‐education correlation observed for GWAS (rg = ‐0.133, p = 2.4e‐5). The positive genetic correlation between AD and cognition is explained by the non‐AD component in AD GWAX (rg = 0.260 with education, p = 5.2e‐11). The non‐AD component in GWAX also showed correlations with lower risks of health outcomes such as coronary artery disease (rg = ‐0.136, p = 2.8e‐3) and diabetes (rg = ‐0.206, p = 2.8e‐3), indicating substantial survival bias in samples reporting parental AD history. Adjusting for parental age in GWAX substantially reduced but did not fully remove the genetic correlation of AD and higher education (rg = 0.013, p = 0.67), hinting at additional unaccounted factors in the current GWAX practice. We also provide evidence that genetic correlation between family health history awareness and education (rg = 0.285, p = 1.4e‐10) leads to these biases in GWAX associations.ConclusionGWAX based on parental health history introduces substantial and systematic biases in AD genetic associations due to the non‐representativeness of the biobank health history survey. Naively combining GWAX with regular case‐control GWAS will lead to misleading results both at the single variant level and in genome‐wide analyses such as genetic correlation estimation and polygenic risk score applications.