Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies. Genome-wide association studies (GWAS) aim to identify genetic variants  that are associated with a phenotype of interest. GWAS have been highly successful at identifying genetic variants influencing a large number of traits, with nearly 5,000 publications and more than 250,000 variant-phenotype associations included in the GWAS Catalog . Not onlHowever, focusing only on participants of European descent, a characteristic of many published studies, restricts extrapolation to those of non-European ancestry  and limOne of the challenges of performing association tests on genomic data is that demographic history influences the genomic structure of the population being analysed. If this is not properly controlled for, any genotype-phenotype association found in the study may be a consequence of this structure, rather than genuine trait association. The source of this potential bias is known as population stratification, where different trait distributions within genetically distinct subpopulations will result in those markers associated with the ancestry of the subpopulation to be also apparently associated with the trait. As an illustrative example, Depending on the populations being studied, analysis may not be as simple as identifying subpopulations in the samples, since each individual may be descended from multiple subpopulations tracing back to a mixture event (or admix event) between them. One of the ways in which we can express this mixing in an individual is as a function of ancestral populations; that is, populations that have been isolated from each other in the past . If the combination of these ancestral populations has been recent, then we expect to observe longer LD tracts; but these will decay over time , thus adIn order to provide an understanding of the methods used for the analysis of admixed populations, we will first review the steps involved in performing a GWAS. Secondly, we will discuss some of the methods used in recent years to study admixed populations, and the way in which each methodology has been applied. Here, we will both explain the rationale behind each methodology and give some examples of applications in recent studies.1) Quality control. (QC). This first, critical step involves filtering poor quality germline DNA samples and inconsistently performing SNPs from further consideration. This consists on applying specific filtering criteria to samples and/or SNPs before proceeding.2) Association testing. Once QC has been completed, a statistical test is performed with the aim of detecting association between variants in the genome and the trait under consideration.3) Post-GWAS interrogation. Once candidate SNPs have been identified, other types of analyses are performed to ensure the integrity of the association testing including that the influence of genetic structure has been well controlled for and to explore the characteristics of the SNPs identified including for instance biological processes implicated.For the purposes of this review, we will divide a GWAS into three steps:In steps 2 and 3, there are ways in which population structure can be taken into account, but it is important to note that we can use more than one technique on a single GWAS; in fact, they are often combined to avoid spurious associations.In order to illustrate the use of these methods, we sampled data using the 1,000 Genomes Project  dataset.The next subsection will cover two methods that are helpful in investigating the ancestry for each of the individuals in our data. These methods will be present throughout the review and will become useful for both quality control and genotype-phenotype association testing. The first one is admixture analysis, which assumes the existence of discrete ancestral populations from which the current population is derived. The second is principal component analysis, which generates explanatory variables from the genotype data that summarise the sources of variation among the samples and helps visualise and interpret the genetic structure of the samples.Ancestry estimation aims to divide an individual’s genome between multiple ancestral populations from which it is hypothesised to have descended. Most methods used here follow a clustering approach, where each allele is assumed to have a probability of coming from one of the ancestral populations; these methods involve assessment of a large number of SNPs to estimate the contributions of each ancestral population. It is important to differentiate between two distinct forms of ancestry estimation: global and local . Local aThe main assumption for this estimation is that a given individual is descended from ancestors drawn from distinct ethnic groups. The result of an analysis of this kind is an estimation of the proportion of each individual’s genome that comes from each of the ancestral populations.The two most popular algorithms for global ancestry calculation are STRUCTURE  and ADMIAlthough global ancestry uses unsupervised methods such as clustering, local ancestry is more restricted as it requires a locally recruited reference panel, enabling the estimation of the locus-specific likelihood of ancestry. In other words, for each SNP, the ancestral population from which it has most probably been inherited is calculated . If the Although there are several packages to infer local ancestry, there are two that are most commonly used. The first one is RFMix , which aPrincipal Component Analysis (PCA) is a dimensionality reduction method that finds the directions in the variable space under study that explain the most variance; these directions are called the components. In the case of genotype data, each SNP can be represented with values 0, 1 or 2 depending on the dosage of the alternative allele . In this way, a data matrix can be created that has individuals in rows and SNPs in columns. From this matrix, we can compute the components. Each component is orthogonal to the others so they can be used, for example, to visualize the highly dimensional genotype data used in GWAS.It has been observed that the first few principal components from genotype data are related to population structure . The advPCA is a widely used method in different disciplines, so its implementations are abundant. Some of the more popular software for genotype data are the PLINK  --pca me• Missingness. This includes removing SNPs that may give misleading results due to genotyping errors across many samples, or samples that have an excess of errors in the genotyping process and too few high-quality SNP.• Strand alignment. Since DNA is double stranded, it is important to report (and compare) equivalent strands in the data; this can be a problem when merging data from different sources since there can be discrepancies in the reported strand. For example, the Illumina platform differs in definition on the concept of strand from the standard human genome reference . It is i• Heterozygosity. In a homogeneous randomly mating population, very high or low levels of heterozygosity can indicate poor quality genotyping. However, this test is not appropriate in a non-randomly mating population, because population structure can lead to extremes of heterozygosity .• Deviation from Hardy-Weinberg (HW) equilibrium. This test, standard in population studies, evaluates whether the expected relationship between allele frequency and genotype frequency exists. However, HW equilibrium assumes that there is random mating in the population under study; so if there are clear subpopulations (different ancestries) the conditions are not met and the test is not valid as a criteria for assessing quality. Therefore, this test is not generally recommended to use directly when studying structured populations. If the populations are labeled  then it is better to apply HW tests separately.In addition to estimating structure within the samples in our study, we also need to identify the individuals and genomic markers that are appropriate for our study. The first set of criteria that we can use to select our data corresponds to the task of spotting genotyping errors. These criteria are discussed in more depth in several reviews, as well as in original published research, and include missingness (applied to SNPs and samples), case-control differential missingness and tests for heterozygosity and Hardy-Weinberg equilibrium, and strand alignment checks when multiple datasets are involved . Quality• Firstly, individuals whose ancestry is not well represented in either cases or controls should be removed. In the case of a continuous trait this is equivalent to removing outliers. This avoids ancestry-specific biases in the association test, but is not expected to affect the variability of the data ancestry-wise.• Secondly, if distinct populations  are represented across the phenotype, the study can be partitioned over these distinct populations. This would allow us to obtain multiple association tests, the results of which can subsequently be combined (see the post-GWAS Interrogation section). This method reduces ancestry-related variability and bias of each of the studies but decreases the amount of data in each of them, diminishing the statistical power of each test.The second set of criteria we can evaluate with genotype data can elucidate the ancestry of the individuals in the study. For this set we can use the methods we described above: admixture analysis and principal component analysis. There are two ways in which these are used as part of quality control:In case-control studies in particular, the selection of controls is a crucial step. If there is a factor that can influence the outcome (in our case the phenotype) in some way other than the variable that we are measuring (the genotypes), then it must be accounted for in the experimental design. As an illustrative example, in a trial for testing a new drug, there may be covariates (such as sex or age) that should be controlled for in order to ensure that the effect of the drug versus a placebo is measurable; e.g. age and sex may influence the outcome variable due to, for example, metabolism changes and hormone differences. One option to control for these covariates is randomizing which patients will receive the drug. What this procedure does is ensure that the distribution of age and sex between cases and controls is effectively the same, so the influence of these variables does not influence our observation of the drug effects. In GWAS studies, the distribution we want to keep consistent between cases and controls (or across the continuous trait) is the ancestry. In the following example we will use the first two principal components to illustrate this.In order to develop a feeling for what quality control means in a GWAS, imagine a simple dataset  to whichSince the principal components represent a factor that we want to control (ancestry/ethnicity), we need a similar distribution of the components in both the cases and controls. We can further simplify the example by summarising the distribution using the mean . Even byOnce this cluster separation has been done, there is a better fit in the distributions in each of the three sets of cases and controls . AlthougIn order to illustrate this approach, we up-sampled 2000 individuals from the 1000 Genomes Project dataset, removed a number of genotypically similar samples and assigned a fictitious case-control status to each of these in order to make the usefulness of the method more obvious. An ADMIXTURE run on the data shows a cluster that is underrepresented in the cases . This meA PCA run on these data shows that there is a region of the plot where there are no cases, so the appropriate step would be to remove the individuals from that region . It is nIf the clustering  or the separation (observed in PCA) is clear, such as in our sample data, it is preferable to analyse the populations in separate datasets as they may have different patterns of linkage disequilibrium. This can be useful to make statements about SNPs associated to the phenotype that are specific to subpopulations. However, if there are some population-specific signals for the tested trait, they may be lost in the subsequent meta analysis. If there are no distinct clusters, it is considered better to analyse the combined data in the association test .n components ; but in mixed models we say that ν ∼ N, where τ is a parameter for Z, the matrix of random effects. Z is usually the genetic relationship matrix, which estimates the degree of sharing of identity by descent (IBD) between all pairs of individuals in the dataset, but it can also be a matrix of categories where each row (sample) is a vector of zeros everywhere except in the columns that represent the subpopulation to which it belongs .The model for a mixed effects regression for association testing can be written as followsZ such as EMMA  with the trait being measured as a dependent variable. This model allows us to add other covariables; in particular, we can use the first principal components from the genotype PCA. Since the components absorb information about the ancestry, the model will only give significance to the SNPs that are related to the trait without the confounding of the population structure captured by the PCs included in the model.One way of determining how many components to use consists in plotting the components until no separation is found in the data. In our 1000 Genomes example, there is clear separation of the individuals in the scatterplot between components one through four, but the direction of the fifth component is reaching for a subset of less than 1% of the data (the few points with the component 5 greater than 0.4), so it is not accounting for a significant amount of ancestry-related variability . So in tAn example of this usage can be found in As for local ancestry, in Admixture mapping is motivated by the scenario of recent mixing of populations which occurs alongside discrepant incidence of the trait between two populations (i.e. a difference in the proportion of affected people between the ancient populations). Affected persons in the admixed population should therefore be expected to have preferentially inherited the risk locus from the higher incidence population . The genThis method relies on the assumption that the phenotype-associated alleles have different frequencies across ancestral populations. This extra requirement helps specify a model with more statistical power to find these specific loci, so in this way fewer SNPs  are needed to find associations. However, this means that it will fail to identify all risk loci; since not all causal SNPs follow this pattern. Also, fewer loci means longer LD tracts and so a higher difficulty in identifying causal markers via fine mapping .Admixture mapping has been successfully used to identify risk loci associated with specific ancestries across different traits; the tools and panels necessary for performing these kinds of analyses were developed in early 2000s. In 2005, the first applications of this seminal method were published, focusing on the study of African American individuals and finding a number of ancestry-specific associated loci  to the traits: More recently, θ for a study participant, then the genome-wide analysis is conducted for each participant by examining their actual inheritance at each SNP from this average across the genome. The calculation of the actual number of alleles at this SNP that have ancestry from the high risk subpopulation requires some discussion . Analysis of a single SNP will often be uninformative in terms of identifying the ancestral origin of each allele so instead the approach required is to use SNPs in proximity to the SNP under consideration to estimate the actual number of alleles from the high risk subpopulation . If, as computed using either of these approaches, the average proportion of genome from the higher risk population is estimated as pulation .x is the estimated number of alleles at an SNP that have ancestry from the high-risk subpopulation  for a person, then given θ and p, the prevalence of the disease , we can fit the logistic regression model from β is the odds ratio for having 2 copies of the risk allele versus 0 in the high risk population. In the formula, the left hand term is the log odds of the trait. The right hand term of the equation has two components: the first one reflects the prevalence of the disease in log odd terms, and the second models genetic risk considers deviation from the average genotypic contribution from the high risk population for that person.If One extra advantage of admixture mapping is that, since this model examines ancestry at each SNP with the average across the genome for that person, there is an alternative test that can be done without controls . It involves testing whether there is an increased risk according to the local ancestry in a given SNP. However, in practice, power is usually greater for the case-control comparison.One widely used software to run admixture mapping can be found in the GENESIS package for the R programming language via the admixMap function.βi are the effect estimates, X1 is the admixture proportion from the first ancestry, X2, X3 are the number of copies of the alternative allele coming from the first and second populations respectively , and after that we can add any number of covariates such as age or some PCA components. This model allows for the inclusion of ancestry specific information, and in that way it results in relevant summary statistics related directly to each of the populations of the admixture. This model accounts only for two ancestries, however the model can be expanded to several ancestries.A novel approach is using the inference of local ancestry directly in the association testing. The software Tractor  implemenHaving a parameter associated to the ancestry in a given locus prevents the association model from attributing an effect to the allele count that is better explained by the ancient population from which the haplotype is descended. This avoids bias caused by local ancestry differences between populations that are not attributable to the trait .In addition, although this method is analogous to controlling via PCA in the sense that we are controlling for ancestry, this type of regression achieves this by analyzing the ancestry of each specific locus at a time. This allows us to add samples without worrying about introducing population structure, which then translates into more statistical power.These ancestry specific parameters provide information on ancestry predisposition to the trait. In contrast to admixture mapping, this method does not assume that the phenotype incidence differs across the ancestral populations. In this model, however, it is necessary to have data on both cases and controls.Association tests are performed on a SNP by SNP basis, so after the candidate SNPs have been identified, it is important to use techniques that help us validate the adequacy of our population adjustments in the previous steps. The technique of genomic control will allow us to evaluate whether the association test has a bias based on population structure. Performing a meta-analysis will allow us to combine the results of the different populations if we previously decided to separate by subpopulations in the quality control step.p-values) obtained from the association analysis based on a single number, usually called the genomic inflation factor  for the trait. However, since factors such as sample size can influence the existence of different levels of uncertainty on each study, we must have a measure available to assess uncertainty. For this purpose, having also the standard error will allow us to perform an inverse variance-weighted meta-analysis; which means that we are using the variance of the estimator to weigh in the uncertainty found in each of the studies before performing the meta-analysis.The results that we intend to aggregate from the studies are the effect sizes (ϵ).The first model we can use is to use a fixed-effects-only model. This assumes that all of the effect sizes across all studies are the same, and the differences between them are the product of a normally distributed random error  Quality Control. The objective in this step is to remove low-quality SNPs and samples and to ensure a comparable population structure across the phenotype (e.g. same distribution among cases and controls).• Comparability of cases and controls. Removing outliers from the data can be convenient to the analysis, but excluding whole subpopulations hurts the generalizability of the study. This strategy is used mostly when the control data has not been sampled according to the same protocol as the cases, like the case of using a generic database such as a biobank.• Separating the data for multiple association studies. If there is an overrepresentation of a subpopulation or if there is a need to report population specific related SNPs, it could be convenient to analyse the data separately. The main caveat of doing this is the possibility of having to perform an association test with few data.2) Controlling for ancestry at the association test step. Here, we account for population structure in the actual modeling of the genotype-genotype relationship. This helps avoid spurious correlations. Methods that we can use for this purpose are:• PCA. There is no reason not to control for ancestry using PCA, but it is important to add the correct number of components to the model . The rec• Admixture mapping. If there are no clearly distinct subpopulations found in the sample, admixture mapping is an appropriate way to find regions where the admixture is related to the phenotype. Some methods such as Tractor can also find the specific effect sizes on each of the subpopulations.3) Post-GWAS interrogation. As in many other cases of experimental studies, the results of a statistical procedure should be analysed and should be open to correction according to the data and data cleaning that has been used.• Genomic control. This tool is useful as a measure of the population structure that has been introduced to the study, and to suggest whether or not it is necessary to go back to previous steps in order to further account for the structure of the data. Although it is possible to use it to control for overall population structure by scaling the p-values of the association test, it is not recommended and should be used only as a sanity check.• Meta-analysis. This is necessary in order to aggregate the results in the case that we have separated the data into its subpopulations. It is possible to achieve the same power as a whole-data association test given some properties, but any population specific signal that may have appeared in the individual studies might be lost in the meta-analysis.In this review, we have attempted to give an overview of the methods used for performing GWAS on admixed populations. The main objective was to shed some light on the intuition behind using each of them.


Given that romantic partners play a pivotal role in patients’ survivorship period, integrating partners into survivorship care and broadening the focus of behavioral interventions from the individual (survivor) to the survivor-partner dyad may make healthy lifestyle behaviors more easily adopted and potentially maintained. Understanding the role of dyadic processes in Black survivors is particularly important because their lifestyle behaviors are poor and they have higher cancer-specific and all-cause mortality. To develop an effective dyadic lifestyle behavior intervention for Black survivors, micro-level investigations of interactions between Black survivors and their partners are necessary to pinpoint how survivors and partners facilitate or hinder each other’s lifestyle behaviors in their natural, everyday lives. Accordingly, the objective of the present study is to fill these gaps using ecological momentary assessment to eventually develop more effective lifestyle interventions for Black prostate cancer (PCa) survivors and partners. A total of 120 dyads  who are Black adult survivors diagnosed with non-metastatic PCa and their romantic partners will be asked to complete four assessments per day for 14 consecutive days on a smartphone after an initial retrospective survey. Over the 14 days, participants will be asked to complete a brief survey regarding their lifestyle behaviors , contexts of lifestyle behaviors, stress, and coping. Physical activity and sedentary behavior will be assessed via accelerometer; eating behaviors will be assessed with the Automated Self-Administered 24-hour Dietary Assessment Tool. After completing the 14-day assessment, participants will be asked to complete a final retrospective survey. Results of the proposed study will inform the rigorous development of a theory-based dyadic lifestyle intervention in this vulnerable survivorship population with the ultimate goal to improve overall survival and reduce morbidities (for survivors) and reduce cancer incidence (for partners). Prostate cancer (PCa) is the most commonly diagnosed cancer in American men , but theWhile many lifestyle interventions have been developed for cancer survivors  and are Cancer is often considered a “we-disease” ; survivoWe argue that these unexpected results are partly due to the fact that these dyadic lifestyle interventions were simply adding partners to the existing individuals-only interventions and did not take on a truly dyadic perspective. In order to maximize the impacts of the dyadic lifestyle interventions, we need to address factors  that will lead to greater behavioral changes in survivors than survival-only interventions and will promote behavioral changes in both survivors and partners. Nevertheless, the characteristics of Black PCa survivor-partner dyads that facilitate these behavioral changes are completely unknown. Before implementing another dyadic lifestyle intervention, micro-level investigation of interactions between survivors and partners is necessary to pinpoint how Black PCa survivors and partners facilitate or hinder each other’s lifestyle behaviors in their natural, everyday lives.Stress can substantially influence poor lifestyle behaviors –40, but supportive/unsupportive dyadic coping , delegated dyadic coping , negative dyadic coping  and common dyadic coping . Thus, dyadic coping goes beyond the exchange of social support ). If participants respond yes, they will be asked to report its severity on each stressor . Unless participants reported 1  to the specific stressor, dyadic coping will be assessed with eight items (two items for each coping) from the DCI [“what did your partner do when you were stressed?” : supportive/unsupportive , delegate , negative , and common dyadic coping . For self-reported lifestyle behaviors, physical activity  and sedentary behavior  occurrence and time spent will be asked. Moderate-to-vigorous physical activity (≥3 MET) and sedentary hours will be calculated. Dietary behaviors such as eating chips/fries, sweets, and fruits & vegetables and drinking regular soda will be assessed (yes/no). Finally, the social and physical contexts of lifestyle behaviors will be assessed (only when lifestyle behaviors occur) with one item each for each lifestyle behavior, for example, ‘Who were you with when you exercised?’ and ‘Where were you when you ate sweets?’.To decrease participants’ burden, reduced items will be used instead of a full scale. Participants will be asked to respond to questions within a certain time frame such as “since waking up this morning (first assessment of a given day),” or “since your last assessment .” Stress will be assessed with five items adapted from the MSQ-C  that cov the DCI , 64 withAll participants will be asked to wear a blinded accelerometer (Actigraph GT3X+ or GT3X-BT) on their waist during waking hours (except bathing and swimming) on their right hip for the 14 days. The Actigraph records counts of physical activity throughout the day in user-set “epochs” and steps per minute. In line with the national surveillance data , moderatDietary intake will be assessed with the Automated Self-Administered 24-hour (ASA24) dietary assessment tool  that estWe will first conduct extensive descriptive analyses on the data collected at baseline, across the 14-day period  and at the final assessment. Descriptive statistics, e.g., means, standard deviations (SDs), and ranges for continuous measures, and frequencies and proportions for categorical variables, along with 95% confidence intervals (CIs) for the means will be calculated. Preliminary associations among variables  will be assessed at specific time points of interest using Pearson and Spearman correlation coefficients, t-tests, analysis of variance (ANOVA), chi-squared or Fisher’s exact tests, where appropriate.For Aim 1, there are two primary lifestyle behavior outcome variables: physical activity  and nutrition . Each lifestyle behavior is measured for both survivors and partners and at each time point , when applicable. The primary predictor variable of interest is common dyadic coping, again measured for both survivors and partners. Our primary analysis will treat the common dyadic coping as a continuous predictor (with a score of 1–5). However, other methods will be explored by treating it as categorical (such as above or below certain thresholds).Our analysis will mainly use generalized linear mixed-effects models (GLMMs), which can account for binary outcomes with a canonical (logit) link function and the within-subject  correlations using random effects and a variety of repeated measures correlation structure, as appropriate. The GLMM will reduce to a linear mixed model (LMM) for the continuous physical activity outcome. A variance-stabilizing transformation such as the square-root or log transformation may be applied to the continuous physical activity outcome, as appropriate. When both the survivors’ and partners’ outcomes are analyzed, the within-dyad correlation will be accounted for using random dyad effects. The random effects and repeated measures correlation structure will be selected using the Bayesian information criterion (BIC).To assess the association between common dyadic coping of partner or survivor and each lifestyle behavior of the survivor or partner , we will construct two new variables corresponding to each common dyadic coping variable for each individual: 1) the mean across all common dyadic coping measurements within the individual, representing an “average” individual coping level; and 2) the deviation of situation-specific common dyadic coping from the individual-specific average, representing a within-subject level coping deviation. Both variables, in place of the one original common dyadic coping variable, will be included in the model and tested for their associations with the lifestyle behavioral outcome. This approach allows us to test for both the between-subject and within-subject effects of common dyadic coping on a lifestyle behavior, which is made possible given the intensive EMA measures . To contHowever, significance based on the secondary analyses will be interpreted as hypothesis generating, given that the overall type I error rate is not adequately controlled for in these secondary analyses. Similar analyses at the within-day level  will also be conducted. All analyses described above assume the lifestyle behaviors and predictors of interest are measured during the same time intervals of day for each individual. Additional exploratory analyses will examine lagged associations, i.e., the effects of the predictors measured in the previous time interval  on the lifestyle behavior outcome in the current time interval ; or alternatively, common dyadic coping could be measured based on potential interactions between the survivor and partner during the past 24 hours, given that such interactions may not occur as often as in four-hour time intervals during each day. All these exploratory analyses are possible, again given the intensive EMA measures.We will perform the above-mentioned analyses with and without controlling for the baseline covariates of interest, including demographics  and physical activity or nutrition variables. The association between common dyadic coping and health eating index (0–100), which is measured only four times for each individual, will be conducted by using summarized measures of common dyadic coping for the corresponding periods in which the health eating index is calculated. For example, if the health eating index is calculated for a whole week that includes all work days, then the summaries of common dyadic coping may be the mean and/or standard deviation of common dyadic coping measured during the week (and across days). To derive such summary features of common dyadic coping, we will follow the methods of Cofta-Woerpel and colleagues .For Aim 2, descriptive analysis  will be conducted to examine the places  in which the lifestyle behaviors occurred and the number of lifestyle behaviors engaged alone and with others . In addition, the contexts (days of a week and times of a day and place) in which survivors and partners engaged in lifestyle behaviors together will be investigated. All analyses for assessing Aim 2 will be exploratory in nature.For the exploratory aim  will be examined in the associations between stress and dyadic coping by adding to and testing for their interaction effects with the predictors of interest. Significance at a two-sided 0.05 level will be deemed interesting findings, due to the exploratory nature of this aim.Our sample size justification is based on testing for the 16 primary associations of interest, i.e., each newly constructed common dyadic coping variable of a partner or survivor with each lifestyle behavior of the survivor or partner, each at a two-sided 0.0031 significance level. That is, we will test the effects of both the between-subject variable (the average within-subject common dyadic coping) and the situation-specific deviation of the common dyadic coping from the subject-specific average. For the between-subject variable, by assuming conservatively a 10% loss to follow-up of individuals and an additional attrition of 25% lifestyle behavior or EMA measurements for those who are not lost to follow-up, and an ICC of 0.5, a 0.0031 two-sided Fisher’s z test of the null hypothesis that the Pearson correlation coefficient r = 0 between common dyadic coping and physical activity, will have 80% power to detect an r of 0.295  when the sample size is 159 (nQuery 7.0). The effective sample size is calculated by accounting for the variance inflation factor (= 120×0.9×0.75×14×4/(1+(14×4–1)×0.5)). If the ICC is 0.2, the sample size is 378, resulting in a detectable correlation of 0.195 with 80% power. To test the association between a common dyadic coping variable and a nutrition outcome (binary), a logistic regression analysis  with a sample size of 119 observations  achieves 80% power at a 0.0031 significance level to detect a change in the probability of eating fruits/vegetables from the value of 0.300 at the mean of common dyadic coping to 0.478 when the common dyadic coping is increased to one standard deviation above the mean. This change corresponds to an odds ratio of 2.138 (PASS 2005). When the ICC is 0.2, the corresponding detectable odds ratio will be 1.637 under an effective sample size of 378. The power to detect the within-subject effect of the variable of deviation of common dyadic coping from its individual-specific average is generally higher than detecting the same level of between-subject effect. Thus, our proposed sample size will yield adequate power to detect the 16 primary associations of interest, while allowing reasonable assessment of the secondary and exploratory associations of interest.The proposed study seeks to shift current individual-focused (either survivors or caregivers) research and clinical paradigms to relationship-focused ones. To our best knowledge, this is the first study that explicitly investigates a unique, theory-based concept, dyadic coping, which is beyond social support for Black PCa survivors’ and partners’ lifestyle behaviors. Results of this study are expected to lead to the development of a dyadic lifestyle intervention that addresses stress and dyadic coping that may have higher effects for behavioral changes compared to individual-only interventions. This is also the first micro-longitudinal study that investigates in-depth interpersonal processes between Black PCa survivors and their partners that are essential for the future intervention development. Despite the significant roles of partners in the lives of Black men , very feBecause partners are often primary caregivers among Black PCa survivors  and rela


Objective: Body weight is an important economic trait for a goat, which greatly affects animal growth and survival. The purpose of this study was to identify genes associated with birth weight (BW), weaning weight (WW), and yearling weight (YW).Materials and Methods: In this study, a genome-wide association study (GWAS) of BW, WW, and YW was determined using the GGP_Goat_70K single-nucleotide polymorphism (SNP) chip in 1,920 Inner Mongolia cashmere goats.Results: We discovered that 21 SNPs were significantly associated with BW on the genome-wide levels. These SNPs were located in 10 genes, e.g., Mitogen-Activated Protein Kinase 3 (MAPK3), LIM domain binding 2 (LDB2), and low-density lipoprotein receptor-related protein 1B (LRP1B), which may be related to muscle growth and development in Inner Mongolia Cashmere goats. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these genes were significantly enriched in the regulation of actin cytoskeleton and phospholipase D signaling pathway etc.Conclusion: In summary, this study will improve the marker-assisted breeding of Inner Mongolia cashmere goats and the molecular mechanisms of important economic traits. IGF-II and CAPN1) in chickens breeding for meat quality  and weaning weight (WW), are the basis for selection in genetic improvement programs for meat production . To inve quality . HoweverKCTD4, LIM domain binding 2 (LDB2), HEP21, and PCASP2 were closely correlated with the spleen weight of Layer chicken  as candidate genes for this trait (CCND2 genes as the candidate gene for body weight trait (Genome-wide association study (GWAS) is a tool of genome-wide linkage disequilibrium to determine genetic marker information  and related genes that genetically affect complex phenotypic traits or important quantitative economic traits throughout the genome –6. In 20 chicken . Ghasemiis trait . GWAS ofht trait .The Inner Mongolia cashmere goat is a local breed in China with dual-purpose, producing both pleasant meat and excellent cashmere. It is famous for its “white cashmere and meat” in Inner Mongolia. Besides, Inner Mongolia cashmere goats also provide sufficient meat resources and have become one of the most important livestock species for the herdsmen. However, there are no GWAS studies focusing on the body weight traits of Inner Mongolia cashmere goats. Therefore, in this study, we examined BW, WW, and yearling weight (YW) by GGP_Goat_70K SNP chip and GWAS methodology for Inner Mongolia cashmere goat population of 1,920 individuals to identify significant SNPs and the major candidate genes associated with body weight traits in cashmere goats. This study may facilitate the potential use of major genes involving in growth and production traits for genetic improvement of productivity in cashmere goats.In this experiment, the breeding environment was in compliance with the standards relevant to an ordinary animal laboratory facility in China National Standard “Laboratory Animal Environment and Facilities” (GB14925-2010). The feeding and the experimental operations on animals were in accordance with the animal welfare requirements.n = 1,920, 2 years old from six herds; 1 year old from three herds) were selected randomly from the Arbas Stock Farm in Inner Mongolia, China. The BW, WW (3.5 months), and YW (12 months) were measured using an electronic scale for all goats. BW was recorded within 0.5 h after birth, while WW and YW were measured after 12 h of fasting. Samples of ear tissue were collected by ear deficiency forceps and quickly placed in a prepared cryopreservation tube containing 75% alcohol for storage at −80°C until DNA extraction.Goats used in the present study  procedure using the Statistical Analysis System (SAS) program. The influencing factors included the year of production , herd , birth status (simple or twin), maternal ages , and sex . Therefore, repeatability and multivariate animal model was used with restricted maximum likelihood method (AIREML) in the WOMBAT software to estimate the variance components of each trait. Then heritability for each trait was obtained under this model. The default convergence criterion was 10yi is the vector of observation of animals for trait i; bi is the vector of fixed effects for trait i; ai is the vector of direct additive genetic effects of animal for trait i; mi is the vector of random maternal genetic effects of animal for trait i; and pi is the vector of individual performant environmental effects for trait i. Yi is maternal genetic effects for trait i, Xi, Zi, and Wi are design matrices of the corresponding effects, respectively. ei is the vector of random residuals effects for trait i. The heritability (h2), was calculated using the formula Where Deoxyribonucleic acid was extracted from ear tissue with the standard phenol-chloroform method according to the protocol of the manufacturer. DNA integrity and purity were tested using 2% agarose gel electrophoresis and a NanoDrop 2000 ultraviolet spectrophotometer .−5 were removed from the analysis. Plink 1.90 beta . The samples with call rates <90% were removed from the analysis. The SNPs with GenCall (GC) scores <0.6, genotype call rates <90%, minor allele frequencies (MAF) < 0.01, and significant Hardy-Weinberg (HWE) disequilibrium at 10.90 beta  and R soP < 9.92 × 10−7 (0.05/50383), and the chromosome-wide significance level threshold was P < 2.88 × 10−5 . Chromosome-wide significance level SNPs were defined to call chromosome-wide significance associations, with a suggestive association corresponds cutoff P < 10−4  with inflation factors (λ). The association map and the significant SNPs were visualized in the Manhattan plot with a threshold line. The Manhattan and Q–Q graphics were generated with R v. 3.5.2.The correlations between the SNPs and the traits were tested using mixed linear models in GEMMA software version 0.98 –18. TherP < 10−4 . The quaP < 0.05 were statistically significant.Genes associated with significantly correlated SNP loci were annotated with the goat reference genome . The genes were used as input for Cytoscape that analyzes and visualizes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) , 21. GO Observed phenotypes were analyzed by first descriptive statistics . The BW,−5). After the quality control (QC) was performed on the raw genotypes, a total of 1,909 animals and 50,383 SNPs were obtained and were distributed over the 29 goat chromosomes , LDB2,low-density lipoprotein receptor-related protein 1B , which is similar to the values estimated by Ma et al. . The herMaternal age only has significant effects on BW and WW in early traits, but not YW, indicating great influences of maternal effects on lamb BW and WW. Studies discovered that with the increase of the age of mother, BW and WW also increase, suggesting lower BW and WW of lambs born to primiparous ewes than that of adult ewes. The reason is that the physical maturity and physical maturity of primiparous ewes are not well-developed. In short, lactating lambs have a strong dependence on the ewe, strengthen the feeding and management, improve the nutritional status, and promote lactation, which is conducive to the normal development of lamb.To correct the effects of population structure and individual kinship relationships, a population genetic structure is considered as a fixed effect while an individual kinship is thought of as a random effect. Therefore, the GWAS of BW, WW, and YW of 1,920 Inner Mongolia cashmere goats was performed using a mixed linear model in this study. The Inner Mongolia cashmere goats that we chose are mainly grazing animals and the nutritional level of their pastures varies among the seasons, which will further affect gene expressions , 26.TULP2 gene is located on chromosome 18 and is correlated with WW. TULP2 is a member of the tubby gene family, and gene SNPs in the 19q13.33–13.43 chromosomal region are significantly related to severe obesity in French Caucasians  gene in the chicken chromosome 4 (GGA4) region ~8.6 Mb in length (71.6–80.2 Mb) had the strongest association with body weight for weeks 7–12 and with average daily gain for weeks 6–12  chromosome 4 associated with Carcass Weight (CW) and Eviscerated Weight (EW) traits, GWASs were conducted using the Illumina 60 K SNP Bead chip to genotype 724 Beijing-You chickens  and a significant association with child body mass index (BMI) in humans (NR1 is involved in the growth cone (GO: 0030426). CNR1 can affect the regulation of leptin signaling to regulate BW, food intake, and metabolism , FBXL3, and LOC102176015 genes are new candidate genes related to BW. PROM1 is a protein-coding gene, which plays a role in cell differentiation, proliferation, and apoptosis. FBXL3 encodes a member of the F-box protein family, which plays a key role in the maintenance of both the speed and the robustness of the circadian clock oscillation.The gramming . Wang etty trait . LRP1B gl uptake . Dietricethality . Associan humans . CNR1 istabolism . BesidesMAPK3, LDB2, and LRP1B genes are key candidate genes for BW. PROM1, FBXL3, and LOC102176015 genes are new genes related to BW and can be given to follow-up priority research. The candidate genes in this study were closely correlated to BW development. These findings will make a significant contribution to the understanding of the mechanisms underlying BW traits and facilitate genetics improvement of productivity in Inner Mongolia cashmere goats by providing essential genes related to goat BW traits.In summary, the first GWAS of Inner Mongolia cashmere goats was performed to identify gene-associated to three body traits. We identified 21 SNPs that reached the genome-wide significance levels and 50 SNPs that reached the chromosome-wide significance levels for the three traits in a population of 1,920 Inner Mongolia cashmere goats. figshare.com/s/7734276ce6c1239e9474.The original contributions presented in the study are publicly available. This data can be found here: All samples were collected in accordance with the International Guiding Principles for Biomedical Research involving animals and approved by the Special Committee on Scientific Research and Academic Ethics of Inner Mongolia Agricultural University, responsible for the approval of Biomedical Research Ethics of Inner Mongolia Agricultural University [Approval No. (2020) 056]. No specific permissions were required for these activities, and no endangered or protected species were involved.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was financially supported by National Natural Science Foundation of China (31860637); Science and Technology Major Project of Inner Mongolia Autonomous Region 2021SZD0010); Science and Technology Project of Inner Mongolia Autonomous Region (2019GG243); China Agriculture Research System of MOF and MARA (CARS-39); Scientific Project of Inner Mongolia Agricultural University on High-level Introduced Talented Personnel (NDYB2018-1); Central Government Guides Local Science and Technology Development Fund Projects (2020ZY0007); Youth Foundation in Inner Mongolia Agricultural University (No. QN202003). Special Funding Project for the Iconic Achievements of the College of Animal Science, Inner Mongolia Agricultural University (No. BZCG202111).LZ and LH were employed by company Inner Mongolia Jinlai Livestock Technology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Carcass and meat quality are two important attributes for the beef industry because they drive profitability and consumer demand. These traits are of even greater importance in crossbred cattle used in subtropical and tropical regions for their superior adaptability because they tend to underperform compared to their purebred counterparts. Many of these traits are challenging and expensive to measure and unavailable until late in life or after the animal is harvested, hence unrealistic to improve through traditional phenotypic selection, but perfect candidates for genomic selection. Before genomic selection can be implemented in crossbred populations, it is important to explore if pleiotropic effects exist between carcass and meat quality traits. Therefore, the objective of this study was to identify genomic regions with pleiotropic effects on carcass and meat quality traits in a multibreed Angus–Brahman population that included purebred and crossbred animals. Data included phenotypes for 10 carcass and meat quality traits from 2,384 steers, of which 1,038 were genotyped with the GGP Bovine F-250. Single-trait genome-wide association studies were first used to investigate the relevance of direct additive genetic effects on each carcass, sensory and visual meat quality traits. A second analysis for each trait included all other phenotypes as covariates to correct for direct causal effects from identified genomic regions with pure direct effects on the trait under analysis. Five genomic windows on chromosomes BTA5, BTA7, BTA18, and BTA29 explained more than 1% of additive genetic variance of two or more traits. Moreover, three suggestive pleiotropic regions were identified on BTA10 and BTA19. The 317 genes uncovered in pleiotropic regions included anchoring and cytoskeletal proteins, key players in cell growth, muscle development, lipid metabolism and fat deposition, and important factors in muscle proteolysis. A functional analysis of these genes revealed GO terms directly related to carcass quality, meat quality, and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell–cell adhesion. These results contribute with novel information about the complex genetic architecture and pleiotropic effects of carcass and meat quality traits in crossbred beef cattle. Bos taurus taurus) beef cattle breeds (Bos taurus indicus) breeds as a strategy to enhance beef production  are two of the most important attributes for the beef industry because they drive profitability and consumer demand. Carcass and meat quality are complex concepts that are described through multiple traits like ribeye area and marbling ; tenderness, flavor, and juiciness ; and color, texture, and firmness . Each one of these individual component traits are complex in nature, under the control of multiple genes, and influenced by environmental factors. Most of these component traits are challenging and expensive to measure and unavailable until late in life or after the animal was harvested. Genetic improvement of such traits is not viable through traditional phenotypic selection, but these traits are perfect candidates for genomic selection if genetic markers accounting for a large proportion of the additive genetic variation can be identified.The genetic architecture of carcass quality traits in beef cattle has been more extensively investigated in purebred  than in The University of Florida Institutional Animal Care and Use Committee approved the research protocol used in this study (number 201003744). The cattle population for this study consisted of 2,384 steers from the University of Florida multibreed Angus × Brahman herd  born bet2). Carcasses were ribbed between the 12th and 13th rib and marbling and REA were visually appraised and recorded by graders 48 h postmortem. Marbling (MARB) was graded as follows: Practically Devoid = 100–199, Traces = 200–299, Slight = 300–399, Small = 400–499, Modest = 500–599, Moderate = 600–699, Slightly Abundant = 700–799, Moderately Abundant = 800–899, Abundant = 900–999. Visual meat quality traits recorded included color (COLOR) on a scale of 1 = extremely bright cherry red to 8 = extremely dark, texture (TEXT) on a scale of 1 = very fine to 7 = extremely coarse, and firmness (FIRM) on a scale of 1 = very firm to 7 = extremely soft. All visual meat quality phenotypes were taken by trained personnel between the 12th and 13th ribs, 48 h postmortem and approximately 1 h after ribbing to allow for oxygenation of the Longissimus muscle. Given the small number of observations at the high end of the range for COLOR, TEXT, and FIRM, scores 7 and 8 were combined for COLOR, scores 5, 6, and 7 were combined for TEXT, and scores 4 and 5 were combined for FIRM.Steers were transported to a commercial packing plant when they reached 1.27 cm of subcutaneous fat over the ribeye , where they were harvested under USDA-FSIS inspection. Carcass quality traits available included hot carcass weight , marbling score, FOE, and rib eye area , juiciness , and beef flavor intensity . Average sensory score from all members of the panel for each steak was used as input in the statistical analyses.One 2.54 cm thick steak from the A factor analysis was used to identify high percentages of explained common variances between HCW and REA and between FOE and MARB (data not shown). Subsequently, REA and MARB were selected for further analyses based on their economic importance and likelihood of being included as selection objectives in genetic evaluation programs. Using a similar approach, TEND, JUIC, and FLAV were selected to describe the sensory meat quality, and COLOR, TEXT, and FIRM were chosen to explain visual meat quality.DNA was extracted from blood with the QIAamp DNA Blood Mini DNA kit  following the manufacturer’s protocol and stored at −20°C. Genotyping was carried out on 1,038 of the 2,384 animals using the Bovine GGP F250 array  which contains 221,115 single nucleotide polymorphisms (SNPs). The SNP markers mapping to the sex chromosomes, with minor allelic frequency (MAF) lower than 0.01% and call rate lower than 90% were excluded. After quality control, 125,042 SNP markers were retained for subsequent genomic analysis.airemlf90 package from the BLUPF90 family of programs from Ignacy Misztal and collaborators, University of Georgia. The ssGBLUP procedure utilizes all available phenotypic, pedigree and genotypic information (H−1) instead of the inverse of the classical pedigree-based relationship matrix (A−1). The H−1 is defined as follows (Average information restricted maximum likelihood (AIREML) variance components, heritabilities, additive genetic correlations, and phenotypic correlations were estimated using single-trait and two-trait single-step genomic best linear unbiased prediction (ssGBLUP) from single-trait and two-trait animal linear mixed models. Computations were performed with the ormation . Thus, t follows :G−1 is the inverse of the genomic relationship matrix and G matrix was constructed based on where Z is a centered incidence matrix of genotype covariates , and 2∑pi(1−pi) is a scaling parameter in which pi is the frequency of the reference allele at the ith SNP. To avoid singularity issues, G inverse was built as G−1 = (0.95G + 0.05A22)−1.where The single-trait and two-trait animal mixed models used in this study included the direct additive genetic and residual as random effects, year of birth as a class effect, and age at slaughter as a covariate, except for TEND and FLAV where age at slaughter was not significant. The single-trait animal mixed models were as follows:y is a vector of phenotypic records, X is an incidence matrix linking phenotypic records to fixed effects, b is a vector of fixed effects, Z is an incidence matrix relating phenotypic records to direct additive genetic effects, u is a vector of random animal direct additive genetic effects, and e is a vector of random residuals. The random vectors u and e were distributed as H is the joint pedigree-genomic relationship matrix, and I is an identity matrix. Thus, the (co)variance matrix of u and e random vectors in single-trait models (1V) was as follows:where u ∼MVN and e ∼MVN, where T that is the additive genetic (co)variance matrix and R that is the residual (co)variance matrix were defined between the two traits under analysis, MVN represents the multivariate normal distribution, and ⊗ denotes the Kronecker product. Thus, the (co)variance matrix of u and e random vectors was as follows:The two-trait animal mixed models used to estimate phenotypic and genetic correlations between pairs of traits included the same fixed and random effects as the single-trait models. However, it was assumed that Single-trait genome-wide association studies (GWAS) were carried out using the weighted ssGBLUP (WssGBLUP) procedure  to inves1.D = I.Set the diagonal matrix of SNP variance or weights as identity, 2.G matrix: G = ZDZ′λ, where λ = 1/2∑pi(1−pi).Construct the 3.blupf90 package.Predict GEBVs using ssGBLUP with 4.postGSf90 package: Convert GEBVs to SNP effects (5.di) using a non-linearA variance method: Compute the weight for each SNP  and marbling score (410.44 ± 96.89) were comparable to national beef industry averages , and simscounted .While carcass quality is the primary factor determining the value of a carcass in the beef industry supply chain, consumers evaluate beef products at purchase time based on visual quality and at consumption time based on sensory quality. Both the visual and sensory evaluation of the beef product have an important impact on the decision to make a repeated purchase, which is important for sustained or increased demand . Sensoryh2) with standard deviation (SD) for carcass quality, sensory meat quality, and visual meat quality traits in the multibreed Angus–Brahman population. Heritability estimates for MARB, REA and TEND were moderate, ranging from 0.43 to 0.53, and consistent with the average of heritability estimates reported in the literature  were generally consistent with values reported in the literature  and MARB (1.31%). The second one was located at 56.2-56.9 Mb and explained 4.72% of the direct additive genetic variance for REA, 2.45% for TEND, 2.12% for MARB, 1.58% for TEXT, 1.33% for FIRM, and 1.06% for JUIC. The first region around 25–28 Mb on BTA5 was previously reported to be associated with numerous carcass and meat quality traits in beef cattle, specifically MARB and REA . The secOne genomic window located on BTA7 (51.6–52.5 Mb) had pleiotropic effects on MARB (explaining 2.22% of the direct additive genetic variance), TEND (1.65% of the direct additive genetic variance) and TEXT (1.34% of the direct additive genetic variance), and had a suggestive pleiotropic effect on COLOR (0.92% of the direct additive genetic variance). Previous reports also associated this BTA7 region with MARB , TEND A, and fatThe genomic region between 61.9 and 62.5 Mb on BTA18 accounted for 2.31, 2.09, and 1.09% of the direct additive genetic variance for FLAV, COLOR, and TEND, respectively. Although no specific associations with these traits have been reported, this BTA18 chromosomal region was involved with other carcass traits in cattle .A pleiotropic region located on BTA 29 (43.1–43.4 Mb) simultaneously affected TEND (1.42% of direct additive genetic variance) and FLAV (1.22% of direct additive genetic variance). This is an important region because of its reported association with meat quality, in particular TEND, and because it harbors the μ-calpain gene, a well-established candidate gene due to its role in myofibrillar protein degradation.A suggestive pleiotropic region on BTA10 (76.2–77.2 Mb) explained 1.11 and 0.98% of the direct additive genetic variance for JUIC and MARB. Lastly, two suggestive pleiotropic regions were detected on BTA19 (27.0–28.0 and 38.2–39.1 Mb). The first region was associated with TEND (1.13% of the direct additive genetic variance) and had a suggestive effect on TEXT (0.94% of direct additive genetic variance), while the second region explained 1.04 and 0.95% of direct additive genetic variance genetic variances for JUIC and COLOR, respectively.The pleiotropic genomic regions described above contained about 317 genes . HoweverCysteine Sulfinic Acid Decarboxylase (CSAD) and Tensin-2 (TNS2); hence influencing marbling and ribeye area. The CSAD gene is involved in taurine biosynthesis. Taurine, although not used in protein synthesis, is the most abundant free amino acid in mammalian tissues and has multiple functions, including skeletal muscular structure and function (At least two genes in the first pleiotropic region on BTA5 (26.7–27.5 Mb) are directly involved in muscle physiology and lipid metabolism: function  and lipifunction . Tensin function , and mayfunction .Low-Density Lipoprotein Receptor-Related Protein 1 (LRP1), Myosin 1A (MYO1A), and Nascent Polypeptide-Associated Complex Alpha Subunit (NACA). The LRP1 gene plays important roles in many cellular and biological processes, including cell growth and lipid metabolism , Tachykinin 3 (TAC3), and G Protein-Coupled Receptor 182 (GPR182) genes were also reported to be simultaneously associated with carcass and meat quality latent variables in the same multibreed Angus–Brahman population (G Protein-Coupled Receptor 182 (GPR182) gene that was differentially expressed in the skeletal muscle of finishing pigs fed a lysine-deficient vs. a lysine-adequate diet (Retinol Dehydrogenase 16 (RDH16) gene that is involved in retinol metabolism and seems to be involved in steatosis in Japanese Black cattle (The second pleiotropic region on BTA5 (56.2–56.9 Mb) harbors three candidate genes involved in lipid metabolism and muscle development, namely tabolism , and regtabolism . MYO1A ilineages , and liplineages . In addipulation . Lastly,ate diet , and thek cattle . This sek cattle .Protocadherin Beta 1 (PCDHB1), which may directly impact marbling, tenderness, and texture. Protocadherins are cell-adhesion molecules and PCDHB1 to be under selection in cattle. Cadherins are structural proteins and some of them were associated with marbling, suggesting that they play important roles in cell adhesion and differentiation in several bovine tissues  which could affect color and flavor. Vitamin A, or retinol, gives beef a yellowish hue (RDH13 was associated with fat deposition in beef cattle (RDH13 on beef fatty acid composition could have a direct impact on flavor. Another gene in this window is Ubiquitin Conjugating Enzyme E2 S (UBE2S), a member of the ubiquitin-conjugating enzyme family with important roles in protein metabolism and remodeling of adherens junctions. The role of UBE2S in ubiquitin-mediated proteolysis supports the association with TEND and this is further reinforced by a GWA study in Nellore beef cattle which identified the UBE2S gene as related to meat tenderness (The pleiotropic window identified on BTA18 contains wish hue . Regulatwish hue . Additiof cattle . The effnderness .Spectrin Repeat Containing Nuclear Envelope Protein 2 (SYNE2) and Spectrin Beta, Erythrocytic (SPTB) genes. Both genes encode spectrin proteins that bind actin filaments in the cell to the nuclear membrane stabilizing the cell’s nucleus. SYNE2 was previously identified in the same multibreed Angus–Brahman population as a candidate gene in a region explaining a large percentage of direct additive genetic variances for carcass quality , Chromodomain Helicase DNA Binding Protein 3 (CHD3), Arachidonate 15-Lipoxygenase Type B (ALOX15B), and Phosphoribosylformylglycinamidine Synthase (PFAS). The DNAH2 gene codes for a motor protein found in cilia and flagella that was related to intramuscular fat content and carcass weight in pigs  contains four genes that may play important regulatory functions in metabolism and gene expression:  in pigs . The CHDn humans . Finallyn cattle .Pyridoxamine 5’-Phosphate Oxidase (PNPO), G Protein-Coupled Receptor 179 (GPR179), and Rho GTPase Activating Protein 23 (ARHGAP23). The PNPO gene regulates vitamin B6 synthesis and mutations in this gene are known to cause seizures (The second suggestive pleiotropic region on BTA19 (38.2–39.1 Mb) contains seizures . The GPRCAPN, an essential factor in postmortem muscle proteolysis. Numerous polymorphisms in the CAPN-CAST system were identified as associated with meat tenderness in various cattle populations  and several of them are structural proteins. Genes coding for anchoring proteins, previously identified as associated with meat quality traits by ulations . While nionships .Gene ontology and pathway enrichment analyses were performed to gain insight into the genes located within the most significant pleiotropic regions using PANTHER Overrepresentation Test and the DAVID Functional Classification Clustering tools. The PANTHER classification according to protein family and functionally important domains and sites using the INTERPRO database  is preseOverrepresented terms for GO Biological Processes within the most significant pleiotropic regions included “Regulation of Apoptotic Process,” “Regulation of Cell Proliferation,” “Cytokine-Mediated Signaling Pathway,” “Linoleic Acid Metabolic Process,” “Cell Adhesion via Plasma Membrane Adhesion Molecules.” Overrepresented terms for GO Molecular Functions included “Iron Ion Binding,” “Calcium Ion Binding,” “Steroid Hormone Receptor Activity,” “DNA Binding,” “Translation Initiation Factor Activity,” and “Transcription Factor Activity,” Many of these biological pathways were previously reported to be important for carcass quality, meat quality, and tenderness in beef cattle . It is iWeighted ssGWAS single-trait genome-wide associations were used to identify genomic regions with pleiotropic effects on carcass quality, sensory meat quality, and visual meat quality traits in a multibreed Angus–Brahman population. Five genomic regions on BTA5, BTA7, BTA18, and BTA29 explained more than 1% of direct additive genetic variance of two or more carcass quality, sensory meat quality, and visual meat quality traits. Moreover, three other suggestive pleiotropic regions were identified on BTA10 and BTA19. A total of 317 genes were identified across all pleiotropic regions. Many of the candidate pleiotropic genes encode anchoring or cytoskeletal proteins, important factors in muscle proteolysis, and key players in cell growth, muscle development, lipid metabolism and fat deposition. A functional analysis of the genes identified in the pleiotropic regions revealed GO terms directly related to carcass quality, meat quality, and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell–cell adhesion. Results presented here contribute with novel information on the complex architecture of direct additive genetic correlation between carcass and meat quality traits in crossbred beef cattle.PRJEB24746.Publicly available datasets were analyzed in this study. This data can be found here: European Variation Archive website, accession number The animal study was reviewed and approved by University of Florida Institutional Animal Care and Use Committee.FR and RM conceived and designed the study. FR, ER, and RM performed the data analyses and drafted the manuscript. ME edited the manuscript. JL-G, ME, DJ, CC, and RM assisted with data collection. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose SCORE , a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale; it achieves a  Genetic correlation is an important parameter that quantifies the genetic basis that is shared across two traits. Estimates of genetic correlation can reveal pleiotropy, uncover novel biological pathways underlying diseases, and improve the accuracy of genetic prediction.While traditionally reliant on family studies, the availability of genome-wide genetic data has led to several approaches to estimate genetic correlation from these datasets.,While GREML methods need individual-level data, several methods,sketch of the genotype matrix, i.e., by multiplying the genotype matrix with a small number of random vectors.We propose, SCORE , a randomized method of moments (MoM) estimator of genetic correlations among traits via individual genotypes that can scale to the dataset sizes typical of the UK Biobank. While SCORE can estimate the heritability of traits as well as the genetic correlation between pairs of traits, we focus on the problem of estimating genetic correlation in this work. SCORE achieves scalability by avoiding explicit computation of the genetic relationship matrix (GRM). Instead, we show that the genetic correlation can be computed by using a In simulations, we show that SCORE yields accurate estimates of genetic correlation across a range of genetic architectures . Relative to summary-statistic methods that can be applied to biobank-scale data, SCORE obtains a reduction in standard error of N individuals i among samples measured for the first phenotype and j in samples measured for the second phenotype refer to the same individual and 0 otherwise. We define M.We describe our model in the general setting, where the traits are not observed on the same set of individuals. Assume we have We assume the following model relating a pair of traits For the SNP effects, we assume tHere, The genetic correlation parameter SCORE uses a method of moments (MoM) estimator to estimate the parameters Because the mean of Here, We obtain the MoM estimator by minimizing the sum of squared differences between the population and empirical covariances:The MoM estimator for the genetic covariance satisfies the normal equationsGiven MoM estimates of the variance components, the MoM estimate of the genetic correlation is given by the plug-in estimate:Naive computation of the MoM estimate of genetic covariance requires computing To overcome this computational bottleneck, we replace B random vectors, Given We obtain the SCORE estimator B vectors resulting in a runtime of ,Here, In the setting where the two traits are measured on the same set of individuals, we can estimate the −OVERLAP .We performed simulations on a subset of Given the genotypes, we simulated pairs of traits under varying genetic architectures. Our first set of architectures assume an infinitesimal model . We varied genetic correlation m, we specify a causal status, m has a non-zero effect on each of the two traits. For medium polygenicity, causal status at SNP m is drawn independently according to the following distribution: Our next set of non-infinitesimal architectures explore traits with medium polygenicity and low polygenicity. For each SNP m on each trait is drawn from the following distribution:The effect size For low polygenicity, we set the probability m on both traits are drawn from the following distribution:The effect size We vary We simulated traits under an infinitesimal architecture with Given To simulate binary traits, we converted the second trait to a binary trait by thresholding the underlying continuous trait such that the prevalence varied across LD scores were computed from Summary statistics input to LDSC were generated with PLINK. We used linear regression to generate summary statistics for continuous traits and categorical traits and logistic regression for binary traits. In computing summary statistics for traits in the UK Biobank, we include the following covariates: age, gender, principal components 1–10, assessment center, and genotype measurement batch. We used the same covariates as input to SCORE.We ran LDSC under default settings with an unconstrained intercept. In addition to summary statistics, HDL requires eigenvectors of the LD matrix. We used the eigenvectors that preserve We restricted our analysis to SNPs genotyped on the UK Biobank Axiom array, filtering out markers that had high missingness rate We performed similar quality control on the imputed genotypes in the UK Biobank: filtering out markers with high missingness rate We chose traits that have missingness tabolism .We performed simulations to compare the accuracy of SCORE to other estimators of genetic correlation under different genetic architectures. Specifically, we compared SCORE to methods that use individual data  on the estimates of The simulations assume that the two traits are measured on the same set of individuals so that both SCORE and SCORE-OVERLAP can be applied in this setting. Because SCORE is a randomized estimator, we first examined the choice of the number of random vectors  depend on the time needed to compute LD scores and summary statistics of each trait. In addition, HDL also requires the computation of the singular value decomposition (SVD) of LD matrices, which is a computationally expensive step. Thus, we do not include runtimes for LDSC and HDL in these comparisons. We varied the number of individuals, while the number of SNPs was fixed at We applied SCORE to estimate 207 SNPs . We compwere N/A . While tCORE see  and S6. We obtain concordant results when analyzing all pairs in our initial set of 40 traits. Although the point estimates of SCORE and LDSC are highly correlated  and serum biomarkers . Serum liver enzyme levels, including ALT and AST, are markers of liver health and hepatic dysfunction, and they have been shown to be associated with cardiovascular disease,We have described SCORE, a scalable and accurate estimator of genetic correlation. We observe that the estimates of genetic correlation obtained by SCORE have accuracy comparable to GCTA-GREMLThe statistical accuracy gain of SCORE relative to LDSC and HDL can be attributed to several factors. LDSC does not use all the available covariances among the summary statistics choosing to only model the variance. The LD information as summarized by the LD scores involve a number of approximations. Typically, LD scores are computed from an external reference panel. Even when in-sample LD is used (as we have here), computational considerations lead to the LD scores’ being computed from a subset of the samples and restricted to SNPs that fall within a fixed-length genomic window. While HDL models the covariance structure among the summary statistics, thereby utilizing additional information relative to LDSC, HDL relies on approximate computations of LD scores like LDSC. To enable computational efficiency, HDL also uses a truncated SVD of the LD score matrix that can potentially further reduce accuracy.We discuss several limitations of SCORE. First, SCORE requires access to individual genotype and trait data. Summary-statistic methods such as LDSC and HDL have the advantage of being applicable in settings where access to individual-level data can be challenging. While summary-statistic methods also have the advantage of being relatively efficient, it is important to keep in mind that the summary statistics are dependent on specific choices of marker sets and covariates. Applying these methods to different sets of covariates and marker sets requires regenerating the summary statistics (and auxiliary information such as LD score matrices). Second, the model underlying SCORE assumes a quantitative trait. We have shown empirically that SCORE provides accurate estimates of genetic correlation when applied to binary traits provided the traits are not too rare (prevalence


Economically important milk production traits including milk volume, milk fat and protein yield vary considerably across dairy goats in New Zealand. A significant portion of the variation is attributable to genetic variation. Discovery of genetic markers linked to milk production traits can be utilised to drive selection of high-performance animals.A previously reported genome wide association study across dairy goats in New Zealand identified a quantitative trait locus (QTL) located on chromosome 19. The most significantly associated single nucleotide polymorphism (SNP) marker for this locus is located at position 26,610,610 (SNP marker rs268292132). This locus is associated with multiple milk production traits including fat, protein and volume. The predicted effect of selection for the beneficial haplotype would result in an average production increase of 2.2 kg fat, 1.9 kg protein and 73.6 kg milk yield.An outstanding question was whether selection for the beneficial allele would co-select for any negative pleiotropic effects. An adverse relationship between milk production and udder health traits has been reported at this locus. Therefore, a genome wide association study was undertaken looking for loci associated with udder traits.The QTL and production associated marker rs268292132 was identified in this study to also be associated with several goat udder traits including udder depth (UD), fore udder attachment (FUA) and rear udder attachment (RUA). Our study replicates the negative relationship between production and udder traits with the high production allele at position 19:26,610,610 (SNP marker rs268292132) associated with an adverse change in UD, FUA and RUA.Our study has confirmed the negative relationship between udder traits and production traits in the NZ goat population. We have found that the frequency of the high production allele is relatively high in the NZ goat population, indicating that its effect on udder conformation is not significantly detrimental on animal health. It will however be important to monitor udder conformation as the chromosome 19 locus is progressively implemented for marker assisted selection. It will also be of interest to determine if the gene underlying the production QTL has a direct effect on mammary gland morphology or whether the changes observed are a consequence of the increased milk volume.The online version contains supplementary material available at 10.1186/s40104-021-00667-y. DGAT1) gene dramatically reduced milk fat content but caused scouring (non-bloody watery diarrhoea), impaired growth rate and flattened intestinal microvilli [MRC2) gene increased muscularity in meat breeds but caused Crooked Tail Syndrome [Genetic gain focused on production for the dairy goat population in New Zealand is limited largely to within farm selection and some buck transfers between farms. This has resulted in considerable unrealised marker driven selection potential which led to the search for and discovery of quantitative trait loci (QTL) for milk production traits in a subset of the population. In particular, the most significant QTL identified was located on chromosome 19 associated with all three production traits including fat yield, protein yield and milk volume , 3 and Bcrovilli . FurtherSyndrome , 7.Goat udder traits and teat morphology play important roles in milk quality (somatic cell count), milking ability/ease of milking  and overA total of 1058 animals from 2 farms  with mixed age  and of primiparous or multiparous status  were scored for udder traits in February and March 2020. The New Zealand goat herd is a mixed breed population comprised of approximately 85% Saanen, 5% Toggenburg, 5% Nubian/Anglo-Nubian, 4.5% Alpine and 0.5% Sable . The uddSkin samples from the 1058 phenotyped animals were collected and genotyped with the Illumina GoatSNP50 BeadChip with 53,347 SNP markers  . MarkersGenome wide association mapping with an applied mixed linear model accounting for differences in population structure was performed using genome-wide complex trait analysis tool (GCTA) v1.93.2 , 21. They is the phenotype of interest, a is the mean term, b is the fixed (additive) effect of the SNP, x is the SNP genotype, g− is the effect of population structure estimated by calculating genetic relationship matrices (GRM) between all animals [e is the residual effect. animals . Mixed l10P-values exceeded -log10(5 × 10− 8). SNP marker trait association plots against the ARS1 goat reference [A SNP was considered significantly associated to a trait of interest at the whole genome level if their -logeference  were coneference .Allelic effect sizes for marker rs268292132 were calculated for production and udder traits in the 1058 animal population using a linear regression model adjusted for age and farm origin. A one-way analysis of variance (ANOVA) test between the means (α = 0.05) was performed to characterise allelic effect differences between rs268292132 genotype groups presented in Fig. https://broadinstitute.github.io/picard/). Reads aligned around small insertions/deletions were realigned using the Genome Analysis Toolkit  [2 values between variants were calculated using PLINK software [https://www.ensembl.org/vep) was used to annotate variants with their calculated variant consequences based on the ARS1 reference General Feature Format (GFF) file (http://ftp.ensembl.org/pub/release-100/gff3/capra_hircus/Capra_hircus.ARS1.100.gff3.gz).Whole genome sequencing was performed on 302 goats consisting of 48 animals in the cohort used for GWAS, 45 additional animals originating from farms A and B and 209 animals distributed across four other farms. The animals were selected to capture the genetic diversity of each farm. Skin samples were collected from these animals and sequenced on the Illumina HiSeq XTen platform . For each animal, 700–1000 million paired end reads with read pair lengths of 150 nucleotides were generated. An average sequencing depth of 35 × was achieved. Read sequence quality was assessed using FastQC (FastQC v0.11.7). Reads were mapped to the ARS1 goat reference genome assembly using Burrows Wheeler aligner . Duplicar tools) , 26. Pror tools)  and genosoftware . The EnsWe report a single genome wide significant QTL for several udder health traits including udder depth, fore udder attachment and rear udder attachment Table  & Fig. 1An overlapping locus was previously identified to be associated with milk production traits in the French , BritishR2 > 0.8 with the most significant SNP marker, rs268292132. Of these, no high impact variants with severe consequence on protein sequence including transcript ablation, splice acceptor, splice donor, stop gain, frameshift, stop lost and transcript amplification variants were identified , detected at an alternative allele frequency of 0.553 and linkage disequilibrium R2 value of 0.877 with the most significant SNP marker deletes a conserved glutamate (E89del). The variant has not been reported in other goat populations , observed at an alternative allele frequency of 0.566 and linkage disequilibrium R2 value of 0.887 with the most significant SNP marker encodes a substitution of conserved valine 221 to isoleucine . The variant has been reported in the NextGen collaborative research project at an allele frequency of 0.038  exists at an alternative allele frequency of 0.555 and linkage disequilibrium R2 value of 0.87 with the most significant SNP marker in the genome-sequenced animals. This variant encodes a substitution of serine 267/277/281/166  to isoleucine (ENSCHIP00000021646.1:p.S267I/ ENSCHIP00000021662.1:p.S277I/ENSCHIP00000021669.1:p.S281I/ENSCHIP00000021683.1:p.S166I/ENSCHIP00000021685.1:p.S166I). This variant has been reported in the NextGen collaborative research project at an allele frequency of 0.028 , 4.12 kg protein  and 159.52 L volume  was recorded when comparing opposing homozygotes , 0.99 score points in fore udder attachment  and 1.19 score points in rear udder attachment  when selecting for the high production T allele , gamma-glutamyltransferase 6 (GGT6) and the arachiodonate lipoxygenase (ALOX) family of genes [ASGR2) in udder formation [PLD2, GGT6, ASGR1/ASGR2 and ALOX genes.A genome wide association study with an applied mixed model to account for differences in population structure was performed in 1058 dairy goats in New Zealand. The study highlighted a QTL located on chromosome 19 24–29 Mb) associated with udder depth, fore udder attachment and rear udder attachment. This QTL was also identified in a previous study of the dairy goat population in New Zealand to be associated with milk production traits; fat yield, protein yield and milk volume  in the proteasome 20S subunit 6 (PSM6B) gene, and a serine to isoleucine variant (S267I/S277I/S281I/S166I) in the sex hormone binding globulin (SHBG) gene. SIFT scores were predicted at 0.3 and 0.01 respectively suggesting a likely deleterious effect for the SHBG mutation. Genetic variation in human PSM6B was associated with LDL cholesterol and total cholesterol levels in a meta-analysis of 617,303 individuals of multi-ethnic backgrounds with 32 million genotyped and imputed variants [PSMB6 encodes a core component of the 20S proteasome complex involved in intracellular protein degradation. However, its role in lipid metabolism is unclear. In the same GWAS study, genetic variants in SHBG were associated with triglyceride levels [SHBG regulates the plasma metabolic clearance rate of steroid hormones such as oestrogen [PSMB6 a strong functional link with lipid metabolism is not yet determined. Additionally, a GAG glutamate inframe deletion (E89del) was detected in the candidate gene Sentrin-specific protease 3 (SENP3). Limited knowledge exists in the literature on the mechanistic role of SENP3 in milk production or morphological udder development. Given its widespread roles in protein stability, chromatin organisation, transcription, DNA repair, autophagy, protein localisation and homeostasis, a number of potential mechanisms maybe of interest [Whole genome sequence analysis revealed 340 variants within the 5 Mb QTL with linkage disequilibrium interest . AttribuSelection on the chromosome 19 QTL for milk production traits will hold economic value in dairy industries. Markers capturing the QTL effect may be utilised in marker-assisted breeding programmes to drive selection of the next generation of high-performance animals. Employing selection with the most significant marker, rs268292132  was predicted to increase fat yield, protein yield and milk volume in our study population. However, a potential adverse relationship between production traits and udder conformation has been indicated for this locus . Our stuThe overarching goal of this research is development of a marker-assisted breeding programme that can be implemented to accelerate productivity gain. This study identified use of the rs268292132 SNP as a viable selection marker for marker assisted breeding programmes to increase production.Genome wide association analyses across the dairy goat population in New Zealand have discovered a 24-29 Mb QTL on caprine chromosome 19 that has a pleiotropic effect on milk production and udder conformation traits. A negative correlation between production and udder conformation traits was observed in the New Zealand goat population which corroborates reports in French and British dairy goat herds. Close monitoring of udder health is recommended for future selection programmes involving this locus. Marker assisted breeding programmes with markers effectively identifying this QTL can be utilised to select the next generation of high-performance animals to accelerate productivity gain.Additional file 1 Supplementary 1 Full protein sequence alignment between species across several taxa indicating the degree of conservation around the SENP3 E89del (A), PSMB6 V222I (B) and SHBG S267I/S277I/S281I/S166I (C) variant sites (outlined in red). Multiple sequence alignment was performed with ClustalW. NCBI or UniProt accession numbers provided in brackets. Sequence logos were created using WebLogo web based application [lication Additional file 2 Supplementary 2 Summary of the 340 whole genome sequence variants identified from 302 goats located within the chromosome 19 QTL interval with high linkage disequilibrium, R2 > 0.8 with the most significant SNP marker rs268292132Additional file 3 Supplementary 3 Ensembl Variant Effect Predictor (VEP) calculated variant consequences on the 340 variants identified from whole genome sequencing of 302 goats