Bos taurus indicus) have poor reproductive performance compared to other cattle breeds. In this context, genetic progress can be achieved for fertility and reproduction through the use of genomic selection for indicator variables that efficiently assess the biological mechanisms underlying cattle fertility. Therefore, this study aimed to estimate genetic parameters (heritability and genetic correlations) and identify candidate genes associated with anti-Müllerian hormone levels (AMH) and antral follicle populations measured after estrous synchronization (AFP) in Nellore cattle. Our findings indicate that measuring circulating AMH in Nellore cattle enables the identification of animals with high genetic merit for superovulatory responses, as well as early selection of the best oocyte donors for in vitro embryo production. Both traits are heritable and influenced by a large number of important genes. Therefore, AMH and AFP can be used as indicator traits to genetically improve fertility rates in Nellore cattle and to identify better oocyte donors.Reproductive performance has direct economic implications on the beef cattle industry. The majority of Zebu breeds . These findings indicated that these traits can be improved through selective breeding, and substantial indirect genetic gains are expected by selecting for only one of the two traits. A total of 31 genomic regions were shown to be associated with AMH or AFP, and two genomic regions located on BTA1 (64.9–65.0 Mb and 109.1–109.2 Mb) overlapped between the traits. Various candidate genes were identified to be potentially linked to important biological processes such as ovulation, tissue remodeling, and the immune system. Our findings support the use of AMH and AFP as indicator traits to genetically improve fertility rates in Nellore cattle and identify better oocyte donors.Reproductive efficiency plays a major role in the long-term sustainability of livestock industries and can be improved through genetic and genomic selection. This study aimed to estimate genetic parameters (heritability and genetic correlation) and identify genomic regions and candidate genes associated with anti-Müllerian hormone levels (AMH) and antral follicle populations measured after estrous synchronization (AFP) in Nellore cattle. The datasets included phenotypic records for 1099 and 289 Nellore females for AFP and AMH, respectively, high-density single nucleotide polymorphism (SNP) genotypes for 944 animals, and 4129 individuals in the pedigree. The heritability estimates for AMH and AFP were 0.28 ± 0.07 and 0.30 ± 0.09, and the traits were highly and positively genetically correlated ( Bos taurus indicus) compared to Taurine breeds (Bos taurus taurus) [Reproductive performance directly impacts the profitability of the beef cattle industry ,2. Preco taurus) . The maj taurus) . Further taurus) ,6. Despi taurus) ,8. ThereAnti-Müllerian hormone (AMH) regulates follicle selection , as wellGenome-wide association studies (GWAS) are frequently used in livestock studies aiming to detect causal mutations and candidate genes associated with traits of interest ,29,30,31No approval from the local Ethics Committee was required for this study because the datasets were generated in previous experiments approved by the Bioethics Commission of the School of Veterinary Medicine and Animal Sciences of the University of Sao Paulo, Sao Paulo, Brazil ,36.The datasets used were from three commercial beef cattle farms  located in the state of Mato Grosso do Sul in the midwestern region of Brazil. All animals were raised under similar environmental conditions and received mineral supplementation on a pasture-based production system, with ad libitum water. The pedigree dataset included 4129 animals , in which 1032 were founders and 2883 individuals had both parents known. The pedigree spanned up to five generations according to suggestions that the inclusion of at least three generations is enough to predict accurate breeding values and estimate genetic parameters . FurtherA total of 1099 females 425 (cows (16 months of age) and 674 heifers (14 months of age)) were measured for AFP. The total number of visible follicles (≥3 mm in diameter) in both ovaries were evaluated using ultrasonography equipment . Animals were recorded at the beginning of the follicular wave  . The synA total of 944 females (379 cows and 565 heifers) were genotyped using a high-density SNP chip panel  containing 777,962 single nucleotide polymorphisms (SNP). Quality control was performed using the PREGSF90 program . The foly is the vector of individual observations for AMH and AFP, β is the vector associated with the fixed effect of contemporary group and the linear effect of age of the animal at the measurement, α is the vector of direct additive genetic effects, X and Z are the incidence matrices linking records to the β and α vectors, respectively, and e is the vector of the residual effects. The contemporary group was defined by concatenating the effects of the farm (3 levels), management group, birth year (2013–2017), and season of the year during which the measurement was taken. Additive genetic and residual effects were assumed to follow a normal distribution. The ssGBLUP is a modified version of the traditional BLUP, in which the inverse of the pedigree-based relationship matrix , in which p is the observed allele frequency of each marker. The weighting factor can be derived either based on SNP frequencies [G is close to that of A was added to 0.95 of G.A linear animal model and the Average Information Restricted Maximum Likelihood method (AI-REML) were used to estimate variance components, heritability, and genetic correlations using the AIREMLF90 package from the BLUPF90 software ,41. Geno follows ,43:(2)H−stimates , ωA22−1 lated as :G = ZZ′/quencies , or by et of A22 . In ordeGWAS was carried out for each trait based on the weighted ssGBLUP method (WssGBLUP) . The samThe POSTGSF90 program  was usedBioMart tool using the Ensembl Genes and the Bos taurus taurus ARS-UCD1.2 reference genome [p ≤ 0.05; False Discover Rate ≤ 10), using all candidate genes identified for AMH and AFP. Furthermore, important SNPs (from the key genomic windows) were further explored using the Animal QTL Database  [The candidate gene list from the genomic regions that explained at least 1% of genetic variance was annotated considering upstream and downstream intervals of 100 kb (threshold defined based on the level of linkage disequilibrium in the population) via the e genome . The DAVe genome  softwarealQTLdb) .Gr = 0.81 ± 0.02).The descriptive statistics and genetic parameter estimates for AMH and AFP are presented in A total of 13 genomic regions located on BTA1, BTA3, BTA5, BTA7, BTA8, BTA10, BTA11, BTA18, BTA22, and BTA25 were identified for AMH . AdditioThe chromosomes BTA5 and BTA7 demonstrated the most important genomic regions associated with AMH. For AFP, the two highest peaks were identified on BTA1 (at 109.1 Mb) and BTA26 (at 45.7 Mb), which accounted for 6.24% and 7.33% of the total additive genetic variance, respectively. The highest peak for AMH was found on BTA5 (at 97.2 Mb) explaining 5.22% of the total additive genetic variance. Two overlapping regions located on BTA1 (64.9–65.0 Mb and 109.1–109.2 Mb) were found for the two traits, as illustrated in PLXNC1) and four  positional genes were detected on BTA5, located around 24.0 Mb and between 97.1 and 97.4 Mb, respectively. A group of genes  were found to be harbored within the genomic region located on BTA26 for AFP. The overlapping regions described above shared three candidate genes, namely, RSRC1, GPR156, and LRRC58. Five biological processes and one pathway were enriched (p ≤ 0.05), including pathways associated with the ovulation process  genetic correlation between AMH and AFP, which was in agreement with our current findings, highlighting the importance of measuring AMH concentration to identify precocious cows through follicular and ovulatory responses [The circulating concentrations of AMH are highly variable among mammalian species. In the current study, the AMH mean agreed with Zebu cattle reports, which ranged from 1.20 to 1.60 ng/mL ,24,48. Tesponses . AFP wash2 = 0.28 ± 0.07) was lower when compared to the literature reports, however, a lower standard error was also observed in the current study. This moderate heritability indicated that substantial genetic progress could be achieved through direct genetic selection.Recently, Nawaz et al.  obtainedTGF-β family involved in follicular development, cell proliferation, steroidogenesis, and ovulation.Júnior et al.  reportedADAM29, CTSB, SOX7, PPP1CB, CLIP4, and CCK) associated with fertility traits. The significant peak on BTA26 around 46 Mb harbors the gene ADAM12 , which is associated with several biological activities, such as regulating remodeling of extracellular matrix, modulation of cell morphological changes, satellite cell activation, regulation of myogenesis and adipogenesis in beef cattle [TGF-β1 [ADAM9, ADAM10, and ADAM12) together seem to regulate the breakdown of matrix and differentiation of granulosa cells prior to ovulation [BAG3 (BTA26:39.7–39.8 Mb) and TIAL1 (BTA26:39.6–39.7 Mb) genes were also annotated. TIAL1 acts on post-transcriptional regulation encoding protein members of the RNA binding family, and has also been associated to the development of primordial germ cells of gametes  in rodents [BAG3 was annotated in proximity to a QTL affecting daughter pregnancy rate in Nellore beef cattle [Similar to our findings, three genomic windows located in BTA8 (4.16–7.66 Mb), BTA11 69.4–73.6 Mb), and BTA22 (11.9–15.9 Mb) were associated with AFP in Nellore heifers . These rf cattle , and reg [TGF-β1 , a gene  [TGF-β1 . Evidenc [TGF-β1 ,61. The vulation . The BAG rodents . In addi.6 Mb, anIL1R2, SERINC5, PLXNC1, THBS4, and CBS) are implicated in the ovulation process, tissue remodeling, immune system, and embryo preimplantation [PLXNC1 (Plexin C1) is associated with the immune system [IL1R2 is a glycoprotein expressed on monocytes, neutrophils, and T and B lymphocytes. Part of the interleukin family, the type 2 form of IL-1, was extensively related to ovulation during follicular growth [THBS4 is a calcium-binding protein that modulates cellular phenotype during tissue genesis and remodeling [p = 0.02). The genes CNTFR and ATP1A1 are associated with productive efficiency  [ATP1A1 was reported to be related to heat tolerance [ATP1A1 may also influence mastitis resistance [The enrichment analysis  highlighantation ,70. PLXNe system  and beloe system . The maie system . Particur growth . THBS4 imodeling . In thisiciency)  and heaticiency) ,76,77 inolerance , in whicsistance .The KEGG protein processing in the Mitogen-Activated Protein Kinase (MAPK) signaling pathway is involved in a conserved module that controls various cellular events and biological processes, such as embryogenesis, cell differentiation, cell proliferation, cell death, short-term changes required for homeostasis, and acute hormonal responses ,80,81. AAMHR2) gene was not identified as significant in this study, but was reported as an important gene related to AMH [AMH gene and early pregnancy occurrence and age at first calving in Nellore cattle, but also did not find significant additive effects of the three SNPs investigated.There are known QTLs located in the genomic regions identified in this study, including associations with calving performance  . In agred to AMH ,92,93. Sd to AMH  investigDespite the fact that AMH and AFP are highly genetic correlated traits (0.81 ± 0.02), a limited number of overlapping genomic regions were identified, indicating that the genes with pleiotropic effects for AMH and AFP might be genes with small effects. Furthermore, based on the heritability estimates and large number of genomic regions with small effects, AMH and AFP seem to be traits of complex inheritance regulated by a large number of small-effect genes.Genetic selection for AMH and AFP in Nellore cattle is expected to improve reproductive performance, especially superovulatory responses. Furthermore, breeding values for these traits can be used to select the best oocyte donors for in vitro embryo production. The number of genotyped animals with phenotypes for AMH is a limiting factor that needs to be increased in future studies, as this impacts the identification of important genomic regions with smaller effects. However, the use of a high-density SNP panel may have minimized this issue, as the average linkage disequilibrium between markers was greater compared to a lower density SNP panel. Further studies using datasets from independent populations, as well as larger datasets in this current population, should be performed to validate the findings of this study. AFP can be measured at a reasonably low cost per individual and, as AMH and AFP are highly and positively genetic correlated, AFP could be a feasible indicator for inclusion in breeding schemes.Our findings indicated that anti-Müllerian hormone levels and antral follicle populations (measured after a synchronization protocol) are heritable traits in Nellore cattle and can be improved through genetic and genomic selection. Furthermore, breeding values for both traits can be used to identify females that best respond to superovulation that are better oocyte donors for in vitro embryo production. Substantial indirect genetic gains are expected by selecting for only one of the two traits, as a high positive correlation was observed between them. A total of 31 genomic regions were identified to be associated with AMH or AFP, and two genomic regions located on BTA1 overlapped between the traits. Various candidate genes were identified to be potentially linked to important biological processes such as ovulation, tissue remodeling, and the immune system. Anti-Müllerian hormone levels and antral follicle populations could be used as indicator traits to genetically improve fertility rates in Nellore cattle and to identify better oocyte donors. Future studies should also investigate the genomic backgrounds of these traits in the absence of an estrous synchronization protocol.


This study uses a Mendelian randomization approach to resolve the difficulties of identifying the causal relationship between height and earnings by using a unique sample of 3,427 respondents from mainland China with sociodemographic information linked to individual genotyping data. Exploiting genetic variations to create instrumental variables for observed height, we find that while OLS regressions yield that an additional centimeter in height is associated with a 10–13% increase in one’s annual earnings, IV estimates reveal only an insubstantial causal effect of height. Further analyses suggest that the observed height premium is likely to pick up the impacts of several cognitive/noncognitive skills on earnings confounded in previous studies, such as mental health, risk preference, and personality factors. Our study is the first empirical study that employs genetic IVs in developing countries, and our results contribute to the recent debate on the mechanism of height premium. Height is widely believed to be a key determinant of professional success. Indeed, taller people are usually found to earn more in both developed countries –5 and deThese differing interpretations point into vastly different directions of policy-making. If labor-market discrimination is at work, then policies aiming to prevent discrimination may be needed to protect employee welfare. In contrast, if the observed height premium is, in fact, capturing the impact of some hard-to-observe factors, such as emotional stability and perseverance, then using height as a marker to gauge returns to these factors is justifiable (and may even be desirable). Therefore, to better inform policy in response to inequality, it is crucial to investigate the origins and driving channels of the observed height premium. Such an investigation is of particular interest in transitory economies, where the role of height as a productive factor is rapidly changing, as a result of structural transformations undergoing in the labor market  and in the economy as a whole .China provides an interesting case to study. Partly due to China’s market-oriented reform, employment opportunities and wage determination rely more on the market rather than institutional and political factors –18, whicNeedless to say, estimating the causal effect of height on wages using observational data is challenging, as it is difficult to control for all potential confounders in estimation. A standard solution is to find instrumental variables (IVs) for height, i.e., exogenous variables that affect the wage only through their impacts on height, to achieve identification. Some researchers exploited twinning experiments to identify the impact of height . Since iTaking another path, this paper adopts an IV strategy based on genetic information to achieve identification. Similar to those recently adopted by von Hinke et al. , TyrrellBy analyzing a unique dataset recently collected in China, we find that the statistically and economically significant height premium estimated by OLS regressions, i.e., an additional centimeter in height is associated with a 10–13% increase in one’s annual earnings, largely reflects impacts of other factors. While our OLS estimates are similar to their counterparts in most previous studies conducted in China –21, our Our analysis makes three contributions to the literature. First, our study is the first height-premium study that employs genetic IVs in China, if not the first in developing countries. Not only can our findings provide a deeper understanding of the working of China’s labor market, but they are also complementary to findings in other, more developed countries –25. SecoThe remainder of this paper is structured as follows. The next section briefly reviews the relevant literature, paying particular attention to the channels through which height may affect one’s wage income. Section 3 describes the data. Section 4 details our empirical methods. Section 5 presents our main empirical results. The final section draws conclusions and points out a number of directions for future research.To better understand what genetic IVs can identify, it is helpful to consider first the potential channels through which height may affect wages. Thus far, four channels have been put forward in the literature. First of all, height itself may be a productive factor, especially in developing countries where many jobs value physical strength and endurance of physical labor –33. Yet generator of some other productive factors. For example, taller people may enjoy more social dominance [perception and expectation. For example, taller people may be perceived as more productive by employers [Secondly, height may be the ominance –35 and hominance , all of ominance . The heimployers . The permployers . Thus taper se to act as confounders in a conventional height-wage regression. As illustrated in Case and Paxson [endowment, say, insulin-like growth factors that stimulate simultaneous neural and physical growth [Thirdly, height may be correlated with productive factors that are not generated by itself. The most well-known examples include cognitive skills  and non-d Paxson , for exal growth –42, whicl growth . Height l growth , for exaFinally, the observed height premium may simply reflect pure discrimination against shorter employees in the labor market –12,43. UWhile earlier studies tend to examine what the height premium captures in an indirect manner, estimating OLS models that control for variables capturing impacts through the afore-mentioned channels, more-recent studies tend to estimate the impact of genetic height on wages more directly. The recent decade witnessed a new strand of studies that employ variations in one’s genetic markups induced by Mendelian randomization to identify the impact of height –25, whicThe present paper also exploits genetic variations to identify the causal impact of height in the context of China for the first time. We control for several personality characteristics as well as additional genetic factors, which allows us to see more clearly the role of height in wage determination.Our empirical analysis is based on a unique dataset collected in collaboration with WeGene, a leading private genetic testing company founded in 2014 in Shenzhen, China. The study has been approved by the Institutional Review Board of College of Economics and Management at China Agricultural University . All participants of this study were drawn from the customer database of WeGene, all of whom provided (signed) informed consent and participated in the online genoeconomic survey sometime between April 2018 and January 2019. Approximately 3,600 participants completed the full survey. The online survey collected information on each participant’s demographic characteristics such as height, year and month of birth, their socioeconomic characteristics such as annual income and educational attainment, as well as their preferences such as risk attitude, altruism and trust –45.Important to our study, all sample subjects were genotyped on a WeGene custom genotyping array platform (AffyMetrix). Imputation and quality control were performed by WeGene using PLINK (1.90 Beta), SHAPEIT (v2.17) and IMPUTE2 (v2.3.1). A total of 10,670,107 Single Nucleotide Polymorphisms (SNPs) were identified for each participant, which were then used to construct polygenic scores and genetic instrumental variables. Respondents’ economic preferences  were assessed using the Global Preferences Survey on a 10-point scale .http://www.chinadaily.com.cn/a/201805/21/WS5b02d4d6a3103f6866ee9b15.html). The average educational attainment of an employee is 10.2 years in 2015 (Source: http://www.gov.cn/xinwen/2017-07/25/content_5213292.htm). The spatial distribution of respondents’ home provinces, shown in Excluding individuals who were younger than the age of 16, older than 60, or still a student at the time of the survey from the original dataset yields an analytical sample with 3,427 observations . Our empirical analysis mainly consists of two related parts. The first part measures the potential height premium in China’s urban labor market. The second part then seeks to explain what the observed height premium really captures by (a) sequentially adding a set of covariates in the model, and (b) instrumenting height using the genetic IV.Y is one’s annual earnings (in log), H is his or her adult height measured in centimeters (cm), X is a set of personal characteristics to be detailed below, and u is an error term capturing the influence of all unobserved factors. If Eq  techniques. However, as discussed above, in many situations, OLS estimates of β1 may capture something other than the causal effect of height.As a starting point, consider a statistical model that links one’s wage income and adult height:Hukou) status, political affiliation, as well as dummies for occupation, industry, and firm ownership [choice variables that can be influenced by height or are jointly determined with height, and thus the inclusion of these variables may yield misleading results. Therefore, we adopt a specification similar to that used by Persico et al. [Assuming away identification problems with OLS for the moment, one subtle issue in estimating the height premium is: What covariates should be included in Eq ? A numbewnership –21, as cwnership , Heckmanwnership , and Perwnership , many ofo et al.  as the bo et al. , depresso et al. , delay do et al. , and repo et al. . ExpandiX) but are orthogonal to potential confounders, to eliminate the influence of confounders in estimating Equ  based on 697 SNPs previously identified to be associated with adult height in human beings [PGS_Heighti is calculated as the sum of genotyped or imputed allele j dosages carried by a respondent i  multiplied by the estimated effect size (βj) reported by Wood et al. [j,iSNP can be 0, 1, or 2. PGS_Height could range from low to high, and the higher score that an individual has, the more likely that he/she would be taller.It is worth noting that there is no n beings . Mathemad et al.   determined or triggered by height, such as social dominance and self-esteem discussed in Section 2.1. In this sense, the ‘causal’ effect of height identified using G = PGS_Heighti is the genetic IV, along with Eq  for those individuals who are taller because they have the particular genes that made them tall. In contrast, the impact of height identified in many previous studies in developing countries using shocks to childhood nutrition  captures the LATE for those individuals who are shorter because they encountered these shocks.Note finally that the above framework Eqs –3 implice et al. ,23 formae et al.  and Angre et al.  and provPGS_Height rely on several critical criteria that we discuss in detail below [The validity and credibility of il below ,61–63.The relevance criterion requires that the genetic IV of PGS_Height must be correlated with the endogenous variable of observed height. In our data, the explanatory power of PGS_Height is 0.0459, comparable to the 0.048 that Böckerman and colleagues found using a Finnish sample . We furtThe independence criterion requires that the genetic IV of PGS_Height must not be correlated with unmeasured confounders through population stratification. We first divide our analytical sample into two groups: the higher PGS_Height subsample with an above-average value and the lower PGS_Height subsample with a below-average value of the polygenic score. We then assess whether the two groups differ in major observable characteristics significantly. As reported in As a second strategy to address the potential population stratification problem, we include a total of 42 individual ancestral composition variables based on each respondent’s genetic data as additional control variables . In our data, individual ancestry composition is estimated by using the ADMIXTURE program, developed by the Department of Human Genetics, University of California Los Angeles –65. TablThe exclusion criterion requires that the genetic IV of PGS_Height have no direct effect on income through pleiotropy. In our design, if some of the SNPs used to construct PGS_Height can directly affect the outcome variable of interest , then the exclusion condition of the genetic IV would be violated . AlthougPGS_Height), while excluding the endogenous explanatory variable, Height, from the model. If height does not have a causal effect on (log) earnings, then one would expect the genetic score of height to have little impact on (log) earnings. The results, reported in PGS_Height in all three specifications are small and statistically insignificant, suggesting little predictive power of the genetic IV for the outcome variable.First, we follow VanderWeele and colleagues  and estiHeight in the OLS and the IV models. If our genetic IV satisfies the exclusion restriction, one would expect to see = 0.0103) and Second, we compare the magnitude of the coefficients of d income . Our resIt is worth noting that Böckerman et al. and Palmer et al. point out that the use of polygenic scores can significantly reduce the risk of pleiotropy compared with a single gene variant or SNP ,66. BöckX, we include personality traits  and additional genetic controls  for two reasons. First, personality characteristics  have been found to play crucial roles in determining one’s labor market success [In  success ,67–68. S success . Althoug success . SimilarTuring to our empirical results, this section first documents the existence of a height premium using OLS regressions, with various specifications that have been adopted in previous studies. By sequentially adding more explanatory variables in the model, these OLS estimates help shed light on what is being captured in the estimated height premium. We then use the genetic IV to estimate the causal effect of height, to see how genetic height affects one’s earnings while netting out the influence of confounding factors.Model 1 and 2 presents results for the full sample. As a benchmark, Model 1 controls for gender, age, years of schooling, province fixed effects, and individual ancestral composition. It estimates that one additional cm in height is associated with a 1.30% increase in annual salary income, which is similar to the findings of Gao and Smyth  and YamaPGS_Height). Model 2 further includes a set of personality traits  and additional genetic markers  that can potentially affect one’s labor-market performance in the regression. The estimated height premium declines–a 1-cm increase in height is associated with a 1.03% increase in annual income. It is worth noting that higher risk-loving preference and lower polygenic score of delay discounting  are positively associated with annual income. Results in Model 3 and 4 of Taken together, the OLS results reported in PGS_Height) is used as the sole instrument for observed height. An important finding emerging from PGS_Height, has a very strong predictive power for height, with an associated F-statistic of 85.1053, far exceeding the rule-of-thumb value of 10 [More specifically, the first model of full sample (column 1) suggests that the impact of height on annual income identified by Mendelian randomization of height-related genes upon conception is very small compared to its OLS counterpart  as an additional genetic IV to the above models again. The key results remain robust , implying a linear effect of PGS_Height on observed height. It is worth pointing out that the use of two genetic IVs facilitates an overidentification test for the validity of the exclusion restriction. For example, in Model 1 of the full sample (p = 0.1323) reveal no sign of violation of the exclusion restriction, lending further support to the IV validity of PGS_Height.As additional checks, we perform 2SLS by including the quadratic form of The income disparities associated with one’s physical appearance  have motivated a large body of studies that seek to identify the underlying causal mechanisms . This paper se, which suggests that discrimination from the workplace may not be a crucial component to wage disparity with respect to height, we interpret these results as empirical support of channels through which both cognitive and non-cognitive skills are correlated with height during individual development. In other words, the observed height may act as a signal of beneficial circumstances for developing higher cognitive/non-cognitive skills during childhood or early-life [While we find an economically insubstantial premium of height rly-life . This napositive selection from the realm of evolutionary biology, in which beneficial traits (i.e. those that can make carriers survive and reproduce more successfully) tend to be selected by exogenous environments and become more frequent in populations over time [Before closing, there are several limitations of the present study. First, the current study is based on a sample of 3,427 observations, which may lack statistical power due to small sample size . Second,ver time . Future S1 Appendix(DOCX)Click here for additional data file.


Family and twin studies of Borderline Personality Disorder (BPD) have found familial aggregation and genetic propensity for BPD, but estimates vary widely. Large-scale family studies of clinically diagnosed BPD are lacking. Therefore, we performed a total-population study estimating the familial aggregation and heritability of clinically diagnosed BPD. We followed 1,851,755 individuals born 1973–1993 in linked Swedish national registries. BPD-diagnosis was ascertained between 1997 and 2013, 11,665 received a BPD-diagnosis. We identified relatives and estimated sex and birth year adjusted hazard ratios, i.e., the rate of BPD-diagnoses in relatives to individuals with BPD-diagnosis compared to individuals with unaffected relatives, and used structural equation modeling to estimate heritability. The familial association decreased along with genetic relatedness. The hazard ratio was 11.5  = 1.6–83.8) for monozygotic twins; 7.4 (95% CI = 1.0–55.3) for dizygotic twins; 4.7 (95% CI = 3.9–5.6) for full siblings; 2.1 (95% CI = 1.5–3.0) for maternal half-siblings; 1.3 (95% CI = 0.9–2.1) for paternal half-siblings; 1.7 (95% CI = 1.4–2.0) for cousins whose parents were full siblings; 1.1 (95% CI = 0.7–1.8) for cousins whose parents were maternal half-siblings; and 1.9 (95% CI = 1.2–2.9) for cousins whose parents were paternal half-siblings. Heritability was estimated at 46% (95% CI = 39–53), and the remaining variance was explained by individually unique environmental factors. Our findings pave the way for further research into specific genetic variants, unique environmental factors implicated, and their interplay in risk for BPD. Borderline personality disorder (BPD) is a complex psychiatric disorder characterized by emotional dysregulation , 2, seveThe causes of BPD are poorly understood  but it hTwin and extended twin studies, including relatives of twins, enable exploration of the relative contribution of genetic and environmental factors to familial aggregation . PreviouIn summary, due to heterogeneous assessment procedures, questionable diagnostic validity, risk for biased estimates, and underpowered studies due to small sample sizes, there is considerable uncertainty regarding the familiality and heritability of BPD. Further, it is unclear how the results of many studies relate to clinically diagnosed BPD. To provide more precise and reliable estimates of familial clustering and heritability of BPD, we conducted a population-based familial aggregation study of more than 1.8 million individuals, including 11,665 individuals with clinically diagnosed BPD. We also applied quantitative genetic modeling in order to disentangle the genetic and environmental contributions. In addition, we examined possible sex differences in the patterns of familiality of BPD.We performed a register linkage of the National Patient Register (NPR) , the MulWe identified 2,181,047 individuals born between 1973 and 1993 through the MBR. Individuals born with congenital malformation , who died (9121) or emigrated  before their 15th birthday or 1 Jan 1997, and whose biological parents could not be identified , were excluded. In all, our analytical sample comprised 1,851,755 individuals.Using the MGR and the STR we identified all pairs of following relatives types  monozygotic (MZ) twins (100%), dizygotic (DZ) twins (50%), full siblings (50%), maternal half-siblings (25%), paternal half-siblings (25%), cousins whose parents were full siblings (12.5%), and cousins whose parents were maternal half-siblings and paternal half-siblings (6.25%). We paired every individual with each relative of the different types that were identifiable, the relative indexing the exposure and the individual the outcome. Thus, each relative pair was included twice, once with an individual as exposure person and once as outcome person.th birthday, in the NPR from 1997 (when ICD-10 was introduced in Sweden). NPR includes inpatient data throughout, and outpatient data from 2001. Our linkage was updated through 2013. In the Swedish version of the ICD-10, the F60.3 diagnosis code corresponds to the DSM-IV-TR criteria for BPD [We identified individuals with BPD using first date of International Classification of Diseases 10th revision (ICD-10) diagnoses for Emotionally Unstable Personality Disorder (ICD-10 code F60.3) after their 15 for BPD . We have for BPD .To adjust for cohort effects, birth year was included as a potential confounder and treated as a categorical variable . Furthermore, to account for potential incidence and prevalence differences between men and women, sex was included in the analyses as a covariate.In addition, we identified ICD-10 diagnoses of psychiatric disorders and self-harm from the NPR clustered into broad groups  by allowing delayed entry into the at-risk group in analyses. Informed by previous studies , 47 we uConfidence intervals were obtained using a cluster robust sandwich estimator to adjust for non-independence between relative pairs.We used a quantitative genetic approach similar to previous studies , 47, whiWe used the statistical software R and packages survival , drgee , polycorFor full siblings, we performed the familial aggregation analyses separately for sex-combinations of exposure and outcome individuals, as well as separately per birth year categories.Among females only, we calculated concordance rates and tetrachoric correlations for female siblings in a subsample identified equivalently as outlined above. Finally, we performed quantitative genetic analysis on females only using the same subsample.Table The cumulative incidence of BPD was approximately 0.5%  by age 25 and increased to over 1%  by age 40 . Cousins whose parents were full siblings had a HR of 1.7 (95% CI = 1.4–2.0), for cousins whose parents were maternal half-siblings it was 1.1 (95% CI = 0.7–1.8); and, for cousins whose parents were paternal half-siblings the HR was 1.9 (95% CI = 1.2–2.9).Results of familial aggregation calculated through Cox regression are presented in Fig. In Supplemental eTable Concordance rates and tetrachoric correlations in subsampled pairs of siblings are presented in Table In Table p-value = 0.020; Supplemental eTable 3). However, the support for sex differences did not permeate the analyses; none of the other comparisons between exposure-outcome combinations of sex was statistically significant. For birth years, the association was strongest for the oldest cohort, and weakest for the cohort born 1983–1987 .In the analyses of familial aggregation among full siblings, when one sex acted as both exposure and outcome individuals, males had a higher HR than females . Consequently, quantitative genetic analysis on females only yielded very similar result as the main analysis, with an estimate of heritability of 47%  in the AE-model .This is the first total-population study of familial aggregation and heritability of clinically diagnosed BPD. Confirming previous studies, relatives of individuals with a diagnosis of BPD had a higher risk of receiving a BPD-diagnosis than those without familial vulnerability. For example, there was a 4.7 times increased risk for full siblings. Heritability of clinically diagnosed BPD was estimated at 46% (95% CI = 39–53), consistent with previous studies demonstrating the heritability of dimensional BPD-traits in general population twin samples. The best fitting model indicated little or no role for shared familial environmental factors. Thus, the pattern of familial aggregation of BPD across different types of relatives indicates that genetic factors play a significant role in the risk of developing BPD, and explain the familial clustering of the disorder.Previous heritability estimates vary widely –37 and oThe results also estimate a 54% contribution of non-shared, individually unique, environmental factors. Shared environment did not influence the statistical goodness of fit, suggesting that shared familial environmental factors, such as socioeconomics, are unlikely to contribute substantially to the etiological underpinnings of BPD. Traumatic life events, such as sexual or physical abuse and parental divorce or illness, are more frequently reported by individuals with BPD compared to healthy controls or patients with other personality disorders , 55–58. In line with previous research, the pattern of comorbid disorders showed that comorbidity is a hallmark of BPD. One potential cause for high comorbidity rates is genetic overlap since recent research findings indicate high genetic overlap across many psychiatric disorders and a general genetic propensity , 60. ThuOn a mechanistic level, it has been suggested that several underlying neural and cognitive processes involved in emotional regulation are dysfunctional in BPD . The preSince we had categorical yes/no measure of BPD, we necessarily treated it as a categorical variable in analyses, rather than having continuous measures or separate symptoms. Concurrent research has found that many psychiatric diagnoses are representable as the extreme end of a continuous trait , althougThis study has several important strengths including utilizing Swedish nationwide register linkages. Previous studies are often limited by small sample sizes , 34, 36 Our results need to be interpreted in light of some limitations. First, we most likely underestimate the true proportion of individuals with symptom levels and impairments corresponding to BPD-diagnosis . Second, it is possible that we are missing male individuals with BPD in our population sample as the female to male ratio in our study is 5.5:1, whereas a more even sex ratio has been reported in a US community sample assessed by interview . ResultsIn conclusion, BPD aggregates in families and the heritability was estimated at 46%, with the remaining variance explained by non-shared environmental factors. This finding is important for further expanding our understanding of BPD. The time is ripe for identification of genetic variants associated with BPD through large scale genome-wide studies, for identification of environmental risk factors, and of how these correlate or interact to increase the risk of BPD.Supplemental online material


Gossypium hirsutum landraces, obsolete cultivars and modern cultivars based on high throughput genome-wide sequencing of the core set of genotypes. As a result of the genome-wide scan, we identified 93 differential regions and 311 selection sweeps associated with domestication and improvement. Furthermore, we performed genome-wide association studies to identify traits associated with the differential regions and selection sweeps. Our study provides a genetic basis to understand the domestication process in Chinese cotton cultivars. It also provides a comprehensive insight into changes in genome structure due to selection and improvement during the last century. We also identified multiple genome-wide associations (GWAS associations) for fibre yield, quality and other morphological characteristics.Upland cotton is the most economically important fibre crop. The human-mediated selection has resulted in modern upland cultivars with higher yield and better fibre quality. However, changes in genome structure resulted from human-mediated selection are poorly understood. Comparative population genomics offers us tools to dissect the genetic history of domestication and helps to understand the genome-wide effects of human-mediated selection. Hereby, we report a comprehensive assessment of  Gossypium hirsutum) is a major source of fibre for the textile industry, especially tetraploid cotton which covers 95% of the worldwide cotton production. Selection has been carried out in cotton to improve production and adaptation to the local environment, reduced growth period, and its defence against biotic and abiotic factors. Due to continuous selection pressure, the cotton crop is facing a narrowed genetic base in terms of diversity [Cotton : Hebei, Shandong and Henan, northwestern China: Xinjiang province, The Yangtze River Valley (YaRV): Hubei, Hunan, Jiangsu, Anhui . During Improvements in genomic studies and resequencing technologies have established the tools to dissect the genetic basis of elite cultivars. Different techniques have been used to understand genetic diversity in upland cotton ,12,13,14Gossypium hirsutum landraces collected from North America, used in this study are the representation of the breeding history of cotton crop in China over the decades since the introduction of upland cotton in China. This study will provide insight into the differences between the genetic profile of current varieties, accessions collected from southwest China and geographical landraces of G. hirsutum, which could be further utilised in breeding to expand the narrowed genetic base of upland cotton. Though multiple studies have been conducted to understand the genomic basis of domestication in different crops ,23,24, tG. hirsutum (GHL), respectively. Some accessions showed admixed ancestry suggesting the presence of introgression or gene flow during the breeding process  decay was measured as the physical distance on the chromosome (kb) when LD decreased to half of its maximum value. Linkage disequilibrium is critical in understanding and determining the location of causal loci through GWAS [G. hirsutum (GHL) declined dramatically compared with that in modern cultivar (MCl) populations, which are in agreement with previously published statistics [We exploited the genetic relationship among all accessions using principle component analysis (PCA) and performed phylogenetic analysis to construct a phylogenetic tree using 4,329,838 single nucleotide polymorphisms (SNPs). The inference drawn from phylogenetic analysis, structure and PCA supported the classification into three groups . Group 1 process . Phylogeugh GWAS . Furtherugh GWAS . LD decaatistics . FurtherModern upland cultivars have been developed from limited resources  and are Fst) estimates resulted in understanding the differentiation between two groups of cultivars. Comparatively higher differentiation was associated with chromosomes A06, A08, A09 and A11 on sub-genome At  and obsolete cultivars (OCl) was estimated a. Populaenome At a, while b-genome . Selecti signals . A total signals . The ide signals , D02, D1 signals . Besides signals c. G. hirsutum landraces and cultivar groups, we compared the landraces of G. hirsutum with modern cultivars and obsolete accessions. Genome-wide population fixation statistics (Fst) suggested a wide range of genetic differentiation among these groups. G. hirsutum landraces showed significantly higher differentiation than modern cultivars and other obsolete accession collected from Southwest China, which is consistent with the breeding history of cotton   using phenotypic data collected in 2017 and 2018. We selected 4,329,838 high-quality SNPs with minor allele frequency >0.05. The high-density map was found to be superior to previous reports ,27. A to (EMMAX) . Very feGossypium genus includes 50 species distributed worldwide in tropical and subtropical regions. Gossypium hirsutum is the most important species among all due to its high yield and spinnable fibre quality for industrial use [G. hirsutum, dominates worldwide and has been the primary source of fibre production, as it has been growing on more than 90% of cotton-growing areas. Upland cotton originated in Mesoamerica, from where it spread worldwide through trade routes during the 18th century. In the early 20th-century, upland cotton was systematically introduced to cotton-growing countries worldwide, including China, India and Australia, which lead to the development of the modern cotton industry. Since upland cotton was developed from limited resources [G. hirsutum. The rial use . Upland esources ,37, it iesources ,39. A coPhylogenetic, principal component and structure analyses indicated the divergent behaviour of modern cultivars with comparison to OCL and GHL, which were in agreement with genetic differentiation analysis. The divergent trends of landraces compared to modern cultivars are also in agreement with previous studies ,40,41,42G. hirsutum was in accordance with the genetic differentiation analyses. This divergent trend of landraces compared to modern cultivars and obsolete accessions was also found in agreement with previous studies [Furthermore, Obsolete accessions collected from southwest China are a rich source of genetic information for comparison of genetic variation in modern cultivars because of domestication and improvement. These genotypes comprised a distinct gene-pool, which is not only from a different ecosystem but also with less systematic selection. A comparison of these accessions with modern cultivars can provide insight into changes in genomic structure due to human-mediated selection. Therefore, we analysed these accessions and compared them with MCl and GHL. Our results suggested a marked differentiation between OCl, MCl and GHL. A comparison of GHL with OCl showed lower differentiation as compare to MCl. The divergent behaviour of geographical landraces of  studies ,40,41,42 studies . OCl/πMCl) were lower than selection signals (πGHl/πMCl), suggesting a weaker selection pattern during modern genetic improvement than earlier selection [Besides, we identified a large number of selection sweeps, suggesting the domestication bottlenecks. Some of the identified selection sweeps overlapped with highly differentiated regions on respective chromosomes, i.e., A06, A08, A09, A10, A11, D02, D04, D10, D12 and D13. This overlapping pattern suggests the occurrence of differentiation due to human-mediated selection. Further, to understand the genetic basis of domestication and improvement in fibre yield, fibre quality, maturity and other morphological traits, we compared the location of selection sweeps with the significant loci of GWAS analysis and narrowed down selection sweeps into corresponding small regions which will be helpful for future studies to determine and characterise new genes concerning domestication and selection in upland cotton. Some of the selection sweeps we identified have been previously reported for fibre yield , fibre qelection ,48. Theselection ,49,50. IG. hirsutum, i.e., Yucatanese, richmondi, morrilli, Marie-Galante, palmeri, punctatum and latifolium (Group 3 was not included in phenotyping as these landraces cannot flower in the test locations due to photoperiod sensitivity)  with diverse genetic backgrounds were used for phenotyping. These accessions comprised three groups, i.e., group 1 belonged to modern cultivars (235) currently being cultivated, group two comprised 91 obsolete cultivars collected from southwest China and group 3 (31) comprised seven reported geographical landraces of itivity) . Two repIn all test locations, phenotypic traits were recorded following the same scoring standard. We characterised lint yield (LY), lint percentage (LP), fibre quality , fibre length uniformity , fibre micronair (FMic), fibre strength (FS), fibre elongation ), flowering time , boll opening days , leaf pubescence (LPub) and stem pubescence (SPub). Fibre quality was tested using twenty naturally opened balls from each accession. A High-volume instrument (HFT9000) was used for characterizing fibre quality parameters at the Cotton Quality Testing Center in Anyang, China. Flowering time was observed daily, and days to flowering (DF) were calculated from the sowing day to the day that the first flowers appeared in 50% of the plants. All samples were subjected to the High-volume instrument (HFT9000) for the estimation of quality parameters. G. hirsutum, GATK  was used for SNP calling. Sequencing data for G4; GHL was obtained from published data [Total genomic DNA was extracted from the seedlings of five cultured seeds of each accession in a growth chamber. After three weeks of sowing, at the true leaf stage, young leaves were collected, and a Plant DNA Mini Kit  was used to extract total genomic DNA. Three hundred and fifty base pair whole-genome libraries were constructed for each accession according to the manufacturer’s specifications . Subsequently, we used Illumina HiSeq X10 by a commercial service “Novogene” platform to generate 6.45-Tb raw sequences with 150-bp read length. Following alignment of high-quality reads with the genome of hed data .Population structure was studied using ADMIXTURE , which uPrincipal component analysis was performed using the EIGENSOFT package with an embedded SMARTPCA program  using 4,Phylogenetic analysis was performed to understand phylogenetic relationships among genotypes by constructing a phylogenetic tree using SNPs of all genotypes. SNPs were filtered with minor allele frequency, MAF = 0.05. Subsequently, a neighbour-joining tree was constructed using the maximum likelihood method with SNPhylo software . To visuFst) is a measure of population differentiation as it provides insight into the genome-wide differentiation among different groups. Thus, we calculated population fixation statistics (Fst) using vcftools with a sliding window of 100 kb and step size of 20 kb . The average Fst of all sliding windows was considered as the value at the whole genome level among different groups. The fixation index  was calculated as an alternative estimate for selection/improvement signals, using SweeD. Diversity ratios and CLR scores were compared for better assessment of selection/improvement signals. Nucleotide diversity (π) is an estimate of the degree of variability within population and species . Nucleot−5) and the genome-wide significant threshold at −log10(5 × 10−8).For GWAS analysis, we categorised genotypes into two sets. Set 1 comprised 169 accessions whose phenotypic data were collected from SJZ, CS, AY, XJ2 and XJ3, Set 2 comprised 324 accessions whose phenotypic data were collected from AY. A total of 4,329,838 high-quality SNPs were subjected to filtering with MAF >0.05, missing rate <20% and 1,604,221, and 1,506,091 SNPs were kept in set 1 and set 2, respectively, and subsequently, GWAS was performed on both sets of genotypes separately. Accessions with missing SNPs data were excluded from analyses. We performed GWAS for multiple traits in efficient mixed-model association expedited (EMMAX) software ,56. PopuOur study provides a genetic basis to understand the domestication process in upland cotton cultivars. It also provides a comprehensive insight into changes in genome structure due to selection and improvement during the last century. We also identified multiple GWAS associations for fibre yield, quality and other morphological characteristics. Further study is required to explore these novel loci associated with different traits to uncover causal genes related to these traits. Our study provides a comprehensive insight into the differentiation between modern cultivars, OCl and GHL, which can be a useful tool for the cotton breeders to understand changes accumulated due to selection and improvement breeding strategies.


Vitis species, in order to dissect the genetic basis of transpiration-related traits and to identify genomic regions of grape rootstocks associated with drought tolerance mechanisms.Understanding the complexity of the vine plant’s response to water deficit represents a major challenge for sustainable winegrowing. Regulation of water use requires a coordinated action between scions and rootstocks on which cultivars are generally grafted to cope with phylloxera infestations. In this regard, a genome-wide association study (GWAS) approach was applied on an ‘ad hoc’ association mapping panel including different The panel was genotyped with the GrapeReSeq Illumina 20 K SNP array and SSR markers, and infrared thermography was applied to estimate stomatal conductance values during progressive water deficit.In the association panel the level of genetic diversity was substantially lower for SNPs loci (0.32) than for SSR (0.87). GWAS detected 24 significant marker-trait associations along the various stages of drought-stress experiment and 13 candidate genes with a feasible role in drought response were identified.VIT_13s0019g03040, VIT_17s0000g08960, VIT_18s0001g15390) were actually induced by drought stress.Gene expression analysis proved that three of these genes  was significantly associated with stomatal conductance.Our results represent a step forward towards the dissection of genetic basis that modulate the response to water deprivation in grape rootstocks. The knowledge derived from this study may be useful to exploit genotypic and phenotypic diversity in practical applications and to assist further investigations.The online version contains supplementary material available at 10.1186/s12870-020-02739-z. Climate change is strongly influencing human life and natural systems , 2, haviGrapevine is considered to be a relatively drought tolerant plant, thus the impact of climate change on viticulture sustainability is subject of lively debate –7. NeverDrought-stress response is the result of complex and dynamic physiological, biochemical and molecular processes at cellular and systemic levels. Water deficit leads to vegetative development , stomataPrevious studies aimed to identify the genetic basis of drought response in grapevine , 31 and Vitis spp. accessions, listed in Table Snon-vinifera Vitis species and interspecific hybrids used for fruit production, maintained at the grapevine germplasm collection of Fondazione Edmund Mach . The volumetric soil moisture content per pot was monitored with a ML3 ThetaProbe Soil Moisture Sensor . The pot surface was covered with a plastic film to avoid soil water evaporation. The experimental plan was completely randomized.canopy (°C) was the temperature deduced from the thermal images of six sun-exposed mature leaves per vine, Tdry (°C) and Twet (°C) were the temperatures detected on the cardboard “reference surfaces”. Stomatal conductance (gs) and transpiration were measured with a steady state porometer (Licor Li-1600) in the third experimental year.The physiological response to drought was evaluated over 30 days. To evaluate the effect of water stress thermal images of the grape leaf canopies were elaborated using the software InfReC Analyzer (NS9500LT) . Stomatal conductance was estimated from two different thermal indices: crop water stress index (CSWI) Eq.  121] an an121] ai is the phenotypic value of ith sample, μ is the model intercept, β is a vector of SNP effects, ν is a vector of population effect and ε is a vector of residual effects, Q is the matrix from STRUCTURE that considers the individual probabilities to be associated to a subpopulation. The second model employed was the Mixed Linear model (MLM), which extends eq.  [qqman and CMplot packages of R software [http://genomes.cribi.unipd.it/grape [Genotype-phenotype associations were tested considering the average value of each trait for each year separately. When phenotype scores were not normally distributed they were transformed using the logarithm function. Three different models were tested using TASSEL v.5.2 . The firte (FDR)  that limte (FDR) . GWAS resoftware . Genomicit/grape .V. vinifera gene annotation v2.1. An assembled contiguous sequence of 3678 bp of the VIT_17s0000g08960 locus was resequenced with primers listed in Table SGene-specific primers were designed using Primer 3 software  based onVIT_17s0000g08960 coding region. Nucleotide diversity was estimated as π [The estimation and frequency of polymorphisms were defined using the DnaSP software , based oted as π . The neuted as π  was estited as π  and Fu ated as π  tests. Pted as π .Total RNA was isolated from grape leaves using the Spectrum™ Plant Total RNA Kit . DNase treatment was performed using the Dnase I  during the RNA extraction. RNA samples were quantified with the spectrophotometer NanoDrop ND-8000  and their integrity was checked by agarose gel electrophoresis. cDNA was synthesized with the SuperScript® III Reverse Transcriptase . Analysis of candidate genes expression was carried out using LightCycler® instrument and the related LightCycler® software . All Real-Time PCR reactions were performed using LightCycler® 480 SYBR Green I Master Mix  in 20 μl reactions according to to manufacturer’s instructions by using primers listed in Table SStatistical analyses were performed using R packages ‘stats’, ‘agricolae’ and ‘companion’ v3.5.1 . Different tests were applied for mean comparisons. Parametric Student’s t-test or one-way ANOVA were used to compare normally distributed data with equal variances. Non-parametric Mann-Whitney U test and one-way Kruskal–Wallis test were applied when the assumptions of normality or homogeneity of variances were violated.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.


Goats play a key multifunctional role in food security and poverty alleviation for small farmers in many less developed countries. Unfortunately, the productivity level of these goats is low. Among the alternatives proposed to overcome this situation, one is to establish a phenotypic breeding program with the support of community breeding organizations. In this study, genetic parameters were estimated for the growth, conformation, and survival of 1538 young goats raised by small farmers in Burundi organized in farmer field schools. Overall, the results suggest that phenotypic selection of growth and conformation traits would be possible if data recording and animal management were improved. On the other hand, efforts to improve survival should focus on improving the environmental conditions in which kids are raised. The role of community breeding organizations and animal health workers is therefore essential to disseminate breeding techniques and methods that optimize animal production and health.The goal of this study was to estimate genetic parameters for the growth, conformation, and survival of goat kids raised in smallholder farming systems in Burundi. To do this, measurements were taken on live weight, thoracic perimeter, length, and height at birth , at 3 months , at 6 months , at 9 months , and at 12 months . Kids were born between 2016 and 2019, from 645 dams and 106 bucks. Three bivariate animal models were used to estimate genetic parameters of body weight and conformation measurements as potential indicators of this weight. According to the measure, heritability was estimated between 15 and 17% and genetic correlations between 65 and 79%. An accelerated failure time animal model was used to estimate the heritability of survival for kids under one year, adjusted for birth weight. Goat survival was significantly prolonged by 0.64 days per kilogram of birth weight. The estimated heritability for this trait was 2%. Overall, these results suggest that a selection program could be implemented to improve animal growth, either directly on weight or indirectly on conformational traits. At the same time, efforts need to be made to improve rearing conditions to increase the survival of kids. Goats play a key multifunctional role in food security and poverty alleviation of smallholder farmers in many less developed countries such as Burundi. Native goats, the most important breed, were considered the most suitable species for harsh environments  but theyGiven their low level of productivity, it may be feared that native goats will vanish  so diffeOne genetic intervention has been to cross them with imported breeds. But a consequence of this crossbreeding scheme is that the local, old, and well-adapted breed could be left behind, along with its unique genetic makeup. Another alternative would be to set up a genotypic breeding program whose goals would be to improve goat productivity. To do this, accurate information on pedigree and performance is needed. This information is often not available because of the many constraints on small-scale farming systems in the least developed countries. These include the high illiteracy rate among smallholders, the lack of animal identification and pedigree recording systems, the non-existence of institutional frameworks, and the inadequacy of village-level organizations to ensure an effective participation of the farmers in breeding programs ,9,10. ThAnother solution would be to set up a phenotypic selection program directly on the farm. In such cases, community-breeding organizations have an important role to play. In fact, they have been set up in many developing countries to support programs to improve the performance of small ruminants ,12,13. AThis is the option taken in this study. We have collected information on goat characteristics in pilot farmer associations called farmer field school (FFS), as they help producers build their technical capacity. Researchers and local veterinary services followed the FFS and collected information on goat performance and pedigree. Selection objectives and criteria to be improved in the phenotypic selection program have been identified after surveying the farmers . This suTherefore, this study aims to estimate the genetic parameters of growth and mortality rates of local goats raised in smallholder Burundian farming systems to determine whether phenotypic selection would be relevant to these traits.The study area included the provinces of Gitega and Rutana. Gitega province is located in the humid central uplands and Rutana province is located in the dry eastern lowlands. The geoclimatic and management characteristics of goats in these provinces are summarized in Each animal has been identified with an ear tag number and each event  has been recorded. The data covered a period of 3 years, between 2016 and 2019. Dams remained in the flock until death while the bucks were replaced when the first offspring of their son were old enough to mate. Breeding males were traded between farmers while the others were sold.For each kid, a record included the parents’ identity codes, its sex, date and type of birth, dam’s parity, herd code, and performance. Body weight (BW), chest girth (CG), body length (BL), and height at withers (HW) were recorded at birth, 3 months, 6 months, 9 months, and 12 months. The BW is the selection criterion, and CG, BL, and HW are indicator traits. Measurements at birth had to be recorded within 3 days of birth. Measurements were taken  by trained community animal health workers in each village. Data has been regularly recorded in a central data set. Survival time was measured as the number of days alive over a one-year period since birth. Usually, one year is age at first service or optimal sale, as reported by farmers. Animals who have not experienced death during this period were right-censored .During the follow-up period, a total of 1538 kids were born to 645 dams and 106 sires with 1538, 1270, 992, 787, and 705 records at birth, 3 months, 6 months, 9 months, and 12 months, respectively. About 1149 records on length of survival were gathered. 1 is the vector of observations for BW and y2 is the vector of observations for CG, BL, or HW; b1 and b2 are the corresponding vectors of fixed effects; a1 and a2 are the corresponding vectors of additive genetic effects; m1 and m2 are the vectors of maternal effects; p1 and p2 are the vectors of permanent environmental effects; c1 and c2 are the vectors of common environmental effects; e1 and e2 are the vectors of random residuals; and Xi, Zai, Zpi, Zmi, and Zci are the incidence matrices relating yi to the corresponding vectors (i = 1 et 2). The fixed effects included in all three models were: age (n classes = 5), sex (n classes = 2), parity (n classes = 3), and type of birth (n classes = 2), as well as all interactions. Furthermore, random components were assumed normally distributed: a ~ N , m ~ N , pe ~ N , ce ~ N , and e ~ N  where σ2a, σ2m, σ2p, σ2c and σ2e are the variances components and A is the additive genetic relationship matrix. After computing preliminary descriptive statistics, records were analyzed assuming three bivariate repeatability-maternal animal models, one for BW and CG, one for BW and BL, and one for BW and HW:a, Zc, and Zm, are incidence matrices relating y to corresponding vectors. Exponential distributions were assumed for all random components where σ2a, σ2m, σ2c, and σ2e are the variances components and A is the additive genetic relationship matrix. An accelerated failure time (AFT) animal model ,16,17 waFor both models, variance components and genetic parameters were estimated using a Bayesian approach and with the MCMCglmm package-R ,19. Inve2ph) of body weights and linear measurements were computed as: σ2ph = σ2a + σ2m + σ2p + σ2c + σ2e. Then, heritability (h2) and repeatability (r) were computed as: h2 = σ2a/σ2ph and r = (σ2a + σ2p)/σ2ph. Genetic correlations between BW and one of the indicator trait  were computed with the following model: rg = g12/(g11*g22)1/2 where g12 is the additive genetic covariance between the two traits, and g11 and g22 are the additive genetic variance for trait 1 and 2, respectively.Output of the iterations were used to calculate the mean, median, and highest posterior density (HPD) interval for each variance/covariance components ,21,22. POverall means BW (±standard deviation) at birth, 3 months, 6 months, 9 months, and 12 months were 2.1 ± 0.5, 6.4 ± 1.6, 9.0 ± 2.2, 11.4 ± 2.5, and 13.6 ± 2.9 kg, respectively. Phenotypic correlations between BW and CG, BW and BL, and between BW and HW were 0.95, 0.94, and 0.93, respectively. Mean litter size at birth was 1.4 kids, slightly smaller in the Gitega (1.2) than in the (1.6) Rutana province. Mortality rate up to one year was estimated at 31.6% and was higher among twins (42%) than among kids born alone (24.2%).2, r, genetic correlations, and all variance components. There appears to be a tendency for estimates of σ2m to be smaller than the other variance components, especially for BW, CG, BL, and HW. Inversely, estimates of σ2e are highest for CG, BL, and HW and estimates of σ2c are highest for ST. Heritability estimates are very low for BL and log (ST) and close to 15% for BW, CG, and HW. Genetic correlations are between 60 and 70%. Results of the statistical analyses are given in Age, sex, and type of birth significantly influenced body weights. For example, posterior BW mean of twins was 1 kg lower than posterior BW mean of singles (0.7 and 1.3 kg for the lowest and highest HPDP limits). Posterior BW mean of males was 0.58 kg heavier than females (0.35 and 0.83 for the lowest and highest HPDP limits). Concerning ST, the time ratio to death is expected to decrease significantly by 0.64 day per one kg increase in birth BW.2 estimate is within the range (0.09 to 0.47) reported by several authors from BW measured between birth and yearling age in different goat breeds [2 estimate is in the range reported by [The main objective of this study was to obtain BW and ST genetic parameters to confirm whether phenotypic selection of these traits would be possible in smallholding farms regrouped in FFS. Our results are more or less consistent with estimates published in the literature. For example, our BW ht breeds ,24,25,26t breeds ,28,29. Sorted by ,31. 2 for growth related traits were low to moderate, on-farm phenotypic selection to improve these traits is thought to be not very effective in the current situation. This is because the expected response to selection, i.e., the gain achieved by mating the selected parents, is directly related to h2 [2 values were not caused by a paucity of σ2a but by great residual variances. For example, median estimates of σ2a for BW is 0.58 kg2, much lower than the estimate for σ2e at 1.57 kg2. A similar disparity is also noticeable for CG, BL, and HW. Moreover, estimates of repeatability, which is often considered as the upper limit to heritability [2a is not negligible and that repeatability is 30%, there may be some genetic variation between animals and it would be possible to select animals as parents of the next generation based on their phenotypic values [Given that estimates of hed to h2 . But thetability , were clc values  for grow2 estimates. Indeed, missing parentage data have been shown to influence h2 estimates in domestic and wild species [2 is not necessarily a useful measure of a trait’s potential to evolve [Other factors, such as the shallow depth of the pedigree, may also explain the low h species . Another species  and redu species . We incl species ,39,40 buo evolve . Finallyo evolve  and shouEstimates of genetic correlations between BW and CG, BL and HW are high, suggesting these indicator traits could be used to indirectly select for better growth. As it has been reported elsewhere ,43, the 2 is null and common environmental variance is very high. Therefore, the expected response to selection for this trait would be almost null and efforts should focus on improving the environmental conditions in which kids are raised, especially as the number of days of survival increases as birth weight increases. This is a great challenge because veterinary surveillance systems are weak. As an illustrative example, the outbreak of small ruminant pest decimated more than 4000 goats herds in 2018 [Concerning ST, h in 2018 . A meta- in 2018 . At an i in 2018  propose  in 2018 ,49. Their role is also essential to ensure the sustainability of breeding programs, as these require a long-term commitment from all stakeholders . In thisResults of estimates genetic parameters in this study indicated that a modest genetic progress could be achieved by on farm phenotypic selection of indigenous goats with better weight and conformation. This phenotypic selection should be accompanied by efforts on improving recording and management. On the other hand, response to selection for kid survival would be almost null and efforts should focus on improving the environmental conditions in which kids are raised.


High feed efficiency and rapid growth, especially of the breast muscle, are believed to contribute to development of such muscle defects; however, their etiology remains poorly understood. To gain insight into the genetic basis of these myopathies, a genome-wide association study was conducted using a commercial crossbred broiler population (n = 1193). Heritability was estimated at 0.5 for WB and WS with high genetic correlation between them (0.88). GWAS revealed 28 quantitative trait loci (QTL) on five chromosomes for WB and 6 QTL on one chromosome for WS, with the majority of QTL for both myopathies located in a ~ 8 Mb region of chromosome 5. This region has highly conserved synteny with a portion of human chromosome 11 containing a cluster of imprinted genes associated with growth and metabolic disorders such as type 2 diabetes and Beckwith-Wiedemann syndrome. Candidate genes include  However, the financial gains and increased production capacity associated with improvements to production traits are threatened by the concurrent global emergence of numerous muscle disorders that severely affect meat quality and may also impact animal welfare6. Wooden breast and white striping, often co-occurring and believed to be part of the same disease spectrum8, are two such myopathies, which together represent the breast muscle defects with the highest prevalence and greatest economic burden.The modern commercial broiler chicken embodies remarkable gains in the economics of meat production realized through intensive breeding programs, optimized nutrition, and enhanced management practices. Compared to the 1950s, modern broilers can be raised to approximately the same market weight in close to half the time using substantially less feed and with substantially higher breast muscle yield4, wooden breast manifests as palpably firm and discolored pectoralis major with subcutaneous and fascial edema, petechial hemorrhages, and spongy areas with disintegrating myofiber bundles. Birds affected by wooden breast frequently show signs of white striping as well, which is macroscopically characterized by white fatty striations running parallel to the muscle fibers and presents with similar histological lesions as wooden breast, including myodegeneration with regeneration, necrosis, lymphocyte and macrophage infiltration, fibrosis, and lipidosis9. These muscle disorders present an exceptional challenge to producers, as dietary or management strategies against them often fail to improve meat quality11 or lack viability due to impaired live performance12 or cost-prohibitive inputs. The tight association between breast muscle disorders and economic traits such as feed efficiency and breast muscle yield suggests that successful mitigation of meat quality defects without simultaneous compromise to desirable traits will require an understanding of the genetic basis of these myopathies and selection against their causal variants.First described in the literature in 201414 in the breast muscle, while others suggest shared etiologic underpinnings with type 2 diabetes and other metabolic disorders in mammals16. However, current knowledge regarding the genetic basis of wooden breast and white striping is extremely limited and somewhat conflicting. Bailey et al.17 estimated heritability (h2) of wooden breast and white striping in a purebred commercial broiler line to be low – 0.07 for wooden breast and 0.25 for white striping—but also demonstrated a dramatic 18.4% reduction in wooden breast incidence after only 2 years of genetic selection against breast muscle myopathies. Another study of two broiler lines divergently selected for ultimate pH of the pectoralis major estimated heritability (h2) of white striping to be 0.6518. The only genome-wide association study of white striping was performed on a similar population and found no markers with genome-wide significance19, while the genetic architecture of wooden breast currently remains unexplored.Several hypotheses exist regarding the underlying causes of wooden breast and white striping. Some implicate the rapid growth of the pectoralis major and relative vascular deficiency for creating a buildup of waste products and hypoxic conditionsOur poor understanding of these traits at the genetic level precludes our ability to adequately mitigate their effects through either broiler breeding or management. Therefore, the aim of this study is to estimate genetic parameters for wooden breast, white striping, and two body weight traits in a hybrid commercial broiler population and to identify quantitative trait loci (QTLs) and candidate genes to elucidate potential molecular mechanisms contributing to myopathy development.12. Meat quality is not substantially affected in birds with scores of 1 or 2, and severe wooden breast (score of 4) was detected only in 2.1% of chickens in our study  and white striping .Of the 1,194 progeny that were genotyped for this study, only one bird did not meet sequence filter criteria and was excluded from all analyses. Sex chromosomes were used to confirm each bird’s sex, and found that 12 birds were mis-gendered at necropsy. The sex-specific and overall distributions of wooden breast and white striping scores for the 1,193 birds that passed filter criteria can be found in Supplementary Table h2 = 0.49) than indicated by previous work, which estimated heritability between 0.1 and 0.2420. Additionally, we found the heritability of white striping (h2 = 0.50) to lie between previous estimates ranging from 0.1820 to 0.6518. Variability among these studies is not surprising, as heritability is inherently specific to the population and environment in which it is estimated. For example, the lower estimates for heritability of wooden breast and white striping were for purebred commercial broiler lines that had relatively low incidence rates of myopathy—0.16% to 0.39% for wooden breast; 14.46% to 49.6% for white striping—compared to other hybrid commercial broiler populations20. Moderate heritability for wooden breast and white striping indicates that both genetic and environmental factors exert strong influences on phenotypic differences for these traits, which is consistent with studies that have demonstrated the ability to reduce severity and incidence of breast muscle myopathies through both genetic selection17 and manipulation of dietary energy21.Descriptive statistics, heritability estimates, and variance components of the traits are summarized in Table 8. Note that low genetic correlation between the muscle disorders and body weight traits does not signify low genetic correlations of muscle disorders with other performance traits that were not assessed in this study, such as breast muscle yield and feed conversion ratio.Our analysis found genetic correlations Table  of woode22 in order to identify QTLs with greater confidence.Excluding the single sample that did not meet filter criteria, sequencing produced an average of 5.88 million reads per sample, with an average mapping rate of 77.3%. A total of 199,957 SNPs were retained after locus filtering, consisting of 195,617 autosomal SNPs and 4340 SNPs on the Z chromosome. The average sequence depth for each genotyped SNP was 10.8, with 97.9% of all genotype calls supported by at least 1 read. The average minor allele frequency was 0.24. Filter criteria implemented in this study were substantially more stringent than in previous studies utilizing low-depth genotyping-by-sequencing methods2) in the population decayed at an extremely rapid rate , with chromosome-specific capture rates ranging from 84.6% on the Z chromosome to 3.33% on chromosome 31  1, 2, 4, 5, 6, 9, 10, 18, 24, 30, and 32; however, the majority of significant markers  and significant/suggestive markers combined  were confined to a region on GGA5 from 8.1 to 16.4 Mb. A total of 28 QTLs on chromosomes (GGA) 2, 5, 6, 30, and 32 were identified from significant SNPs that were clustered based on LD , cathepsin D (CTSD), potassium voltage-gated channel subfamily Q member 1 (KCNQ1), lymphocyte-specific protein 1 (LSP1), solute carrier family 22 member 18 (SLC22A18) and USH1 protein network component harmonin (USH1C) on GGA5 and dynamin 2 (DNM2) on GGA30. In humans, variation in all of these genes has been linked to altered insulin expression or secretion26 despite their having distinct biological functions.Top candidate genes for wooden breast Table  include 19 did not discover QTLs with genome-wide significance, but identified three QTLs with chromosome-wide significance in the 3rd Mb windows of GGA17 and GGA18 and in the 88th Mb window of GGA1. No significant or suggestive SNPs were identified for sex-specific analyses of white striping. Genomic windows with suggestive significance in the BayesB analyses are listed in Supplementary Table A total of 18 SNPs (10 significant and 8 suggestive) were associated with white striping score Fig. , all of CTSD, LSP1, troponin I2 fast skeletal type (TNNI2), synaptotagmin 8 (SYT8), and MOB kinase activator 2 (MOB2). As with candidate genes for wooden breast, variation in mammalian homologues of all of these genes is linked to altered insulin expression or secretion in pancreatic beta cells27. However, they possess additional functions that may be relevant to poultry myopathies, such as calcium-dependent regulation of striated muscle contraction28 (TNNI2), and regulation neutrophil transendothelial migration29 (LSP1).Top candidate genes for white striping include No QTLs were identified for either body weight trait using single-SNP or multi-marker analyses  has conserved synteny with portions of human chromosomes 11, 14, and 1533. Selective breeding for meat production traits is considered a major culprit for the rapid increase in meat quality issues among broiler chickens, and the genomic co-localization of loci that are beneficial for production traits with loci associated with breast muscle myopathies provides further evidence of this. Targeted sequencing of GGA5 is required to determine if the specific genetic mutations contributing to each trait are simply linked or, in fact, the same. Additionally, targeted sequencing of GGA5 with long-read technologies could reveal copy number variants, which are not captured by SNP data. The SRY-box transcription factor 6 (SOX6) gene is of particular interest because it was identified as a candidate gene for the selection sweep region on GGA533. Although SOX6 was not identified as a candidate gene for wooden breast based on association analyses described here, a selection sweep in or near this gene would hinder detection of causal variants using many GWAS methods due to filters for minor allele frequency.The QTL-rich region of GGA5 also contains several areas that show evidence of strong selection sweeps Fig.  in two cKCNQ1 polymorphisms are associated with altered insulin secretion in pancreatic β cells, short and long QT syndrome in cardiac muscle, and sensorineural deafness in the inner ear34. This gene encodes a highly-studied voltage-gated potassium channel, which interacts with numerous other protein subunits and switches from voltage-dependent to constitutive activity34, endowing it with a diverse set of functional roles and associating it with a proportionally diverse number of pathologies. Some pleiotropic KCNQ1 variants in humans can simultaneously increase insulin secretion in the pancreas, reduce serum potassium upon oral glucose challenge, and cause long QT syndrome, putting individuals at risk of sudden, uncontrollable, arrhythmias which may lead to fainting or sudden death35. This could represent a potential cause of sudden cardiac death reported in broilers, especially those with wooden breast36. We re-examined data from a previous study of differential gene expression associated with wooden breast in 2-week-old female broilers16 and found that, although there was no differential expression of KCNQ1 in the pectoralis major of 2-week-old birds, expression levels of the gene were very low in all birds (data not shown). It is therefore likely that genetic variation in KCNQ1 would primarily affect organs other than the pectoralis major, potentially including the pancreas. A major functional theme of candidate genes identified in this study is insulin secretion and action, particularly in the QTL-rich region of GGA5 Fig. , which cINS) is located in the middle of this QTL-rich region at approximately 13.94 Mb  encodes a membrane protein with roles in trafficking and exocytosis, including insulin secretion in pancreatic islet cells25. The SYT8 gene is located over 300 kb away from INS in humans, and yet physically interacts with the INS locus to elevate SYT8 expression, especially in the presence of glucose25. This indicates the importance of SYT8 for both basal and glucose-stimulated insulin secretion in human islets. The same study identified additional interactions of INS with troponin I2, fast skeletal type (TNNI2) and lymphocyte-specific protein 1 (LSP1), which are also top candidate genes for wooden breast and white striping.Some candidate genes on GGA5 affect insulin secretion through both gene–gene interactions and protein function, as the insulin gene , which is of particular interest considering findings of a knockout study of this gene in mouse gastrocnemius muscle. Tinelli et al.39 found that DNM2 knockout caused a decrease in the number of muscle fibers, an increase in the proportion of smaller muscle fibers, an increase in lipid droplets, mitochondrial enlargement and disruption of cristae, mitochondrial dysfunction, and altered neuromuscular junctions at various developmental ages39. Previously, Papah et al.5 described an increase in acellular lipid droplets and abnormal mitochondrial morphology with degenerated cristae as part of the wooden breast pathogenesis. A decrease in the number of muscle fibers and increase in the proportion of small fibers has also been documented as a characteristic of wooden breast in some broiler lines40.QTLs outside of GGA5 have strong ties to insulin as well. The protein encoded by GOT1, encodes the cytoplasmic version of an enzyme called aspartate transaminase (AST) that catalyzes the interconversion of aspartate and α-ketoglutarate to oxaloacetate and glutamate. Two key roles of AST should be highlighted from this anaplerotic reaction: the replenishment of aspartate, a key intermediate of the citric acid cycle, and the regulation of concentrations of glutamate, which functions as a potentiator of insulin release and precursor of the antioxidant glutathione in human skeletal muscle41. Thus, variation in GOT1 may play an important role in altered energy metabolism and ROS balance in wooden breast.A candidate gene on GGA6, 42, body weight43 is already known to affect plasma insulin and glucagon levels, as well as insulin sensitivity and glucose clearance. More generally, commercial broilers have elevated plasma insulin levels compared to layers44 and poorer serum insulin homeostasis compared to Silky chickens45. Insulin is also a key regulator of carbohydrate, lipid, and amino acid metabolism47, which are dysregulated in wooden breast and white striping49, although additional research is required to determine whether altered insulin dynamics are a contributing factor to wooden breast and white striping in broilers.Genetic variation associated with broiler production traits such as visceral adiposity16 and suggest that potential alterations to pancreatic function and insulin action may be involved. There exist substantial differences between mammalian and avian insulin signaling and action—for example, the apparent lack of the insulin responsive glucose transporter GLUT4 in the chicken genome50—that warrant additional research, especially with regard to wooden breast and white striping. Investigation into organs involved in regulation of whole-body energy homeostasis, such as the pancreas, liver, and adipose tissue, may also aid in our understanding of these myopathies.This study is the first to characterize the genetic basis of wooden breast and white striping in a commercial crossbred broiler population using genetic markers. A major finding of the present work is the identification of a QTL-rich region for both wooden breast and white striping between 8.0 and 16.5 Mb on GGA5, which is homologous to and possesses highly conserved synteny with an imprinted region of human chromosome 11. Further study of this GGA5 region may prove to be critically important for understanding the cause of chicken myopathies as well as the evolution of genomic imprinting and genes involved in growth regulation in humans. Our findings also provide compelling evidence in support of a previous hypothesis describing a shared pathomechanism between breast muscle myopathies in broilers and type 2 diabetes in mammals1 = 686, n2 = 542) with staggered hatch dates. Broilers were housed according to optimal industry standards in five poultry houses and given free access to feed and water until approximately 7 weeks of age , at which time they were euthanized by cervical dislocation. Live weight was recorded for all birds at 13 days of age. Preceding euthanasia , live weight was recorded again and whole blood samples were collected from the brachial wing vein of each bird using a 3 mL syringe with 23-gauge needle and placed in lithium heparin-coated tubes. Plasma was separated by centrifugation and blood samples were stored at -80˚C until further analysis.All animal procedures were performed in accordance with guidelines set by The University of Delaware Institutional Animal Care and Use Committee (IACUC) and were approved by IACUC under protocol number 48R-2015-0. The study was carried out in compliance with the ARRIVE guidelines. A total of 1,228 mixed male and female Cobb500 broilers from the same breeding population of 15 sires and 200 dams were raised as two separate hatches . Single-end 1 × 100 sequencing of 96-plex libraries was performed on an Illumina HiSeq 2500. To control and detect lane bias and batch bias, three samples of control DNA were included in each library and each library was run on at least 4 lanes across at least 2 flowcells. Additionally, one sample (B92023) was included as a positive control in all libraries.After the live animal experiment was completed, blood samples from 1194 birds were selected for DNA extraction and genotyping. These represented all males (n = 557) and females (n = 636) that did not exhibit any noticeable health conditions or deformations and had a blood sample of sufficient size with no sign of coagulation. The 15 sires of our chicken population were also included for DNA extraction and genotyping to improve genotype calling of progeny; however, phenotypic data was not recorded for sires. Total DNA was isolated from blood samples using the DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s protocol. DNA samples were quantified and quality was assessed using the NanoDrop 1000 Spectrophotometer (Thermo Fisher scientific); all samples had a 260/280 ratio of approximately 1.8 and 260/230 ratio greater than 1.5. Approximately 2.5 µg of DNA per sample was aliquoted into individual wells of a 96-well plate, dried at room temperature, sealed, and shipped to Animal Genomics Research laboratory, AgResearch Invermay, New Zealand for restriction enzyme-based genotyping by sequencing (GBS). The GBS libraries were constructed according to the methods outlined in Elshire et al.53 and mapped by sample and lane onto the GRCg6a reference genome (Ensembl release 95) using BWA-MEM54. Samtools v1.955 was used to remove reads with mapping quality below 30, convert the alignment file to BAM format, and merge all alignment files by sample. Variants were detected across all samples on a per chromosome basis using samtools and bcftools v1.9 with parameters set to limit variants to biallelic SNPs55. The results were then combined into a single VCF.AgResearch supplied a single variant call file (VCF) which was constructed by the following methods. Fastq files were demultiplexed using GBSXVariants were filtered to only include SNPs with a minimum read depth of 5 for at least 50% of samples, a maximum average read depth of 50, and a minor allele frequency of 5% or greater. Samples were also filtered to only include those with a minimum read depth of 5 for at least 50% of SNPs. Loci on the W chromosome were excluded from all analyses, but were used along with Z chromosome loci to confirm sex of the birds before subsequent analyses. Loci on the Z chromosome were filtered separately using only males to avoid filtering bias from hemizygotic females.G) was constructed from the resulting variant data using the R package KGD52, which implements a method developed to account for GBS with low depth of coverage. Variance components and heritabilities of wooden breast, white striping, body weight at 13 days, and body weight at 7 weeks were estimated using ASReml 456 with the following univariate model:A genomic relationship matrix (2) of markers within 5 Mb of each other was calculated with Haploview v4.2 using the discrete genotype calls produced during the SNP calling step described above. To characterize LD decay with distance, markers were filtered to only include SNPs with a minimum read depth of 10 in at least 50% of birds. LD on the Z chromosome was determined using genotype calls from only male birds. LD decay curves were generated for macro-chromosomes (GGA1 through GGA5), intermediate chromosomes (GGA6 through GGA10), micro-chromosomes (GGA11 through GGA33), and the Z chromosome by fitting a four-parameter Weibull function (type-1) to r2 values against physical distance using the R package ‘drc’ (v3.0–1). The resulting curves were used to estimate r2 at distances of 1 kb, 10 kb, 100 kb, 500 kb, 1 Mb, and 5 Mb, and to calculate the distance at which LD decayed below the threshold of 0.2 (D0.2).Pairwise linkage disequilibrium , then computed the average percentage weighted by chromosome length. This final value reflects the percentage of the genome (autosomes and Z chromosome) that was captured by SNPs in the present dataset.The ability of the present study to capture QTL was evaluated by examining SNP coverage on each chromosome in comparison to corresponding D57, with population structure and LD used as a prior and default parameter values. The minimum correlation coefficient between two alleles (r2) for LD to be used in genotype estimation was set at 0.2 and the distance within which to search for alleles that may be in LD with the given allele was set to 10,000 base pairs. The maximum mean difference in allele frequencies between iterations to be tolerated before iterations end was set to 0.001. The resulting genotype posterior probabilities were converted to allele dosages (i.e. posterior mean genotypes) for use in association analysis. Genotype calling of markers on the Z chromosome was performed using only male birds.Due to the relatively low-depth sequencing methods implemented here, continuous genotype probabilities were used for all association analyses rather than discrete genotype calls. Genotype posterior probabilities were calculated using the Bayesian genotype caller polyRAD v1.158 and involved first estimating the proportion of markers with null effect (π) using BayesCπ, followed by computation of the genetic variance explained by each 1 Mb window of SNPs in the genome with BayesB. The BayesCπ algorithm was run for 200,000 MCMC iterations with a burn-in of 150,000 iterations and used a starting value of 0.5 for π. BayesB was run for 60,000 iterations with a burn-in of 20,000 and used the posterior mean of π calculated by BayesCπ for each trait. The following model was used for both Bayesian methods:Two separate approaches—Bayesian multi-marker regression and single-SNP analysis—were used to detect quantitative trait loci (QTL) for each trait. Bayesian models were implemented in GenSel59 with software patches provided by van den Berg et al.60 that adapted the program to accept allele dosages. Equation 1 was used for all single-SNP analyses with the addition of a SNP effect fitted as a fixed covariate. Single-SNP analysis was performed three separate times for each trait, once using all birds, once with only females, and once with only males. Loci on the Z chromosome were only included when male birds were analyzed separately. For analyses including only male or female progeny, the fixed effect of sex was dropped from the model. Significant and suggestive genome-wide association thresholds were set at p-values corresponding to FDR-adjusted61 q-values of 0.05 and 0.20, respectively.Single-SNP analysis was performed with a mixed linear model using GCTA version 1.26.02 > 0.75) or that were separated by a distance less than or equal to the D0.2 value for the relevant chromosome size were assumed to share evidence in association analyses for a given trait and were thus grouped into non-overlapping QTL. Markers considered “suggestive” were not used in the construction of QTL. Candidate genes were identified based on proximity to QTL  using Ensembl and NCBI annotation of the Gallus gallus genome version GRCg6a (annotation release 99).Significant intra-chromosomal SNPs that were in high LD  and between human genome assembly GRCh38 and mouse genome assembly GRCm38  were retrieved using the UCSC Table Browser63. Associations with human muscular and metabolic disease phenotypes in genomic regions homologous to the QTL-rich section of GGA5 were identified from the OMIM database64. Additionally, previously identified selective sweeps on GGA5 in commercial purebred broiler lines that are susceptible to wooden breast33 were lifted over to the GRCg6a chicken genome assembly using the UCSC liftOver tool65.An ~ 8 Mb region of GGA5 was found to be exceedingly rich in QTL for both wooden breast and white striping, and warranted additional attention with regard to existing knowledge of the chromosome’s genomic features, which were visualized in a Circos plotSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.