Strong genetic correlations (GCs) were found between CBF in subcortical and cortical GM regions, as well as among the three arterial territories , suggesting a largely convergent genetic control for the CBF in brain GM. The moderate heritability of CBF warrants future investigations to uncover the genetic variants and genes that regulate CBF.Adequate cerebral blood flow (CBF) is necessary to maintain brain metabolism and function. Arterial spin labeling (ASL) is an emerging MRI technique offering a non-invasive and reliable quantification of CBF. The genetic basis of CBF has not been well documented, and one approach to investigate this is to examine its heritability. The current study aimed to examine the heritability of CBF using ASL data from a cohort of community-dwelling older twins (41 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs; age range, 65–93 years; 56.4% female). The results showed that the cortex had higher CBF than subcortical gray matter (GM) regions, and CBF in the GM regions of the anterior cerebral artery (ACA) territory was lower than that of the middle (MCA) and posterior (PCA) cerebral arteries. After accounting for the effects of age, sex and scanner, moderate heritability was identified for global CBF ( Accounting for ~20% of body’s oxygen consumption for normal function , followed by total CBF rate (h2 = 0.161). It is noteworthy that although both these two studies investigated CBF, the measures represented two different aspects of CBF. FAIR is a symmetrical arterial spin labeling (ASL) technique measuring the perfusion of blood into brain tissue, whereas MR angiography measures CBF velocity within vessels. So far, there has not been any studies investigating the heritability of CBF using twin samples. Twin studies allow for more accurate modeling of genetic and environmental factors, and are often considered as the optimal approach in estimating the genetic basis of a phenotype.A few studies have shown that genetic factors are involved in regulating CBF. APOE ε4 genotype was shown to differentially affect CBF across the lifespan , the gold standard for CBF quantification . We also examined the heritability and genetic correlations (GCs) of regional CBF in the three major arterial territories, as well as in the cortex and subcortical GM regions.The study sample was drawn from the Older Australian Twins Study  were scanned, with background suppression enabled. A separate M0 image without background suppression was also acquired with TR/TE = 6,000/12 ms and the same spatial resolution as the 30 control-tag pairs. T1-weighted scans were also acquired for the postprocessing. The scanning parameters were TR/TE = 6.5/3.0 ms, flip angle = 8°, FOV = 250 × 250 × 190 mm3, spatial resolution = 1 mm isotrophic, and matrix size = 256 × 256.PCASL scans were acquired using a Philips 3T Achieva Quasar Dual scanner . The acquisition parameters were TR/TE = 4,500/12 ms, label duration = 1,800 ms, post label delay = 2,000 ms, flip angle = 90°, imaging matrix = 128 × 128, and FOV = 240 × 240 × 95 mm2, voxel size = 1 × 1 × 1 mm3, and bandwidth = 240 Hz/pix.Both Victoria and Queensland study centers have used the same scanner model and identical scanning parameters for ASL and T1. At both sites, PASL scans were acquired from 3T Siemens Magnetom Trio scanners, using the PICORE Q2T perfusion mode. The acquisition parameters were TR/TE = 2,500/11 ms, TI1/TI2 = 700/1,800 ms, flip angle = 90°, phase partial Fourier factor = 7/8, bandwidth = 2232 Hz/pix, imaging matrix = 64 × 64, and FOV = 192 mm. Eleven sequential 6-mm thick slices with a distance factor  of 25% between adjacent slices were acquired for each volume. The first of the 101 PASL volumes was used as the M0 image. T1-weighted images were acquired in Victoria and Queensland sites with TR/TE/TI = 2,300/2.98/900 ms, flip angle = 9°, 208 sagittal slices, within plane FOV = 256 × 240 mmWe applied oxford_asl  was used to examine the differences in GM CBF between ACA, MCA and PCA, as well as between cortical and subcortical GM regions.Univariate analysis of variance (ANOVA) were conducted to test the differences in age and CBF estimates between MZ and DZ. Cross-tabulation with chi-square analysis was carried out to test the differences in gender distribution between MZ and DZ. General linear model with repeated measures . Cortex had higher CBF than subcortical GM regions  and PCA .The demographic characteristics and CBF estimates were summarized in h2 = 0.611, 95% CI = 0.380–0.761, p = 2.19 × 10−5). The heritability of examined regions of interest (ROIs) ranged from 0.500  to 0.612 (ACA GM). Separate heritability estimates in PCASL and PASL sub-samples were summarized in Heritability of global and regional CBF was summarized in Pearson correlations of the CBF measures derived from three different calibration tissues  have been shown in p-value (0.582) in model comparison. In Cholesky AE model, the heritability estimates (additive genetic variance) of PCA, ACA and MCA were 0.532 , 0.614  and 0.592 , which were in line with the estimates from the univariate AE model. The path coefficients for the Cholesky AE model have been shown in The multivariate model fitting has been summarized in Using bivariate AE model, high genetic and ECs of CBF were also observed between cortical and subcortical GM regions  originating from internal carotid artery, accounts for approximately 72% of total CBF  = 0.18 (0.10), p = 0.033) and the basilar artery (h2 (SE) = 0.24 (0.10), p = 0.0056) were significantly heritable. Our heritability estimates are slightly higher than these two prior studies. The increased heritability of CBF with age , Raffield et al.  found thThe current study showed strong GCs in CBF between cortical and subcortical GM regions, as well as among the three arterial territories, indicating a convergent genetic control for the blood perfusion in cerebral GM. This was supported by the strong effects from the latent factor in the common pathway model . This isThe fundamental differences in the labeling techniques between PCASL and PASL, and the consequent differences in the SNR and image quality, might have impact on the current findings, as indicated by the varied heritability estimates between PCASL and PASL . MacrovaAll datasets generated for this study are included in the manuscript and/or the OATS has been approved by the ethics committees of the Australian Twin Registry, University of New South Wales, University of Melbourne, Queensland Institute of Medical Research, and the South Eastern Sydney & Illawarra Area Health Service. Informed consent was obtained from all participants, and the methods were carried out in accordance with the relevant guidelines.JJ, WW, AT and PS designed the study and interpreted the data. JJ, FK and TL processed the ASL data. JJ drafted the manuscript. FK, WW, PS and QX revised the manuscript critically for important intellectual content. PS, JT, DA, MW and VC acquired the data. All authors read, edited and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In addition, the lead SNP in 4p16.2 for atrial septal defect and the lead SNP in 3q25.32 for tetralogy of Fallot are less than 250 Kb distant from the lead SNPs for NB at the same genomic regions. Some of these shared susceptibility loci regulate the expression of relevant genes involved in NCC formation and developmental processes  and are enriched in several epigenetic markers from NB and fetal heart cell lines. Although the clinical correlation between NB and CHD is unclear, our exploration of a possible common genetic basis between NB and a subset of cardiac malformations can help shed light on their shared embryological origin and pathogenetic mechanisms.In the past years, genome wide association studies (GWAS) have provided evidence that inter-individual susceptibility to diverse pathological conditions can reveal a common genetic architecture. Through the analysis of congenital heart disease (CHD) and neuroblastoma (NB) GWAS data, we aimed to dissect the genetic susceptibility shared between these conditions, which are known to arise from neural crest cell (NCC) migration or development abnormalities, via identification and functional characterization of common regions of association. Two loci (2q35 and 3q25.32) harbor single nucleotide polymorphisms (SNPs) that are associated at a  Neuroblastoma (NB) is an embryonic tumor arising from the sympathetic nervous tissue and is among the most frequent cancers diagnosed in early infants, accounting for 13% of all deaths due to childhood malignancies , sporadiCongenital heart disease (CHD) is one of the most frequent inborn disorders in infants, affecting 7 in 1000 live births and is a major cause of childhood death and long term morbidity . ComplexPHOX2B, a susceptibility gene for familial NB [Neural crest cells (NCC) development and migration abnormalities have been conjectured to be implicated in the genesis of both CHD and NB ,14,15,16ilial NB . In spitDemonstrating a correlation between NB and CHD could provide useful information to patients suffering from these conditions, including the opportunity of specific genetic counseling addressing the possible onset of the other disease. Although epidemiological studies are a powerful tool for addressing this question, genome wide association studies (GWAS) can provide a deeper level of understanding of the genetics underlying phenotypic traits, including pathological conditions. The accumulation of large-scale genomic datasets has led to the detection of novel loci associated with diverse traits and enhanced the study of shared genetic factors across phenotypes, but a thorough characterization of these identified loci would be advisable, both at the genetic and epigenetic level. Using data from different conditions can reveal the presence of common genetic risk factors and shared causal pathways, thus improving our understanding of disease.Given NB and CHD common embryological derivation from NCC ,24, we aGWAS summary statistics were taken from the work of McDaniel and colleagues . These rGenotypes from 5159 controls and patients with seven different subtypes of CHD, namely atrial septal defect/patent foramen ovale , conotruncal malformations , double outlet right ventricle , left-sided malformations , transposition of the great arteries , tetralogy of Fallot , and ventricular septal defect , were those included in the work of Cordell and colleagues [We used the Michigan Imputation Server  to perfoDosage vcf files from the imputation output were fed to SNPTEST v2.5.4 beta1  software2 threshold of 0.2  in order to consider only SNPs in approximate linkage equilibrium and evaluated the number of independent SNPs with association p-values above and below several thresholds  in NB and in each CHD condition. This procedure prevents the overestimation of association signals due to LD structure . If the two conditions do not share a genetic basis, these values should not deviate from random expectation. A 2 × 2 table for each p-value cutoff was created and one-sided Fisher exact tests were used as the statistical measure of significance and strength of association. We also ran simulations to assess the validity of our results: For each CHD condition, each SNP was randomly assigned a p-value from the list of observed p-values deriving from the association analysis for that condition. We repeated this a thousand times, and an empirical p-value was calculated from the proportion of simulations having a number of SNPs below a given p-value threshold in both datasets (NB and one CHD type) greater than or equal to the observed number of SNPs below the same p-value threshold in both real datasets (NB and one CHD type) under consideration.The first step of our workflow  was the p-value < 10−3 [To identify shared association signals between NB and CHD, SNPs with an association e < 10−3  in both p-value < 10−5 in NB or at least one CHD dataset, and candidate regions were identified if at least 10 SNPs within a distance of less than 100 Kb were present. The threshold of p-value < 10−5 was chosen as it corresponds to an average of 1 false-positive association per GWAS in European populations [To identify possibly colocalizing but distinct association signals, we selected SNPs with an association ulations . The disp-value was calculated from the proportion of simulations having a number of regions less than 250 Kb distant between NB and CHD greater than or equal to the real datasets.We also ran simulations to assess the significance of these distinct, colocalizing signals by randomly reshuffling the location of the associated regions in our NB dataset while keeping their size fixed: An empirical A method proposed by Pickrell and colleagues  was alsoEnrichment in epigenetic features of several cell types related to neural crest cells (NCC) and heart, and NCC derived tumors (NB and melanoma) was computed through R VSE package from CRAN . BrieflyLinDA  was usedp-value above and below different thresholds. We used Fisher exact test and simulation analysis to evaluate whether NB and each CHD condition in turn share more SNPs above and below the p-value thresholds than expected by chance [p-value less than 0.01 are shared more frequently than expected between NB and all CHD datasets . Common association signals are also observed for low p-value thresholds when considering NB and CM  and when considering NB and VSD  and for high p-value thresholds when considering NB and ASD/PFO .To evaluate the genome-wide shared genetic signals between NB and CHD, we selected a subset of independent SNPs in approximate linkage equilibrium with each other and evaluated for each condition the number of SNPs with association y chance  . p-value < 10−5 and their relative distance.Following recent works that have pointed out the importance of effects mediated by distinct genetic determinants located in the same genomic regions for informing the causal relationship between different traits ,39,40,41We also used a Bayesian method designed to test whether some genomic regions may harbor distinct variants associated to multiple traits  see Mat. We founp-value < 10−3 in NB and in at least one CHD subset in the regions reported in p-value = 0.024) in these cross-associated variants.Epigenetic features overlapping genetic polymorphisms can help predict in which cell tissue that variant is likely to act . Therefop-value < 10−3) and distinct  colocalizing signals and queried them for eQTL annotation through LinDA (http://linda.irgb.cnr.it/). Genes, variants, and tissues are listed in We used the genetic variants from the shared  show an opposite effect in several cases, possibly implying opposite functional changes in different cells/tissues affecting the same molecular trait or pathway .MSX1 (4p16.2) is a homeobox gene involved in neural crest specification [MSX1 expression and can also predispose to NB. The role of MSX1 in NB biology is also supported by a recent paper that demonstrates a signaling axis leading from PHOX2B via MSX1 to Delta–Notch and proneural gene expression in NB pathogenesis [MSX1 gene [Some of the regions detected by our colocalization analysis include intriguing candidate genes for NB and CHD. fication  that hasfication . Our resogenesis . RecentlSX1 gene .SHOX2 (3q25.32), a member of the homeobox family which is one of the major genes involved in the development of the sinoatrial node [SHOX2 is implicated in specifying neural systems involved in processing somatosensory information, as well as in face and body structure formation [Another relevant gene is ial node ; its proial node . Moreoveormation ,53 and hormation ,54. The Our results and those from the literature show that the aforementioned genes are involved in developmental processes and that their abnormal functioning due to genetic alterations could predispose to the development of NB and CHD.MLF1, RP11-538P18.2, and RSRC1) are associated with variants relevant in at least four conditions: NB, DORV, ToF, and VSD. MLF1 in particular has been recently described in NB [MLF1 is highly expressed in heart and has been identified as a novel modulator of cardiomyocyte proliferation [MLF1 expression.eQTL analysis points out the relevance of loci associated at 3q25.32; in fact 3 genes (feration . InteresBARD1 (2q35) [BARD1 locus have been previously associated to developmental delay, coarctation of aorta and ToF [BARD1 in early organogenesis and heart formation.We found that the known NB susceptibility gene 1 (2q35) ,56 lies  and ToF , suggestThe identification of regions of shared susceptibility can help in assigning a hierarchy in the pathogenic mechanisms of related conditions, and functional and epigenetic characterization of common associated SNPs from different traits can contribute to single out loci belonging to shared and unique pathways. Our results suggest a possible common genetic basis between these two NCC originating conditions. However, larger sample sizes and further studies will be needed to validate our results and better elucidate the shared genetic risk factors between NB and CHD.


The Murciano-Granadina goat is a local breed of importance, not only for the economic and social impact of the breeders but also for its conservation. Estimates of the genetic parameters of peak and persistency traits (not commonly used in breeding schemes) using multivariate models is a feasible tool in early lactation  for improving sustainable production in dairy goats, specifically when more data are available. The genetic variability for the parameters of the lactation curve  is low. The heritability was low to intermediate in all the traits, being between 0.08 for persistency and 0.17 for yield. The genetic correlations were high for peak yield and yield (0.94), indicating that the selection for both peak production and persistence is feasible, with no detrimental response in either. Murciano-Granadina should be a guide dairy goat, and the result could provide a general strategy applicable to other local breeds.2)/repeatability (re) of PY, yield (Y) and P was estimated as 0.13/0.19, 0.16/0.25 and 0.08/0.09 with the uni-trait and h2 of bi- and multi-traits analysis ranging from 0.16 to 0.17 of Y, while that of PY and Y remained constant. Genetic correlations were high between PY–Y (0.94 ± 0.011) but low between PY–P (–0.16 ± 0.054 to –0.17 ± 0.054) and between Y–P (–0.06 ± 0.058 to –0.05 ± 0.058). Estimates of h2/re were low to intermediate. The selection for Y–PY or both can be implemented given the genetic correlation between these traits. PY–P and Y–P showed low to negligible correlation values indicating that if these traits are implemented in the early stages of evaluation, they would not be to the detriment of PY–Y. The combination of estimated breeding values (EBVs) for all traits would be a good criterion for selection.This paper studies parameters of a lactation curve such as peak yield (PY) and persistency (P), which do not conform to the usual selection criteria in the Murciano-Granadina (MG) breed, but are considered to be an alternative to benefit animal welfare without reducing production. Using 315,663 production records  over a period of 24 years (1990–2014), genetic parameters were estimated with uni-, bi- and multivariate analysis using multiple trait derivative free restricted maximum likelihood (MTDFREML). The heritability (h Over a period of 30 years the Murciano-Granadina (MG) breed has been consolidated in European and international agriculture as one of the main dairy goat breeds based on its population, geographical distribution, quality and production, where practically all milk obtained is destined for the cheese industry. Currently, this Spanish breed of goat is considered the most exported due to its hardiness (adequate adaptation to a new environment) and its extensive grazing capacity; MG is a permanent polyestric animal with high prolificacy and easy milking .The breed has an improvement program that provides genetic parameters to measure milk, fat, protein and dry extract performance criteria that guide selection decisions . Based oThe estimation of parameters for economically important traits makes it possible to predict direct and correlated selection responses , improving the selection indexes ; being pIn this instance, using multivariate analyses has a great importance in providing reliable and unbiased estimates of genetic parameters  in animaPrevious studies of lactations curves in goats ,7 have iTo our knowledge, the modeling of individual lactation curves of P by Spline and subsequent multi-traits combined analyses for PY, yield (Y) and P traits have not yet been reported in the literature. PY and P traits would constitute a point of support for decision making within the genetic breeding improvement scheme, given the possibility of including them as additional criteria in the future. Given this background, the objective of the current study was to estimate genetic parameters, (co)variance components, their correlations (genetic and phenotypic), prediction of genetic values  and their accuracy of PY, Y and P traits using a multiple trait model. We tested the hypothesis that PY and P traits can be used as new selection criteria in the breeding program of the MG breed using multi-traits models. This information is also useful in increasing lactation length and will be able to provide decisive results that should be taken into account in the near future for breeding programs The genealogical information, the records of the milk control  and datAn exploratory data analysis, exhaustive review of all lactations and filters  was applied through the debugging of the database. The final file consisted of 1,349,347 records, which corresponded to 180,872 lactations belonging to 85,404 goats of 229 herds.The age of the active animals was between four and eight years, where the animals between four and five years of age represented 47.55%; and the range of six and seven years of age 33.68%; there being a significant presence of females with more than eight years of age (17.77%).Pedigree files included a total of 38,756 individuals , for the coding Pedigree Viewer 6.5 computer program developed by Kinghorn and Kinghorn  (where L represents the likelihood function) was <1 × 101 and Z2 = are incidence matrices that are relative to fixed effects, additive genetic effects and permanent environmental effects, respectively.The description of the model in matrix notation was:The calculations of the genetic parameters, the best linear unbiased prediction (BLUP) solutions for the fixed effects  and their accuracies of the animals for PY, Y and P traits, were obtained by solving the mixed model equations same as that used in León . The thrModel uni-trait was used for the univariate analysis: Each trait  was studied as a separate trait. The variance-covariance matrix of random effects was structured according to León .Model bi-traits was used for the bivariate analysis: Each pair of traits:  was independently analyzed. Model multi-traits was used for the multivariate analysis: In this instance, three traits  were analyzed simultaneously.iy = vector of observations for the i-th trait; ib = vector of fixed effects that contains the effect of HYS (herd–year–season), LN and TK to the i-th trait; ia = vector of direct additive genetic effects for the i-th trait; pi = vector of permanent environmental effects for the i-th trait; ie = vector of residual effects for the i-th trait and iX and Z are design matrices that relate the data using fixed and random effects, respectively.Matrix notation models of statistical and genetic analyses:The (co)variance structure of the random effects  for the models were structured and designed according to León  and Espih2 and er were calculated by the following formulas:h2 is the heritability; er is the repeatability; The parameters of Standard errors were estimated using the procedure cited in León , which igr), and phenotypic (pr) correlations on all pairwise combinations of traits  were obtained from the estimation of covariance components using the following formulas:aijσ = additive genetic covariance between trait i and j,2aiσ = additive genetic variance for trait i,2ajσ = additive genetic variance for trait j; pijσ = phenotypic covariance between traits i and j, 2piσ = phenotypic variance for trait i and 2pjσ = phenotypic variance for trait j. The estimates of genetic  according to uni-, bi- and multi-traits analyses were higher for PY and Y (ranged from 0.014 to 0.015) than for P . The variance of permanent environmental (2σpe) effects (σ2e) and phenotypic (σ2p) variances also showed oscillations, where PY and Y had increased estimates in bi- and multi-traits analyses compared to the uni-trait estimates for P was constant across all analyses.The estimated genetic variance  and Y (0.17) than for P (0.08).In a general way, the estimated hn values . The ave2 was slightly increased; whereas the other traits (PY and P) maintained a constant h2 with respect to uni-trait analysis. However, in this table, it can be noted that in the multivariate analysis (bi- and multi-traits) for Y trait the he for the P and PY traits in the three analyses was low . However, re of bi-trait (PY–Y combination) and multi-traits (PY and Y traits) analysis confirmed a slight increase, due to oscillations shown in their values with respect to uni-trait analysis. In turn, the re of the P trait in uni- and bi-traits was maintained, but in multi-traits showed a slight increase variance, genetic parameters, genetic and phenotypic correlations, accuracy breeding value for the PY, Y and P traits using uni-, bi- and multi-traits analysis with full convergence and consistent results. In fact, invariability of the estimation from uni- to multi-traits models suggests a good signal as quality control of results. To the best of our knowledge, this is the first study where the PY and P traits (traits of economic interest) are evaluated together with selection object traits of the program . The multi-traits analysis is suggested to analyze traits that have not been considered as selection criteria  but that belong to a population that has been subjected to selection by other traits . According to Baselga , there iThe results of the variance components estimates (VCE) from the uni-trait model until the simultaneous analyses showed good convergence and were similar. This is in agreement with Meyer , who ind2a contributes little to the total variance. Consistent with this, it should be taken into account that the environment does not directly modify the genetic constitution of the individual, but it does determine the extent to which it is expressed, the genetic potential of the animals expressed and their respective interactions.The variance component denotes that there are traits predominantly influenced by environmental effects, where the σ2a obtained in uni-, bi- and multi-traits analyses, was low, indicating a greater impact of the other variances . This evidence was corroborated by Oldenbroek and van der Waaij [2a effects indicate that the traits are predominantly influenced by environmental factors, such as nutrition, climate, exposure to diseases, factors that vary from one farm to another or between individual animals on the same farm, and by the contributions of the dominant or epistatic genes.The σer Waaij  and Espier Waaij , who dis2 estimates for traits using uni-, bi- and multi-traits analyses (ranging from 0.08 to 0.17), suggest that these are complex traits, which remain stable for several generations. This means a slow and often unstable selective effort and reaffirms the great environmental influence and not only the possible genetic variations existing between animals.The low h2 values, these can be measured if there is available information on a large number of animals; as this is economically important for farmers, the evaluation of these traits was justified. Oldenbroek and van der Waaij [2e.Although these traits have low her Waaij  pointed er Waaij  mentione2 (0.13) obtained for the PY trait in MG goats were low and consistent between the three models. In turn, to the best of our knowledge there are no reports of h2 in the literature for this trait in MG goats. In this regard, a report of Pollot and Gootwine [2 of 0.12, placing our results close to that report in sheep. Nevertheless, Devendra and Liang [The estimates of hGootwine  on this nd Liang  suggest In relation to this, Oliveira et al.  indicate2 of the Y trait, Kominakis et al. [Regarding the hs et al.  stresses2 ranges from 0.11 to 0.30 with four Spline knots. García-Peniche et al. [2 for all of them is 0.35.In the last decade, the reports continue to show variations such as the studies of Thepparat et al.  with seve et al.  with sev2 for the Y trait in MG goats were 0.16  to 0.17 . These results are within the ranges described in previous studies conducted on the breed that report a lower to intermediate h2 with complete and standardized milk yields at 210 and 240 days, respectively. León and Delgado et al. [2; while an even lower h2 was reported by Gonzalez-Peña [In our study, we found that the estimates of ho et al.  describelez-Peña  in the b2 of P trait in MG goats were the lowest (0.08) in the three models analyzed. This value was below the reported Menéndes-Buxadera et al. [2 of 0.20 performed with a random regression analysis for the first and second lactation in milk yield between week 17 and 35 in lactation.The ha et al.  in the MHowever, our results are closer to the results reported by González-Peña  in MG an2 estimates with different types of models  respectively was found by [The diversity of estimates for measures of P in goats are also present in studies of reference for dairy cattle, where a large range of hfound by ,27,28.All this variability of results is due in part to the different methodologies used, but we believe that an independent analysis of each lactation curve in different lactations by goats will bring us closer to the “real” value of P to have a measure of biological efficiency. Thereafter, the genetic relationship with other important traits such as the somatic cell score, reproductive traits and longevity would be analyzed. In this regard, Arnal et al.  highlighThe importance of this trait is defined by having a flatter lactation curve without a decrease in the total production (desirable). Its economic value in itself, due to its favorable correlation with factors as health  and ferte estimates for lactation curve traits using uni-, bi- and multi-traits analysis were low for PY and P traits (between 0.09 and 0.20), whereas the Y trait had a medium re (about 0.3). For the Y trait using the multi-traits vs. uni-trait analysis, both parameters (h2 and re) increased slightly. Estimates for the Y trait were similar to the previous reports for the MG breed [e tends to be twice the h2. Nevertheless, in the case of PY and P traits, no re reports were found in the literature for MG goats. In the current study, the rMG breed , but areMG breed  who desce is the maximum limit of h2, and their calculation is necessary because although it does not indicate genetic variation, it does allow us to establish a limit to it and will also indicate the relationship of how many records of the same trait must be obtained at different times of the animal’s life, before being discarded [The parameter of riscarded .The genetic correlations observed for PY–Y traits were large , while PY–P and Y–P traits showed low to negligible (negative) correlation values. Consistent with this, Falconer and Mackay  suggestsAnalyzing more dairy traits  as a whole and their correlations, allows us to adequately weigh the possible negative indirect genetic responses between traits and facilitate its use to rank animals and make breeding decisions .This positive genetic correlation between traits also indicates that an improvement of one trait has a positive impact on the other , and thaA current study with Alpinas and Saanen goats corroborate that estimates of higher genetic correlations will be seen in estimates at shorter intervals . Our resAs for the evidence for a strong association between traits, Oldenbroek and van der Waaij  points oOldenbroek and van derWaaij  describeThe genetic correlations obtained for PY–P and Y–P traits (negative) in goats, to the best of our knowledge were not reported but was described in other ruminants. For instance, a study in buffaloes have provided evidence for the correlations of PY–P, where mean correlations (–0.24) was reported by Pareek and Narang . This reTherefore, given that the correlations obtained in this study are smaller, we advise caution in the selection for these traits, because it may bring antagonistic responses at certain points along the length of lactation. Nevertheless, in sheep, a previous study indicates this type of negligible correlations, suggesting a flexible scope for modifying lactation curves via genetic selection .2 as in our results, these phenotypic correlations will be caused mainly by the environmental correlation. In the case of PY–P traits, they showed a phenotypically low correlation (negative), indicating a low antagonism between these traits.Regarding the phenotypic correlations, the PY–Y traits showed good (positive) correlation. Consistent with this, Falconer and Mackay  describeIn dairy cattle, the phenotypic correlations between Y and P were not significant . TherefoFurthermore, the accuracies calculation for PY, Y and P traits shows stable to slight improvement values from the uni-trait model to multi-traits model. Consequently, the multi-traits analyses will improve accuracies by a better connection between data due to the residual covariance between traits .This enhanced accuracy is due to the ability of the model to account for the relationship between the traits and better connections in the data due to residual covariance between traits . LikewisFinally, this study provides evidence that PY and P (traits of interest), evaluated in conjunction with criteria object of selection  using a repeatability model, can be used in the future as a point of support in making decisions about the criteria of selection within the scheme of selection of the MG breed. In future investigations, it is also suggested to evaluate the PY and P traits in combination with other measures .2 such as the P trait, the possibility of optimally selecting the lactation curve [Even for lower values of hon curve  is sugge2 estimates were low and close to intermediate for all traits, indicating that the major part of variation for those traits were due to environmental factors. Therefore, this suggests that these traits are very complex and remain quite stable for several generations. The re estimates for PY and P traits were low confirming that these traits are mainly influenced by managerial and temporary environmental effects, whereas the estimates for the Y trait had a medium value. The lowest levels of additive genetic variability for PY, Y and P, were estimated in the MG goat breed. The h2 and re) increased slightly. Genetic correlations between PY and Y were large and positive, indicating that it is possible to select for both traits, whereas PY–P and Y–P traits showed low to negligible and negative correlations. The phenotypic correlations indicated low antagonism and levels of significance.Even, for the Y trait using the multi-traits vs. uni-trait analysis, both parameters  shows more stable values from the uni-trait model to multi-traits model. Therefore, this suggests that multi-traits analysis will improve accuracies by a better connection between data due to residual covariance between traits.


Polygenic risk scores (PRS) combine many single-nucleotide polymorphisms into a score reflecting the genetic risk of developing a disease. Privé, Aschard, and Blum present an efficient implementation of penalized logistic regression... Polygenic Risk Scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The “Clumping+Thresholding” (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T. In this paper, we present an efficient method for the joint estimation of SNP effects using individual-level data, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. We also provide an implementation of penalized linear regression for quantitative traits. We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. Overall, we find that PLR achieves equal or higher predictive performance than C+T in most scenarios considered, while being scalable to biobank data. In particular, we find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, in simulations, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC values of 89% and of 82.5%. Applying penalized linear regression to 350,000 individuals of the UK Biobank, we predict height with a larger correlation than with the best prediction of C+T (∼65% instead of ∼55%), further demonstrating its scalability and strong predictive power, even for highly polygenic traits. Moreover, using 150,000 individuals of the UK Biobank, we are able to predict breast cancer better than C+T, fitting PLR in a few minutes only. In conclusion, this paper demonstrates the feasibility and relevance of using penalized regression for PRS computation when large individual-level datasets are available, thanks to the efficient implementation available in our R package bigstatsr. POLYGENIC risk scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS are useful for genetic epidemiology when testing polygenicity of diseases and finding a common genetic contribution between two diseases  is used to derive PRS from results of Genome-Wide Association Studies (GWAS) . This teWe recently developed two R packages, bigstatsr and bigsnpr, for efficiently analyzing large-scale genome-wide data , while being scalable to biobank data.We use real genotypes of European individuals from a case-control study for celiac disease  with a prevalence of 30% . We varyp) is also equivalent to increasing the sample size (n) as predictive power increases as a function of We implement three different simulation scenarios, summarized in In this study, we use two different measures of predictive accuracy. First, we use the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) . In the In this paper, we compare three different types of methods: the C+T method, T-Trees and PLR.i.e., the estimated effect size j along with a corresponding P-value P-values larger than a user-defined threshold i is defined as the sum of allele counts of the remaining SNPs weighted by the corresponding effect coefficientsP-values) learned from the GWAS. In this study, we mostly report scores for a clumping threshold at P-value under the GWAS threshold of significance . In GWAS, a coefficient of regression (Trees inside Trees) is an algorithm derived from random forests .T-Trees , y is the disease status to predict, λ and α are two regularization hyper-parameters that need to be chosen. Different regularizations can be used to prevent overfitting, among other benefits: the L2-regularization , or any situation involving many correlated predictors .In practice, a particular value of the threshold of inclusion of SNPs should be chosen for the C+T method, and this choice can dramatically impact the predictive performance of C+T. For example, in a model with 30 causal SNPs, AUC ranges from <60% when using all SNPs passing clumping to 90% Concerning the nsidered .λ provides similar predictive performance than the best predictive performance of 100 models corresponding to different values of λ .Our implementation of PLR that automatically chooses hyper-parameter We tested the T-Trees method in scenario N°1. As compared to PLR, T-Trees perform worse in terms of predictive ability, while taking much longer to run . Even for simulations with model “COMP” in which there are dominant and interaction-type effects that T-Trees should be able to handle, AUC is still lower when using T-Trees than when using PLR .vs. PLR3 that uses additional features (variables) coding for recessive and dominant effects. Predictive performance of PLR3 are nearly as good as PLR when there are additive effects only  and can lead to significantly greater results when there are also dominant and interactions effects . For model “COMP,” PLR3 provides AUC values at least 3.5% higher than PLR, except when there are 3000 causal SNPs. Yet, PLR3 takes two to three times as much time to run and requires three times as much disk storage as PLR.We also compared the two PLRs in scenario N°1: PLR First, when reproducing simulations of scenario N°1 using chromosome six only (scenario N°2), the predictive performance of PLR always increase . There ivs. 130 sec), and PLR3 takes less than twice as long as PLR (296 vs. 190 sec).Joint PLRs also provide higher AUC values for the Celiac data: 88.7% with PLR and 89.1% with PLR3 as compared to 82.5% with “C+T-max” . Results are presented in Our implementation of L1-penalized linear regression runs in <1 day for 350K individuals (training set), achieving a correlation of >65.5% with true height for each sex in the remaining 24K individuals (test set). By comparison, the best C+T model achieves a correlation of 55% for women and 56% for men (in the test set), and the GWAS part takes 1 hr (for the training set). If using only the top 100,000 SNPs from a GWAS on the training set to fit our L1-PLR, correlation between predicted and true heights drops at 63.4% for women and 64.3% for men. Our L1-PLR on breast cancer runs in 13 min for 150K women, achieving an AUC of 0.598 in the remaining 39K women, while the best C+T model achieves an AUC of 0.589, and the GWAS part takes 15 hr.In this comparative study, we present a computationally efficient implementation of PLR. This model can be used to build PRS based on very large individual-level SNP datasets such as the UK biobank  C+T. For example, this situation occurs when there are many causal variants (3K) to distinguish among many typed variants (280K) while using a small sample size (6K). In such underpowered scenarios, it is difficult to detect true causal variants, which makes PLR too conservative, whereas the best strategy is to include nearly all SNPs (When increasing sample size (scenario N°3), PLR achieves higher predictive performance than C+T and the benefits of using PLR over C+T increase with an increasing sample size . MoreoveP-value thresholds, a threshold that should normally be learned on the training set only. To our knowledge, there is no clear standard on how to choose these two critical hyper-parameters for C+T. So, for C+T, we report the best AUC value on the test set, even if it leads to overoptimistic results for C+T as compared to PLR.The choice of hyper-parameter values is very important since it can greatly impact the performance of methods. In the C+T method, there are two main hyper-parameters: the In contrast, for PLR, we developed an automatic procedure called CMSA that releases investigators from the burden of choosing hyper-parameter λ. Not only this procedure provides near-optimal results, but it also accelerates the model training thanks to the development of an early stopping criterion. Usually, cross-validation is used to choose hyper-parameter values and then the model is trained again with these particular hyper-parameter values . Indeed, the algorithm is composed of two steps: first, for each variable, the algorithm computes an univariate statistic that is used to decide if the variable is included in the model . This first step is very fast. Then, the algorithm iterates over a regularization path of decreasing values of λ, which progressively enables variables to enter the model . In the second step, the number of variables increases and computations stop when an early stopping criterion is reached .For highly polygenic traits such as height and when using huge datasets such as the UK Biobank, the algorithm might iterate over >100,000 variables, which is computationally demanding. On the contrary, for traits like celiac disease or breast cancer that are less polygenic, the number of variables included in the model is much smaller so that fitting is very fast (only 13 min for 150K women of the UK Biobank for breast cancer).Memory requirements are tightly linked to computation time. Indeed, variables are accessed in memory thanks to memory-mapping when they are used , as well as highly polygenic traits  provided that sample size is not too small. Finally, PLR as implemented in bigstatsr can also be used to predict phenotypes based on other omics data, since our implementation is not specific to genotype data.We also demonstrated in simulations and real datasets that our implementation of penalized regressions is highly effective over a broad range of disease architectures. It can be appropriate for predicting autoimmune diseases with a few strong effects (


RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes,  Although, there is increasing evidence that the risk and course of HF depend on genetic predispositions2, genome wide association studies (GWAS) have identified only a handful of genetic variants associated with it. For instance, the chromosome region 9p21 includes several highly replicated genetic variants associated with HF risk factors (e.g. NC_000009.11:g.22096055A > G and NC_000009.11:g.22124477A > G)3.Heart failure (HF) is a complex clinical syndrome characterized by abnormal cardiac structure and function that leads to reduced cardiac output and elevated filling pressures at rest or with exertion5. One of the largest studies on African-American population identified four variants associated with left ventricular mass and left ventricular internal diastolic diameter respectively using Echocardiography6. A meta-analysis on 5 cohorts of individuals with European ancestry identified five genetic loci harboring common variants associated with left ventricular diastolic dimensions and aortic root size7. More recently, a meta-analysis of a large set of samples including 73,518 individuals identified 32 novel loci associated with electrocardiographic markers of hypertrophy as an important and independent risk factor for the development of heart failure8.For better understanding heritability of HF, some studies combine results of multiple cohorts and involve more samples in the analysis through meta-analysis12. Although there are many studies on multi-trait approaches, applications of those methods have recently received increased attention e.g.15. A limitation of those methods is their complexity due to the large number of parameters in the model.Taking advantage of dozens-to-hundreds of traits measured on each study participant creates opportunities to obtain insights into the biology of HF, and consequently reduces morbidity, and economic burden of HF. Multi-trait analysis is toward this aim and increases the statistical power16 with a Bayesian multi-trait polygenic mixed model while setting G-Wishart prior17 on inverse of relatedness matrix and called it Integrative Bayesian Multi-Trait (IBMT) approach. Using IBMT approach, we conducted an analysis to identify genomic variants influencing 10 echocardiographic traits related to cardiac structure and function from Atherosclerosis Risk in Communities (ARIC) study18. We have genotype data of 7810 European American individuals from baseline measurement while 3387 of these individuals have phenotype records in Visit 5. The phenotype data are also recorded for 1265 new participants at visit 5 who do not have baseline genotype. Thus, we had three sets of data, including (i) individuals with only genotype data, (ii) individuals with only phenotype data, and (iii) individuals with both genotype and phenotype data. We incorporated information from all these three sets into the analysis to improve the statistical power, prevent overfitting, and avoid using data multiple times. The details are provided in the Methods section. These steps ultimately improve reliability and generalizability of the results.To reduce the complexity of the multi-trait approaches due to the number of parameters, we integrated a Bayesian networkRGS3, CHD3, and MRPL38 with significant impact on the cardiac traits. The variant in RGS3 gene (NC_000009.11:g.116346115C> A) showed pleiotropic gene action on vertical mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter). RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction and codes for GTPase-activating protein that inhibits G-protein-mediated signal transduction20. CHD3 encodes a protein with a chromatin organization modifier domain and a SNF2-related helicase/ATPase domain21. MRPL38 produces a mitochondrial ribosomal protein, involved in the synthesis of proteins within the mitochondrion23.After data preparation, we applied the IBMT method over the whole exome sequence data to investigate genomic and cardiac trait relationships. We identified 3 genetic variants  in genes 24. The data presented here includes 7810 European American individuals with baseline genotype available on dbGAP (https://www.ncbi.nlm.nih.gov/gap), accession number phs000090.v5.p1. A subset of individuals with genotype data, 3387 out of 7810 individuals, has phenotype records at visit 5. The phenotype data described in the following subsection are also recorded for 1265 new participants at visit 5 who do not have baseline genotype. Figure Echocardiographic and genomic data were collected on a subset of the ARIC study, a biracial longitudinal cohort of 15,792 middle-aged individuals who were randomly sampled from four US sites  and have been measured for risk factor traits related to health and chronic diseases. A detailed description of the ARIC study design and methods have been published elsewhere25. Images were obtained in the parasternal long- and short-axis and apical 2- and 4-chamber views. Primary measures of the traits such as left ventricular (LV) dimensions, volumes, and wall thickness; left atrial (LA) dimensions, volumes, and areas were made in triplicate from the 2-dimensional views in accordance with the recommendations of the American Society of Echocardiography26. This study includes 10 cardiac structure and function tabulated in Table Echocardiograms were obtained from participants at visit 5 using a standardized protocol as recommended by the American Society of Echocardiography. Images were digitally transferred to the Cardiovascular Imaging Core Laboratory at Brigham and Women’s Hospital, Boston, MA, for offline analysis. The intra-observer variability (coefficient of variation and interclass correlation) for key echocardiographic measures has been previously published27. The reads are mapped into the Genome Reference Consortium Human Build 37 (GRCh37) sequence. Low-quality variants are filtered if they were outside the exon capture regions, belonged to multi-allelic sites, had missing rate >20% and had mean depth of coverage >500-fold. In addition, highly significant departures from race-specific Hardy-Weinberg equilibrium  are excluded from the data.Whole exome sequencing was performed on samples with the Illumina HiSeq platform. Mercury pipeline is applied for variant callingThe IBMT as an integrative Bayesian approach takes into account the underlying relationship among multiple traits and seeks for their significant association with genetic variants through multi-trait polygenic mixed model as the followingith trait recorded for jth individual, βik is the effect of kth genomic variants on ith trait in coefficient vector U~N and Ψ is a relatedness matrix. The inverse of relatedness matrix (Ψ−1) represents the conditional dependence of samples between and within traits.Each entry of 1 and slows down the analysis. To overcome this limitation, we proposed to integrate the Bayesian network and the multi-trait polygenetic mixed model since Bayesian network represents the conditional dependence of the traits through graphical representation. Therefore, the IBMT first builds the Bayesian network among the traits of interest and then set the parameters in Ψ−1 to zero if their corresponding edges in Bayesian network are missing. To impose this sparsity to the model in posteriori, we set the G-Wishart prior on Ψ−1. The G-Wishart places no probability mass on zero entries of Ψ−1 and accordingly it reduces the number of parameters of the model and optimizes the performance and computational time of Gibbs sampling scheme. Further details of the model and the Gibbs sampling scheme are provided in the Supplementary, statistical methods section, although in the following, we presented general guidline on the IBMT application.In large scale settings, Ψ imposes numerous parameters into the modelThere is a broad consensus on analytic techniques for covariate adjustment to discover genomic variants associated with traits of interest independently of the correlated covariates, and to improve statistical power by gaining precision. The set of covariates typically include principal components of individual genotypes to account for population structure, and correlated environmental or demographic factors such as gender and age. However, some of those covariates may not have significant impact on the traits and adjusting for those covariates simply leads to a loss of power and cause variance inflation of the effects. Therefore, instead of adjusting each trait for all routine covariates, we first investigated the effect of covariates on the traits using 1265 individuals with only phenotypic record. This not only prevents decreasing the statistical precision or contaminating data due to adjusting for unrelated covariates but also avoids using data twice which prevents overfitting.30. The identified network, which is also supported by clinical background knowledge, revealed the sparsity level of the relationships. Figure 33 can be also applied for the same purpose, although they may estimate more connections among the traits. To avoid overfitting, we built the Bayesian network using the subset of individuals with only phenotype records.We first identified underlying relationships of the 10 cardiac functional and structural traits tabulated in Table Using the identified Bayesian network over the traits, we implemented the IBMT method and analyzed the whole exome sequencing data including 260,688 variants based on sliding window. Each window included 100 variants with a step size of 25, such that each variant appears in 4 windows. If a variant is selected in all 4 windows, we reported the variant as the most promising variant. The parameters of the model were updated at each iteration of Markov Chain Monte Carlo (MCMC) algorithm and estimated with posterior means, which calculate optimal point estimators under square error loss, after 200 burn-in period.35, , for the effects of all the variants to test a null hypothesis of no genomic effects. The endpoints of the intervals correspond to quantile (q) of the empirical distribution of the MCMC drawn from the marginal posterior distribution of genetic effects. A desired degree of precision for the endpoints of intervals is achieved by running a number of iterations untilqL and qU are lower and upper quantile respectively and i represents the number of iterations. We set To identify genomic variants significantly associated with the traits, we calculated 98% credible intervalSince the IBMT method is based on a linear polygenic mixed model, we first tested the normality of the traits as the main assumption. Except the ejection fraction and the maximal left atrial anterior-posterior diameter that are normally distributed, the other traits were transformed to normal using log transformation. The histograms of the traits after winsorization, standardization, and log transformation are represented in Supplementary Figure We then investigated the effects of gender, age, ever-smoked, body mass index (BMI), hypertension, systolic and diastolic blood pressure on the traits. Among them gender and BMI showed highly significant relationships  with all traits except the mean LV Wall Thickness (LV-WT) and the ejection fraction, which they were relatively less significant with P-values 0.05 and 0.005 respectively. Hypertension also showed significant effects  with all traits except the ejection fraction. We obtained these results on the set of individuals without genotype data to avoid the use of data twice. To generalize the results to the set of interest , we compared BMI distribution, gender ratio , and ratio of (with/without) hypertension in the two sets. We observed that in both sets, the distribution of BMI is similar Figure ; gender Applying IBMT, we first identified Bayesian network among 10 traits listed in Table Figure RGS3 (NC_000009.11:g.116346115C > A) with pleiotropic action, two of the females and one male showed different patterns with other individuals, such that their level of LV-MI, LA-VI and Max-LA-APD are not greater than third quartiles  showed pleotropic effect on LV-MI, LA-VI and Max-LA-APD. Among 14 individuals  having rare mutation in 36, this variant is most likely a missense mutation in RGS3  gene  that could yield a codon change from ACC to AAC replacing the amino acid Threonine (Thr) by an Asparagine (Asn) in the protein sequence. RGS genes encode GTPase activating proteins (GAPs) and down-regulate G protein signaling. More details about the results from the VEP analysis is provided in Supplementary Table According to the Variant Effect Predictor (VEP) analysisAnother identified variant with impact on PLAx-IST is in chromosome 17 (NC_000017.10:g.7802658C > T). Individuals with T allele of this rare variant, including 2 Females and 8 Males, all have PLAx-IST level greater than third quartile of the trait  gene based on VEP analysis (Supplementary Table CHD3 belongs to a family of genes coding for proteins that bear CHROMO (chromatin organization modifier) and SNF2-related helicase/ATPase domains. CHD3 protein is one of the components of the Mi-2/NuRD (histone deacetylase) complex that participates in the remodeling of chromatin structure via histone deacetylation.The variant NC_000017.10:g.7802658C > T is intronic to Chromatin Helicase DNA Binding Protein 3 (MRPL38 (NC_000017.10:g.73897977C > T). All 8 male individuals with rare mutation in MRPL38 have LV-MI level greater than third quartiles of distribution  by a Glutamine (Gln) concluded. Gln is a non-charged amino acid and smaller than Arg with putatively less capacity to create hydrogen bonds and favorable electrostatic interactions than Arg . The MRP family stabilizes mitochondrial ribosome (mitoribosome) and are responsible for the mitochondrial translation of 13 protein components of the Oxidative Phosphorylation (OXPHOS) gene complex in the mitochondrial DNA23.The variant NC_000017.10:g.73897977C > T can be in a non-coding exon or be a missense mutation of RGS3, CHD3, and MRLP38 genes are rare, hence a high statistical power is required to detect the association with the trait(s)40. Among those, the variant NC_000009.11:g.116346115C > A in RGS3 showed pleiotropic action on vertical mass index, left vertical volume index,  maximal left atrial anterior-posterior diameter  approach provides a sparse relatedness matrix and, eventually, more precise estimates of parameters in the model. Utilizing the IBMT method to increase the power of identification, in addition to carefully adjusting for covariates to avoid data contamination, and choosing the appropriate transformation function for each trait, we identified three significant genetic variants. These variants located in RGS3  which belongs to RGS family. RGS family codes for proteins that act as GAPs and down-regulate G protein signaling. Many studies have proven that RGS gene expression is highly regulated in myocardium43. Quantitative messenger RNA (mRNA) analysis revealed that RGS3 is most highly expressed in human heart46. The N-terminus of RGS3 can inhibit TGFβ induced differentiation of pulmonary fibroblasts which is associated with left ventricular dilation and systolic dysfunction47. The identified variant in RGS3 was associated with pleotropic effect on LV mass (LV-MI) and measures of left atrial size (LA-VI and Max-LA-APD). The left atrial size may reflect the cumulative effects of increased LV filling pressure and diastolic function48 and is a predictor of heart failure, ischemic stroke, and death. Thus, genetic variants contributing to abnormalities of LV mass and worsened diastolic function would be expected to potentially be associated with LA size.The variant NC_000009.11:g.116346115C > A is in the exon of gene RGS3 and Gα subunit, it could eventually affect GTPase activity.As missense mutation, NC_000009.11:g.116346115C > A could yield a codon change from ACC to AAC, which replaces a Threonine (Thr) to an Asparagine (Asn) in the protein amino acid sequence. The change from Thr to Asn does not alter side chain electrostatic charge because of both amino acids being electrostatically neutral. This could eventually affect hydrogen bond pattern since Asn has an extra hydrogen bond donor group (NH2). However, it is difficult to evaluate the final effect in the protein three-dimensional structure since there may be alternative spliced transcripts. If this mutation happened at the interaction interface between CHD3 gene, which codes for the Chromatin Helicase DNA Binding Protein 3 as a part of the chromatin structure remodeling complex. This is a potential splicing variant that could affect the rate of mature mRNA synthesis, and ultimately impact gene transcription.The variant NC_000017.10:g.7802658C > T that is significantly associated with parasternal long axis interventricular septum thickness (PLAx-IST) is in an intron of the MRPL38 gene, a member of Mitochondrial ribosomal proteins (MRP) family that are part of the large subunit of the mitochondrial ribosome. As missense mutation, the change of CGG into CAG codon causes the substitution of amino acid Arginine (Arg) by a Glutamine (Gln). Gln is a non-charged amino acid and smaller than Arg with putatively less capacity to create hydrogen bonds and favorable electrostatic interactions than Arg. If the amino acid mutation takes place at the interface between MRPL38 and mitochondrial ribosome, it could decrease binding affinity and destabilize mitochondrial ribosome. This in turn may reduce ribosomal protein synthesis levels and affect the oxidative phosphorylation pathway.The other identified variant NC_000017.10:g.73897977C > T is within Overall, this study suggests that rare mutation might provide a better understanding of genetic impact on cardiovascular structure and resulting in remodeling cardiovascular disease and/or heart failure, although further studies are required.Supplementary


Genomic selection (GS), a tool developed for molecular breeding, is used by plant breeders to improve breeding efficacy by shortening the breeding cycle and to facilitate the selection of candidate lines for creating hybrids without phenotyping in various environments. Association and linkage mapping have been widely used to explore and detect candidate genes in order to understand the genetic mechanisms of quantitative traits. In the current study, phenotypic and genotypic data from three experimental populations, including data on six agronomic traits , were used to evaluate the effect of trait-relevant markers (TRMs) on prediction accuracy estimation. Integrating information from mapping into a statistical model can efficiently improve prediction performance compared with using stochastically selected markers to perform GS. The prediction accuracy can reach plateau when a total of 500–1,000 TRMs are utilized in GS. The prediction accuracy can be significantly enhanced by including nonadditive effects and TRMs in the GS model when genotypic data with high proportions of heterozygous alleles and complex agronomic traits with high proportion of nonadditive variancein phenotypic variance are used to perform GS. In addition, taking information on population structure into account can slightly improve prediction performance when the genetic relationship between the training and testing sets is influenced by population stratification due to different allele frequencies. In conclusion, GS is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs. However, GBLUP) . In fact, GBLUP) . In addi, GBLUP) . Several, GBLUP) .With the improvement of statistical models, predication accuracy has increased due to the consideration of supplementary effects, such as nonadditive, fixed, and genotype-by-environment interaction effects . IntegraWith respect to the application of TRMs in GS, many studies have argued in favor of including TRMs as fixed effects in statistical models to enhance prediction accuracy. However, few reports directly integrating TRMs into GS models have been published . In this2:3 families, respectively. The natural population was grown in Henan Province in 2014 and 2015. The two biparental populations were evaluated in the same location in 2015 and 2016. A field trial with a randomized incomplete block design was performed with two replicates. Six yield-related agronomic traits constituted the phenotypic data: plant height , ear height , ear length , ear diameter , grain yield per plant , and hundred-kernel weight . Furthermore, phenotypic values of HKW and GYP were adjusted to 140 g/kg grain moisture.The plant materials were described in detail by lme4 version 1.1-21 with the following mixed linear model (MLM) (Thebest linear unbiased estimates (BLUEs) of genetic effects were estimated using the R package el (MLM) :yijl is the phenotypic value of the ith genotype evaluated in the lth environment with the jth replicate, μ is the overall mean, gi is the fixed genetic effect of the ith individual, el is the fixed effect of the lth environment, geil is the random interaction effect between the ith individual and the lth environment, rjl is the random effect of the jth replicate within the lth environment, and εijl is the model residuals. The BLUE values of individuals in each experimental population were used as phenotypic data to perform the subsequent analyses, including a GWAS, QTL mapping, and GS.where 2:3 population, the DNA extracted from leaves of F2 plants was assayed for obtaining genotypic data. Markers with a proportion of missing values >0.10 were removed from the three experimental populations. Finally, a total of 38,299 SNPs with minor allele frequencies (MAFs) > 0.05 were used for further analysis of the natural population. A total of 14,544 and 10,444 SNPs were retained based on chi-square tests (P > 0.01) for the RIL and F2:3 populations, respectively. The aim of chi-square test is to screen out markers without segregation distortion in biparental populations.All inbred lines from the natural and biparental populations were used for genotyping, which was performed with the novel developed maize 55 K SNP array . As for GAPIT version 3.0 with an MLM procedure considering population structure and relative kinship (Q + K model)(GAPIT.PCA function in the R package GAPIT. The determination of PC number was based on a scree test (10 (P) > 4 for each trait was employed to identify significant association signals for determining the accuracy and quality of association mapping. The R package CMplot version 3.3.3 was used to draw a Manhattan plot (https://github.com/YinLiLin/R-CMplot). The description of candidate genes based on association mapping was based on the maize genetics and genomics database Marker–trait association mapping was implemented in the R package  K model). PCA wasree test , and the2:3 populations were aligned and constructed with the sliding-window approach to investigate variant calling errors and calculate the ratio of SNP alleles derived from Zheng58 and HD568. A criterion with a window size of 15 adjacent SNPs and a step size of one SNP was applied to scan the genotypic data. Windows with 11 or more continuous SNPs derived from either parent were regarded as homozygous, and those with fewer SNPs from one parent were recognized as heterozygous. Adjacent windows with the same genotype were combined into one block, and these blocks with different genotypes were inferred to be at or near a recombination breakpoint, allowing bin markers to be designated when consecutive blocks lacked a recombination event across all RILs or F2:3 families  ratio for evaluating the significance of each QTL–trait association and for assessing the accuracy and quality of linkage mapping. A 1.5-LOD decrease corresponding to the peak value of the LOD for each bin was defined as the confidence interval for each QTL. Candidate genes identified by linkage mapping were described according to MaizeGDB. In addition, the bin markers were eventually used to perform further GS analysis.The bin maps of the RIL and Ffamilies . A linkaon 1.0-4 . Identif mapping . Then, 1rMG). Furthermore, the number of selected markers, including TRMs and randomly selected markers, was set using seven to eight levels  to test for a difference in prediction accuracy. As for the TRMs and randomly selected markers, the former was selected according to rank of −log10 in association mapping or LOD scores in linkage mapping, the latter was stochastically sampled from whole genome.The TRMs detected by association and linkage mapping were used to perform GS for each agronomic trait, and a fivefold cross-validation scheme with 100 replicates was implemented to partition the dataset of each experimental population into training and testing sets and then to calculate the mean correlation coefficient between GEBVs and BLUE values, which represented the prediction accuracy , where W = (wij) with wij = zij − 2pj (where j − 2pj .The TRMs (mentioned above) were used to fit GS models, and the extended GBLUP model can be described as :ua, ud, and uaa are the vectors of random effects for additive genetic effects (A), dominance effects (D), and additive-by-additive interaction (AA) effects and are assumed to obey the normal distributions N2. Thus, the dominance relationship matrix is Gd = nDD’/trace(DD’), where the trace is the sum of all diagonal elements. Then, the epistatic relationship matrix can be calculated by Gaa = Ga # Ga, where the octothorpe denotes the Hadamard product of matrixes.The extended GBLUP models were implemented only for F2:3 population. Four models were used to perform GS, namely, the A, A + AA, A + D, and A + D + AA models, and the A model was equivalent to the general GBLUP model. These extended GBLUP models were fitted using the R package BGLR version 1.0.8 (where on 1.0.8 .GAPIT.PCA function in GAPIT with TRMs (The TRMs (mentioned above) were used to fit the GS models. A PC matrix was constructed by the ith TRMs  and thenX is the design matrix for fixed effects consisting of PCs, β is the vector of fixed effect estimates, Z is the design matrix for random effects in the BC + PC model, and u is the vector of random effects in both models. Finally, the prediction accuracy assessed by fixed-effect models was compared with the rMG estimated by the general GBLUP model with TRMs. The fixed-effects models were fitted using the R package BGLR version 1.0.8  for each agronomic trait ranged from 1 to 4 . This phenomenon may result because considering more effects in models can increase their complexity and affect the goodness of fit , and dominance-by-dominance (DD) interaction effects, has long been recognized as essential component for dissecting the genetic architecture of target traits and understanding the genetic basis of quantitative traits . Several effects . For exa studies . However studies . The lars of fit . On the s of fit . For inss of fit ; howeverrMG. The first is constructing the training population to be closely related to the breeding population before implementing GS , the Agricultural Science and Technology Innovation Program at CAAS, and the National Basic Research Program of China (973 Program) (Grant No. 2014CB138200).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Considering 98 pigs from 10 different breeds with high-quality deep sequenced genomes, we imputed the whole genomic variants of 1020 F2 pigs genotyped by the PorcineSNP60 BeadChip with high accuracy and obtained 14,851,440 sequence variants after quality control. Based on this, 87 novel quantitative traits loci (QTLs) for 18 hematological traits at three different physiological stages of the F2 pigs were identified, among which most of the novel QTLs have been repeated in two of the three stages. Literature mining pinpointed that the FGF14 and LCLAT1 genes at SSC11 and SSC3 may affect the MCH at day 240 and MCV at day 18, respectively. The present study shows that combining high-quality imputed genomic variants and correlated phenomic traits into GWAS can improve the capability to detect QTL considerably. The large number of different QTLs for hematological traits identified at multiple growth stages implies the complexity and time specificity of these traits.The whole-genome sequences of progenies with low-density single-nucleotide polymorphism (SNP) genotypes can be imputed with high accuracy based on the deep-coverage sequences of key ancestors. With this imputation technology, a more powerful genome-wide association study (GWAS) can be carried out using imputed whole-genome variants and the phenotypes of interest to overcome the shortcomings of low-power detection and the large confidence interval derived from low-density SNP markers in classic association studies. In this study, 19 ancestors of a large-scale swine F Hematological traits, which are associated with the immune system , have beCXCR2 associated with the reduction of WBC count, a common missense variant in CPS1 and a rare synonymous variant in GFI1B that caused a lower platelet count, and a mass of genes and loci-related hematological parameters have been detected by adopting the genome-wide association studies (GWAS) approach  associated with swine hematological traits have been identified by linkage mapping recorded in the AnimalQTLdb database , but bec2 resource population using the PorcineSNP60 BeadChip, and we identified 185 significant SNPs distributed on chromosomes 1, 4, 5, 7, 8, 10, 11, 12, 13, 17, and 18, affecting hematological traits at three different growth stages , hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC), and red blood cell volume distribution width (RDW)], seven leukocyte parameters , and four platelet parameters .The description of the White Duroc × Erhualian Fications . In shor0 individuals are the progenitors of the 933 F2 resource populations (target panel) and 98 unrelated pigs are from nine diverse breeds and one wild boar population. Sixty-nine unrelated pigs were used in a study of adaptation and introgression project  of this SNP dataset can be found in our previous report  is critical for high-quality imputation of the F2 population. The number of individuals present in a population is a crucial factor in determining how well the phase can be estimated for haplotype construction. bam files by using the extractPIRs program from SHAPEIT appendix, setting the –base-quality and –read-quality options to 20 and 30, respectively. Combined with the variants files, PIRs were then involved in haplotype construction using the standard Markov chain Monte Carlo iterative procedure of the phasing module (-assemble) in SHAPEIT and the window was set to 0.2 Mb by the –window parameter. Haplotypes of the target panel were constructed and improved by taking advantage of both linkage and LD information using DualPhase software; It firstly phased a large proportion of loci and individuals based on Mendelian rules and linkage information using the pedigree relationship of the F2 population; then, the remaining small part of the unphased loci were haplotyped based on a hidden Markov model  between the best-guess imputed and the original variants were calculated as imputation accuracy.Subsequently, the genotype imputation between the target and reference panels with known phase information were performed by IMPUTE2 (v.2.3.2) . Specifi1 and three F2 individuals. The SNP calling of these five individuals was performed following the pipeline described above. The 2R value between the sequenced variants and the best-guess imputed variants across chromosomes was calculated.To investigate the imputation accuracy further, we carried out whole-genome resequencing for five experimental animals in the present study population including two F2 as it best balanced the imputation accuracy and the missing proportion in the next analysis process. Then, variants with a call rate <90% and a minor allele frequency (MAF) <0.01 were further excluded by PLINK.Furthermore, to obtain relatively accurate imputation results that would be used for the following GWAS, a series of methods were applied into the GEN file format from the imputation result. As the GEN file contains the information of genotype probability, we used a cutoff of 0.5 to convert the GEN file into a common PED file by GTOOLcor function using the Pearson correlation coefficient in the stats package.The estimated breeding value (EBV) of all experimental animals for 54 hematological traits were estimated by Bayesian sparse linear mixed models implemented in Gemma (v.0.94) . The pheA univariate linear mixed model (see Eq. 1) implemented in GEMMA (v.0.94) is employed for the single-marker association test between variants and phenotypes , and is y is the vector of phenotypic observation; W is a design matrix of covariates, including a column of 1 s; α is a vector of fixed effects ; x is a vector of genotypes at each locus; β is the effect of the marker; u is a vector of random effects following the multivariate normal distribution (see Eq. 1), in which τ-1 is the variance of the residual errors, λ is the ratio between τ-1 and the variance of polygenetic effects, and K is a kinship matrix estimated from whole-genome sequence variants; 𝜀 is a vector of errors following the multivariate normal distribution (see Eq. 1) and In is an identity matrix. Using naïve Bonferroni corrections of 0.05 divided by the number of examined SNPs to correct multiple comparisons would lead to an overly conservative threshold in our study because these SNPs were highly correlated with each other. where corrplot in the corrplot R package with the agglomeration method of “complete,” which employs the longest distance principle to generate clusters. Based on the clusters of phenotypic and genetic correlation from different stages, the multitrait association tests between variants and multiple correlated phenotypes were carried out by the multivariate linear mixed model (see Eq. 2) implemented in GEMMA (v. 0.94)  , which in and d are the numbers of individuals and phenotypes, respectively. Y is a n by d matrix of phenotypic observation; W is an n by c design matrix of covariates, including a column of 1 s; A is a c by d matrix of fixed effects ; x is a vector of genotypes at each locus; β is a d vector of marker effect sizes for d phenotypes; U is an n by d matrix of random effects following the n × d matrix normal distribution (see Eq. 2), in which K is a relatedness matrix generated by genotypes of n individuals, and Vg is a d by d matrix of genetic variance components; E is an n by d matrix of errors, in which In×n is an n by n identity matrix, and Ve is a d by d matrix of environmental variance components. The genome-wide significant threshold in multitrait GWAS was also set to 5E-08 as mentioned above.where 0, 68 F1, and 933 F2 were genotyped by the PorcineSNP60 BeadChip. The number of individuals measured for each trait ranged from 181 to 924  and 4,200,615 variants with a low MAF (0.01) were discarded further in the QC process. Thus, a total of 14,851,440 variants were retained for the following GWAS process  and 87% (s1143) across individuals . Variants affecting HCT, HGB, and RBC at day 18 are located on SSC9 and near within 1 Mb, and are treated as identical QTLs . The nearest gene in this region is the serine/threonine kinase 33 (STK33) gene. We also identified a QTL at 304,622,788 bp located on SSC1 that possibly affected HCT and HGB at day 18. The most significant SNP (P = 1.51E-09) was located on the far upstream element-binding protein 3 (FUBP3) gene. Several significant loci were detected for erythrocyte traits at day 46, especially for HCT, MCHC, and MCV . The most significant SNPs (P = 1.97E-09) were exactly located within the fibroblast growth factor 14 (FGF14) gene. The two novel QTLs associated with MCHC were located on SSC6 and SSC17, respectively. More information about significant SNPs are listed in Table One hundred and eight QTLs significantly associated with the seven erythrocyte traits in different stages were identified. The Manhattan plots for these association results and detailed information across the three different stages are presented in Figure FGF14 gene located on SSC11 was simultaneously associated with GRAN, LYM, and WBC at day 18. There was only one possible QTL located on SSC8 that affected GRAR at day 18. This region held 35 significant SNPs, where the most significant SNP was at the position of 10,485,550 bp and located in the coiled-coil and c2 domain containing 2A (CC2D2A) gene. At day 46, the majority of QTLs were identified associated with MONR. At day 240, some identical QTLs were uncovered for MON and MONR. There is no significant association for lymphocyte count (LYM) and WBC (P > 5E-08). The detailed descriptions of significant SNPs across the three different stages of QTLs are presented in Supplementary Table Seventy six loci significantly associated with the seven leukocyte traits at different stages were detected. The related Manhattan plots and detailed information across three different stages are presented in Supplementary Figures P = 4.44E-10). For PDW at day 18, a region of 1.56 Mb on SSC5 was identified including 13 significant SNPs, and the most significant SNP (P = 2.2E-08) was located in the activating transcription factor 7 interacting protein (ATF7IP) gene. Fourteen significant SNPs located on SSC11 were detected for PDW at day 46. The most significant SNP (P = 1.19E-09) was located in the integrin subunit beta like 1 (ITGBL1) gene.Unlike the plenty of loci identified for erythrocyte and leukocyte parameters, only a few loci were identified for platelet traits  was located on SSC3 at 115 Mb near to an annotated gene called the lysocardiolipin acyltransferase 1 (LCLAT1) gene. The other significant SNPs are listed in Supplementary Table For these four multitrait GWAS of correlated traits, we identified eight, one, four, and three novel significant loci, respectively, compared to single-trait GWAS Figure . The mul2 between the best-guess imputed and true variants in the cross-validation reached an average of 89 and 80%. In the case of the pig, most studies focused on the imputation from low-density genotypes to 60 K variants with correlations ranging from 0.938 to 0.992 for imputation from 3 to 60 K  in most native pigs than in western pigs , which is near the TOX2 gene. TOX2 is a transcription factor that shares a highly conserved high-mobility group DNA-binding domain with the other TOX family members. A recent study shows that TOX2 can regulate the development of natural killer cells with the control in T-BET expression  and posited the fibroblast growth factor 14 (FGF14) gene. Fibroblast growth factors (FGFs) family are involved in a variety of biological processes. Among the Fgf family, FGF14 is one member of the intracellular subfamily, also known as iFGFs  gene, implying that the same hematological trait at different stages may be affected by different genes.We almost confirmed all the QTLs identified in our prior 60K GWAS based on a single-marker, except the QTLs located on SSC5, SSC7, and SSC13 for MCH at day 18, WBC at day 240, and PDW at day 46. The undetected signal is probably because of the higher threshold in the whole-genome GWAS. In particular, as to the QTLs located on SSC7 or SSC8 for HCT, MCH, and MCV at day 240, respectively, we did not discuss these QTLs in detail here, because the conditional GWAS was performed and results have been elaborately discussed by KIT gene was proposed as a candidate gene for hematological traits by some different studies. We found that the candidate gene of KIT should be responsible for the MCV trait , and MCH and RBC traits (day 240) in our previous study (2 population at the age of 140 days (KIT associated with two erythroid traits including MCH and MCV at day 240 in White × Minzhu F2 population (P = 1.35E-07). The relatively low significance may be because of the small samples for this trait, i.e., only 250 samples that held phenotypes were used for GWAS. If we can increase number of the valid samples, the association between PDW and the genotypes may emerge. Beside the KIT gene claimed as the candidate gene for hematological traits in majority studies at different stages , some other candidate genes were identified, including neuroligin 4, x-linked (NLGN4X) and high-mobility group box family member 2 (TOX2) gene, which was identified in the Commercial Landrace population  located on SSC3 near the lysocardiolipin acyltransferase 1 (LCLAT1) gene were found to be responsible for MCH and MCV at day 18. A study reported that LCLAT played an important role in the development of hematopoietic and endothelial lineages, probably acting at the level of the hemangioblast in mice  (The genotypic data of 933 Fd.7kn7r) . The rawd.7kn7r) , SRP0472d.7kn7r) , and SRPLH and ZZ conceived and designed the experiments. TG, GY, ZZ, and SX performed the experiments. GY, ZZ, FZ, TH, and YL analyzed the data. SX, WXi, and WXu contributed to materials and analysis tools. GY, TG, ZZ, and LH wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.