We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons . Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in the UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardiometabolic disorders, autoimmune diseases and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson’s  Birth weight (BW) is a powerful predictor of infant and newborn survival, with lower weight infants being at higher risk of mortality . BW is aBW is a complex and multifactorial trait ,15. MateADCY5 and near CCNL1, were found to influence variation in BW in singletons. The number of associated variants increased to seven in 2013 with an expanded meta-analysis study of over 69 000 European individuals (NTRK2 gene (The number of genetic variants identified for BW is growing based on findings from GWA studies (GWAS). In a 2010 study by Freathy et al. , two varividuals . In a muividuals , BW and ividuals  also exaividuals . To dateRK2 gene .The Developmental Origins of Health and Disease (DOHaD) hypothesis is based on observations that adverse influences early in development, particularly in the intrauterine environment, result in permanent physiological and metabolomic changes leading to increased risk of disease in adulthood . One hypIn general, the DOHaD and the Barker hypotheses are environmentally based. That is, the existence of an adverse intrauterine environment leads to decreased BW and long-term cardiometabolic sequalae in offspring. Alternatively, strong genetic correlations between low singleton BW and indicators of metabolic and cardiovascular health, as described in the meta-analysis by Horikoshi and colleagues , correspOn average, twins have lower BW than singletons since twin pregnancy is characterized by a shorter gestational duration  and becaThis study aimed to search for common genetic variants underlying BW in twins by carrying out a meta-analysis of genetic association studies in twins and compare the results to those for BW in singletons. To this end, four approaches were employed: 1) A meta-analysis of combined GWA results from five European twin cohorts, UK Biobank, one Australian twin cohort and one twin cohort from the Midwestern region of the United States of America. 2) An assessment of the genetic correlations between BW in twins and BW in singletons. 3) The evaluation of the genetic correlations between BW in twins and a range of traits and diseases in later life, including anthropometric and neuropsychiatric characteristics. 4) An assessment of the predictive performance of BW polygenic scores in twins and singletons.P-values compared to the observed values across SNPs, can be found in P-value for lead SNPs (P < 5 × 10−8); however, two lead SNPs had an association signal of P < 5 × 10−7. These SNPs were located on chromosome 1  and chromosome 3 . rs10800682 is independent  of all genome-wide significant loci found by Horikoshi and colleagues (2 0.154), one of 60 loci previously associated with BW  recently identified by Warrington et al. . Summary statistics of the 150 overlapping variants are presented in Though no genome-wide significant SNPs were identified, we evaluated the performance of SNPs in the current study with the genome-wide significant SNPs signals . No significant heterogeneity in allelic effects was observed after Bonferroni correction (P > 0.00033). The smallest reported P-value of heterogeneity statistics in the current study was 0.002, which is in line with the smallest reported P-value of heterogeneity statistics for the genome-wide significant variants reported in Warrington et al. of 0.004  using METAL  based on a discovery GWAS of 26 836 European individuals , 'own birth weight'  derived from an expanded GWAS of 286 870 European individuals  in 143 677 European individuals   urg = 0.76, 95% CI: 0.49–1.03). Of the genetic correlations with other phenotypes, six additional anthropometric traits exhibited strong positive genetic correlations, including offspring birth weight  , own birth weight  , child birth length , extreme height , height  and hip circumference .A positive correlation was also observed with 'offspring birth weight' , as measured in 216 611 mothers  . Genetic correlations of BW in twins with autoimmune disorders, psychiatric disorders, reproductive traits and smoking behavior yielded mixed results.Glycemic traits were all negatively associated with BW, whereas cognitive characteristics, measured by intelligence, correlated positively (h2) was calculated using LD Score regression. The h2 was estimated to be 0.0407 for BW in twins. For BW in singletons, the heritability estimates from three studies were h2 = 0.1139, h2 = 0.0985 and h2 = 0.1016 for ‘child birth weight’  and singletons , as shown in The PGS, based on summary statistics from GWA analyses of BW in UK Biobank, robustly predicted BW in NTR twins and singletons. The PGS including the fraction of SNPs with a As shown in P-value selection threshold of 0.01 represented the best predictor of BW category . Together, the results of PGS prediction analyses suggest that BW PGS constructed from a large representative discovery population predict BW similarly in a target population of twins and singletons.BW category was also evaluated as the response variable , indicating a large overlap in the genetic variants influencing BW in the two groups. The genetic associations with health-related traits, when comparing the size and direction from our genetic correlation analyses with the results from Horikoshi and colleagues  and twins (P = 2.10 × 10−51). While the proportion of variation explained by the best predicting PGS was small for twins at 2% and non-twins at 3%, despite moderate heritability estimates, such PGS represents common genetic architecture underlying BW in twins and singletons even though there are clear differences in BW between the two groups. Smaller heritability estimates were also observed for BW in twins, potentially indicating a form of sibling competition. That is, if one twin grows and occupies the growing space of the co-twin, the genes that increase the BW of the larger twin may also limit the growth of the co-twin. Consistent with our results, sibling competition would result in a dampened effect of the PGS and would be reflected in lower heritability estimates in twins.We evaluated the predictive performance of PGS derived from a GWAS on BW from a large representative population from the UK Biobank in a large target sample of NTR twins and non-twins. The PGS calculated from the proportion of SNPs with a P < 5 × 10−7. rs10800682 was not near (>2 Mb away) and was independent (r2 < 0.05) of all genome-wide significant loci found by Horikoshi and colleagues  with rs11719201 (an intronic variant of ADCY5), one of 60 loci previously associated with BW . Another limitation is that we corrected for birth order, gestational age and maternal age at birth in a majority of cohorts but could not do so for all cohorts due to data availability. This information should ideally always be included when BW data are collected.The results from this study strongly suggest that BW data from twins and singletons may be meta-analyzed together in GWAMA, despite the limited sample size of the discovery GWAMA in twins . Suppose the effects of rare variants are not shared similarly to the effects of common variants for each phenotype comparison. In that case, the genetic correlation estimates could be misleading. However, in terms of their shared influences on pairs of phenotypes, there is not a theoretical reason to expect systematic differences in the effects of rare and common variants. Rare variants with larger effects would not preclude carrying far more numerous common variants with smaller effects. Thus, the genetic correlations presented in this study may provide reasonable estimates based on common genetic variation; however, to validate these findings, rare variant studies are needed. Future studies may also expand upon our genetic correlation estimates by utilizing non-European populations, greater sample sizes (for discovery and trait-specific phenotypes as they become available) and increased density across the genome.P-value selection threshold of the most predictive PGS is a function of the effect size distribution, the statistical power of the discovery GWAMA and the NTR target data, the genetic architecture of BW, as well as the fraction of associated markers. Concerning the results of the PGS prediction, we note that the Follow-up research may aim to better understand BW since it is influenced by direct fetal and indirect maternal genetic influences through the intra-uterine environment. The amount of variance in BW explained by the maternal genotype has been estimated as substantially smaller than the fetal genetic contribution . Recent P-values since beta estimates and intercepts will be affected by raw differences in BW.To conclude, we show that based on genetic correlation and PGS analyses, the genetic architecture of BW in twins and singletons is similar. Of course, it is known that mean differences in BW between twins and singletons exist; however, the findings of this work strongly suggest that the genetic causes of variation are the same. Bearing this in mind, the results of this work indicate that it is appropriate to meta-analyze twins and singletons for genetic studies of BW. However, careful consideration of analytical strategies will be needed since details specific to twins may not apply to full-term singletons. Small groups of twins might still need to be excluded; for example, the highly discordant BW pairs due to the possibility for twin-to-twin transfusion syndrome (TTTS). Also, in full-term singletons, a typical gestational age cut-off for exclusion (e.g. born before 37 weeks) is often applied, which will not be applicable with the inclusion of twins due to shorter gestational duration  and delaEight population-based twin registers supplied data: the Netherlands Twin Register (NTR) ,50, Queez-score transformed  before analysis. Each participating study group performed the association analyses between each SNP genotype and BW z-scores with the following covariates where available: sex, gestational age, year of birth, maternal age at birth, birth order and relevant study-specific metrics  correcting for genomic ancestry). For all cohorts, except ATR, birth order was available. The analysis was performed without adjustment for maternal age at birth and gestational age in the DTR. Association analyses were performed in PLINK v1.07 (Birth weight (BW) measures were NK v1.07  with theNK v1.07 . Sample Summary statistics from each cohort GWA analysis underwent another round of standard quality control before meta-analysis. The R-package EasyQC  was usedv1.3.6)  of any of the 60 previously identified loci, it was considered novel. We calculated the r2 between the signals with the web-based application LDmatrix within the LDlink (v3.8)   was usedi et al. , if a lek (v3.8)  suite ofv1.9.3) (http://ldsc.broadinstitute.org/ldhub/) (https://github.com/bulik/ldsc). LD score regression requires large sample sizes and utilizes LD information from an ancestry-matched reference panel; therefore, genetic correlation analyses were constrained to European GWA study samples. SNPs with a MAF To quantify the degree of shared genetic contribution between BW in twins and BW in singletons and to correlate BW in twins to other individual-level health-related traits and diseases, we employed LD Hub (/ldhub/) . LD Hub /ldhub/)  between /ldhub/) . HapMap3For the comparisons with previous genome-wide genetic correlation analyses in singletons , we seleh2) was calculated in LD Hub with LD score regression to evaluate how much of the variation in BW could be ascribed to common additive genetic variation.SNP heritability  served as the discovery set for calculating polygenic scores (PGS) in the NTR target dataset. For the PGS prediction of BW in the NTR, participants with complete BW data and maximum information on covariates  were included. When not available, gestational age was imputed with the mean gestational age separately for twins (mean = 37.38 weeks) and singletons (mean = 39.89 weeks). Genotyping platform and ten genomic PCs were included in the model to account for batch effects (i.e. non-random selection of samples genotyped on specific arrays) and residual population stratification. The target sample consisted of 17 379 individuals, comprising 10 487 twins and 6892 singletons. Summary statistics from the UK Biobank GWAS on BW were adjusted for the effects of LD with LDpred ) were used for allelic scoring in PLINK (GWAS results on BW from the UK Biobank (data field 20022) (h LDpred  using thh LDpred . Recalcuin PLINK .R2, was used to evaluate the predictive performance of each PGS. Our main interest was to determine how well PGS derived from a large discovery population, reflecting general population numbers of twins, could predict BW in a separate target population of twins and singletons.We used the PGS to predict BW in NTR twins and singletons using GEE methods in R , taking BW_Supp_Fig_1_ddab121Click here for additional data file.Supplemental_Tables_Beck_ddab121Click here for additional data file.Supplementary_Methods_Beck_ddab121Click here for additional data file.


Background: Hypothyroidism and primary biliary cirrhosis (PBC) are often co-existed in observational epidemiological studies. However, the causal relationship between them remains unclear.Methods: Genetic correlation, Mendelian randomization (MR) and colocalization analysis were combined to assess the potential causal association between hypothyroidism and PBC by using summary statistics from large-scale genome-wide association studies. Various sensitivity analyses had been conducted to assess the robustness and the consistency of the findings.Results: The linkage disequilibrium score regression demonstrated significant evidence of shared genetic architecture between hypothyroidism and PBC, with the genetic correlation estimated to be 0.117 (p = 0.006). The OR of hypothyroidism on PBC was 1.223  in MR analysis with inverse variance weighted (IVW) method. More importantly, the results from other 7MR methods with different model assumptions, were almost identical with that of IVW, suggesting the findings were robust and convincing. On the other hand, PBC was also causally associated with hypothyroidism , and, again, similar results can also be obtained from other MR methods. Various sensitivity analyses regarding the outlier detection and leave-one-out analysis were also performed. Besides, colocalization analysis suggested that there existed shared causal variants between hypothyroidism and PBC, further highlighting the robustness of the results.Conclusion: Our results suggest evidence for the bi-directional causal association between hypothyroidism and PBC, which may provide insights into the etiology of hypothyroidism and PBC as well as inform prevention and intervention strategies directed toward both diseases. Primary biliary cirrhosis (PBC), known as primary biliary cholangitis, is a chronic inflammatory autoimmune disease of the liver . PBC is Despite vigorous efforts in the characterization of autoantibodies and bile duct histopathology, the etiology of PBC remains unclear. One common sense is that PBC is caused by genetic as well as environmental factors . Often, Recently, the proliferation of publicly available genome-wide association studies (GWASs) provides a rich resource of large-scale summary data that did not involve privacy and ethical issue, which has promoted the researchers to examine previously known and novel relationships among complex traits . GeneticAt present study, we performed the joint analysis of genetic correlation, MR and colocalization to deeply demonstrate the relationship between hypothyroidism and PBC using large-scale GWAS summary statistics . Varioushttps://atlas.ctglab.nl/traitDB/3602), with18,740 cases and 270,568 controls from the UK Biobank (data filed 20002_1226) with 10,321,705 SNPs (We downloaded summary statistics of hypothyroidism from the GWAS ATLAS (705 SNPs . Hypothy705 SNPs . All PBCh2) of hypothyroidism and PBC, and the overall genetic correlation (rg) between them (B. rg. Detailed formula of cross-trait LDSC was provided in the Supplementary Methods.We applied cross-trait linkage disequilibrium score regression (LDSC) to estimate the SNP heritability  the p < 5 × 10−8); 2) matched these significant SNPs with GWAS summary statistics of outcome  by chromosome, position and rsid, and removed those unmatched ones; 3) screened out independent SNPs using PLINK (version 1.90)  excluded the potentially pleiotropic SNPs that are associated with outcome at the p value less than 0.05 after Bonferroni correction; 5) harmonized the alleles and effects between the exposure and outcome datasets. Through the rigorous screening process, there were 69 SNPs selected as instrumental variables for hypothyroidism, with detailed information shown in To choose the valid instrumental variables for MR analysis, we followed the previous MR study  with a son 1.90) , based oThe accuracy of MR results inevitably depends on whether the selected genetic variants meet the 3 MR assumptions. The violation of the relevance assumption would lead to weak instrumental bias, we thus calculated the proportion of variance explained (PVE) by an individual SNP  and thenWe further conducted the leave-one-out (LOO) analysis by removing each genetic variant out of the MR analysis in turn, to assess the influence of individual SNP on the overall causal estimate. In addition, various diagnostic plots were depicted to illustrate the MR results including scatter plot, individual SNP effect forest plot, funnel plot and LOO forest plot.0: no association with either trait; 2) H1: association with trait 1, not with trait 2; 3) H2: association with trait 2, not with trait 1; 4) H3: association with trait 1 and trait 2, two distinct SNPs; 5) H4: association with trait 1 and trait 2, one shared SNP. The PP of each configuration is respectively denoted by PP0, PP1, PP2, PP3, and PP4. A large PP4  was considered to be strong support for colocalization in the original method publication , implying a shared genetic architecture, between hypothyroidism and PBC. The intercept of genetic covariance was estimated to be 0.0135 , indicating that there was little or no sample overlap given that hypothyroidism and PBC were usually phenotypic correlated.Using LDSC, the SNP heritability on the liability scale was estimated to be 0.2085 (se = 0.0257) for hypothyroidism and 0.1527 (se = 0.0253) for PBC, respectively. We found that there existed a significant positive genetic correlation  using the random-effect IVW method, indicating that people with hypothyroidism can lead to an average of 22.3% increase in the risk of PBC and suggesting hypothyroidism may play a dangerous role in the development of PBC. The causal effect estimate of hypothyroidism on PBC from other multiple MR methods were almost identical as that from IVW . In particular, the ORs of hypothyroidism on PBC were estimated to be 1.305  in weighted median method, 1.367  in weighted mode-based method, 1.249  in MR-Robust, 1.285  in MR-Lasso, 1.223  in MRPRESSO, 1.150  in MR-Egger with its intercept indicating no directional pleiotropy (p = 0.687), and 1.251  in MR-RAPS.Using all these selected instrumental variables, we found that hypothyroidism was positively associated with PBC. The MR estimates were reported as odds ratios (ORs) which can be interpreted as the increase of PBC risk per unit increase in log odds of hypothyroidism. The OR of hypothyroidism on PBC was estimated to be 1.223 , and MRPRESSO distortion test further suggested that there was no significant difference in the causal estimates before and after correction for outliers (p = 0.768). The funnel plot indicated there was no obvious outlier, strengthening the robustness of our results (One SNP (rs2111485) was identified as a potential pleiotropic outlier at the nominal significance level of 0.05 in the MRPRESSO outlier test. However, the causal effect estimate did not change substantially after outlier correction , indicating that people with PBC can lead to an average of 4.9% increase in the risk of hypothyroidism. The causal effect estimate of PBC on hypothyroidism from other MR methods were almost identical as that from IVW (p = 4.34 × 10−5) in weighted median method, 1.087  in weighted mode-based method, 1.051  in MR-Robust, 1.049  in MR-Lasso, 1.049  in MRPRESSO, 1.195  in MR-Egger with its intercept indicating the possibility of directional pleiotropy (p = 0.047), and 1.056  in MR-RAPS.In terms of random-effect IVW, the OR of PBC on hypothyroidism was estimated to be 1.049 (95% CI, 1.010–1.089; from IVW . SpecifiFurthermore, the MRPRESSO outlier test identified no pleiotropic SNPs at the nominal significance level of 0.05. Both the funnel plot and the LOO method also indicated the non-existence of outlier instrumental SNPs, strengthening the robustness of our results .4 greater than 0.75, one on chromosome two and the other on chromosome 17 (4 were treated as the most likely shared causal variants, with their regional association plots displayed in Among 79 unique regions, we identified two genomic regions with PPosome 17 , indicatAt present study, we jointly conducted genetic correlation, MR and colocalization analysis with large-scale GWAS summary statistics, to comprehensively investigate the causal relationship between hypothyroidism and PBC. The genetic correlation analysis showed a significant overall correlation between hypothyroidism and PBC. Generally, the observed overall association between hypothyroidism and PBC can be explained in three different ways: causality, horizontal pleiotropy and confounding by LD in alternative genetic variants . Our MR Indeed, hypothyroidism and PBC are usually co-existed . The bi-cis-eQTL that affects IFIH1 mRNA expression (We also identified two shared causal genetic variants through colocalization analysis, including rs2111485 on chromosome two and rs35776863 on chromosome 17. The SNP rs2111485, located in the intergenic region, behaves as a pression . As a cypression . In addipression . These fpression . ACAP1 ipression  and thyrpression .To our best knowledge, this is the first comprehensive study to illuminate the bi-directional causal association between hypothyroidism and PBC by using genetic approaches based on large-scale GWAS summary statistics, where the large GWAS sample sizes provided us sufficient statistical power to detect causal association . MoreoveAppropriate analysis of large-scale genetic dataset can provide some clues for biomedical research. Through the joint analysis, our study rendered strong evidence for the bi-directional causal association between hypothyroidism and PBC, which may provide insights into the etiology of hypothyroidism and PBC as well as inform prevention and intervention strategies directed toward both diseases. However, further experimental study is needed to validate the findings.


ITPK1, PLK4, HSPA4L, REEP1, CHMP2, MRPL35, and SCUBE) with HSPA4L and/or REEP1 genes being highly relevant with a likely effect on host response in managing disease-associated symptoms. The results on the accuracy of predicting breeding values presented 20–43% advantage in accuracy using genomic over pedigree-based information which varied across model types and applied validation schemes.Viral nervous necrosis (VNN) is an infectious disease caused by the red-spotted grouper nervous necrosis virus (RGNNV) in European sea bass and is considered a serious concern for the aquaculture industry with fry and juveniles being highly susceptible. To understand the genetic basis for resistance against VNN, a survival phenotype through the challenge test against the RGNNV was recorded in populations from multiple year classes (YC2016 and YC2017). A total of 4,851 individuals from 181 families were tested, and a subset  belonging to 122 families was genotyped using a ∼57K Affymetrix Axiom array. The survival against the RGNNV showed low to moderate heritability with observed scale estimates of 0.18 and 0.25 obtained using pedigree vs. genomic information, respectively. The genome-wide association analysis showed a strong signal of quantitative trait loci (QTL) at LG12 which explained ∼33% of the genetic variance. The QTL region contained multiple genes ( The aquaculture industry is expected to fulfill an increased demand of fish for human consumption as fisheries realize their resources reach their limits. The need to increase production and fulfill current and future demands comes together with challenges in long-term sustainability and production value, such as diseases outbreaks, feed supplies, water pollution, and global warming.Betanodavirus of the family Nodaviridae (Dicentrarchus labrax), the most cultured species in the Mediterranean whose annual production reached 168,642.57 tonnes in 2018 valued on $1,03 billion USD, FAO. Recently, a reassortant betanodavirus (RGNNV/SJNNV) has also been reported in gilthead sea bream (Sparus aurata) with a postulated asymptomatic contagious host for transmission of disease to European sea bass —also known as viral encephalopathy and retinopathy (VER)—is one of the main infectious diseases affecting marine aquaculture. The disease is caused by the red-spotted grouper nervous necrosis virus (RGNNV) which belongs to the genus aviridae . VNN hassea bass .Viral nervous necrosis can be transmitted horizontally and vertically . The acuSelective breeding is an effective tool to continuously improve traits such as disease resistance , usuallyMajor factors influencing the success of a breeding program are high quality, robust and reproducible phenotypic data, and the maintenance of a reliable pedigree; furthermore, when introducing advanced genomic methods, this extends to the use of genome-wide distributed, reliable, reproducible molecular genetic markers . The GS provides the opportunity to rank individuals within and across families with higher accuracy . The avaIn this study, we investigated the genetic variation for resistance to the RGNNV from a challenge test in a commercial European sea bass breeding program performed during two consecutive years. In addition, fish from the same challenge tests were genotyped to detect QTL for survival against the RGNNV and assess the potential of the GS to improve resistance against VNN.The presented results are based on challenge testing of two different year classes in the Nireus SA’s breeding nucleus of European sea bass . The comParents to both year classes included in this study were photoperiod-manipulated to mature out of the natural spawning season. YC2016, consisting of 89 full-sibs families produced from 65 sires and 30 dams, generated between September 3 and October 10, 2016; YC2017 consisted of 92 full-sib families derived from 85 sires and 35 dams produced by artificial stripping in the period September 17 to October 13, 2017. In the mating design, each female parent was mated with one to four males with an average of 2.8 for YC2016 and 2.6 for YC2017. Each male parent was mated with one to four females  in YC2016, whereas in YC2017 each male parent was mated to one to two with an average of 1.1 females per male. The average age of the male broodstock was 3.9 years post-hatching for YC2016 and 3.6 years for YC2017, while for the female broodstock, the average age was 3.5 and 4.0 years for YC2016 and YC2017, respectively. The use of broodstock of different years of age and the structure with overlapping generations builds a strong genetic tie between year classes in the breeding nucleus which allows for combined analysis of data from both year classes in this experimental population.Families were reared in separate circular tanks of 315-L capacity at the Enalios-Breeding Programme hatchery of Nireus, S.A., located in Central Evia. Approximately 30 individuals per family were individually PIT-tagged using 8-mm Biomark tags. The PIT tagging for YC2016 was performed at an average body weight of 10.9 g (sd = 2.5 g) between March 21 and April 12, 2017, whereas the PIT tagging for YC2017 was performed from March 20 to March 23, 2018, at an average weight of 8.9 g (sd = 1.7 g). All PIT-tagged individuals within each year class for the specific challenge test were stocked in a single tank after transportation to the challenge test facilities at the laboratory of Ichthyology, Aquaculture and Diseases of Aquatic Organisms (ICHTHYAI), Department of Marine Sciences, at the University of Aegean in Lesvos, Greece. The date of transportation of individuals to the challenge test facility was April 24 in 2017 (YC2016) and June 13 and 15 in 2018 (YC2017).6 TCID50/ml, defined from in-house pre-trials. The fish were injected on May 13, 2017 and distributed randomly to three experimental tanks of 2 m3, and mortalities were recorded every 4 h for a period of 28 days after infection (with no exceptions including recordings during nights and weekends), until mortality had stopped. The second challenge test was conducted on YC2017  with similar conditions and environment as on YC2016, but the infection dose was 5 × 106 TCID50/ml. YC2017 had an initial average body weight of 25.3 g in the test. All fish were fin-clipped before the challenge test, and tissues were preserved in 95% ethanol and stored at −4°C.Both year classes (YC2016 and YC2017) were challenged according to similar protocols using a European sea bass RGNNV isolate from a commercial fish farm outbreak in 2012, and the virus challenge was performed with intramuscular (IM) injection. In YC2016, sea bass individuals with an average weight of 14.4 g were injected with a challenge dose of 10n = 4,851) were subsided from both year classes (YC2016 and YC2017). Hence, out of total 4,851 individuals, a subsample of 1,535 individuals were selected for genotyping comprising 767 individuals belonging to 30 families of YC2016 and 768 individuals from 92 families of YC2017. Since the accuracy of selection using genomic information would be highly dependent on the relationship of training  and tested  population, pedigree relationships were considered to select individuals from families with genetic links to contemporary families. Tissue samples from selected fish were used for the DNA extraction and genotyping using the SNPs-based ∼57K Affymetrix Axiom array  were excluded from the data. Filtering was also performed at the individual level with individuals removed based on the missing genotype rate (>10%) and poor heterozygosity (higher than ±3sd of population). The filtering step retained a total of 1,489 individuals  from 120 full-sib families of which 28 families represented YC2016 (15–30 sibs per family), and 92 families from YC2017 (4–14 sibs per family) genotyped with approximately 52K SNPs.The raw genotype data were quality-filtered by PLINK software . SNP marData description and statistics: The statistics for the recorded traits and the initial evaluation of models were obtained using the generalized linear model in statistical software “R”. With the full dataset  year classes and the family effects were tested; year classes did not show significant effects (p = 0.106), while the family effect was highly significant with p < 0.001. Though the effect of year classes was not significant, it was still used in the model to avoid year class-specific deviations in estimates. The analysis and/or results presented below are mainly focused on using the combined dataset from both year classes (YC2016 and YC2017) unless otherwise specified.Quantitative genetic parameter estimation: Estimation of genetic parameters was conducted for the binary survival trait recorded at the end of test(s) . The analysis was conducted including either all data  or the genotyped subset of the data  summarized in .VanRaden ; c is ,,,From where Genome-wide association analysis (GWAS): Genome-wide association analysis was performed using the linear mixed animal model implemented in the GCTA program with the “--mlma-loco” function  variance: The proportion of the genetic variance explained by the top significant SNP(s) were computed using two methods. One is the direct method where genetic variances of SNPs were computed using allele frequencies and allele substitution effects as piqiαi2 . The othBreeding value estimation: Breeding values for the survival  against VNN were computed through model-1 without using random effects common to full-sibs  genotyped individuals  and 2) a full dataset . The comparison for the accuracy of predictions among the genomic and pedigree models was performed using genotyped individuals only, while PBLUP and HBLUP models were compared using the full dataset .1) Within family masking: Under this scheme, 30% of individuals within each family were randomly masked, which produced 447 individuals as validation animals, and the remaining 1,042 individuals with available phenotypes were kept as the training set.2) Random masking: Under this scheme, 30% of the individuals were randomly masked without giving any consideration on families. Hence, 1,042 individuals were used for training and 447 as validation animals.To compare prediction accuracies, validation schemes were designed in two ways.= 0.18), respectively.The adopted schemes for the comparison of PBLUP vs. HBLUP were the same as 1) and 2), but the number for validation and training individuals ended up large with 1,455 validation and 3,396 training animals. The breeding values for the masked individuals were computed using pedigree, genomic, and the hybrid relationship matrices The mean accuracy of 20 replicates was computed as the correlation  and the subset  of the data was very similar, with 42 and 45%, respectively  cover a large region of 15 Mb  with the top significant SNP AX-172280857 centered at 11,359,282 bp  revealed a strong signal of QTL at LG12 with 72 SNPs crossing the genome-wide threshold . Moreove9,282 bp . However9,282 bp . A quanttructure . Howevertructure .SNP variance: The proportion of genetic variance explained by the highest significant SNP using the direct method was ∼26.0%  and validation scheme “b” (random masking), respectively. A similar trend was also seen using the PBLUP model with higher mean accuracy obtained with validation scheme “b”  showed approximately similar accuracy of prediction with fractional differences with values 0.69 ± 0.09 and 0.71 ± 0.09 for validation schemes “a” and “b”, respectively . The BayesB model displayed the highest accuracy with estimates of 0.77 ± 0.09 and 0.75 ± 0.09 for validation schemes ectively .“a” and “b”, respectively  and polo-like kinase 4 (PLK4). The ITPK1 gene is known to be engaged with cell physiological functions involving signaling molecules inositol phosphates (PLK4 (polo-like kinase 4) has a role in cell replication by centriole duplication  gene which belongs to a family of heat shock proteins (HSPs), HSP70 and plays a role in response to harmful circumstances and protect the cell from stress  which is found in nerve cells (neurons), the brain, and the spinal cord (CHMP2 (charged multivesicular body protein 2) and MRPL35 , which are known to be involved in the physiological function in cells while SCUBE1  domain-containing protein 1) seems to be localized in the endothelium with developmental functions.Genes underlying the QTL region i.e., the 50 Kb region covering ±25 Kb from the highest significant SNP (AX-172280857) position was searched using the European sea bass genome  of the total genetic variance along with the obtained highest accuracy of prediction using BayesB is very convincing and complimenting results which present a strong potential for the application of efficient and economical marker-assisted selection. Increasing the resolution of a QTL region using more variants  along with a high-quality phenotype might assist to uncover the missing genetic variance and detect the actual causative mutation affecting the trait.REEP1 gene located immediately at the upstream of the highest significant SNP which seems to be more pronounced with functions involving the nervous system. The detected QTL explained ∼33% of genetic variance suggesting a strong potential of marker(s)-based efficient and economical selection for improving survival against VNN. The comparative results on the accuracy of predicting breeding values with genomic information were substantially higher (20–44%) than predictions using pedigree information which suggests a strong advantage of using genomics over pedigree-based selection to genetically improve host resistance against the RGNNV.The survival against the VNN disease showed low to moderate levels of genetic variation with a possibility of improvement in traits through selective breeding. The genetic architecture for host resistance to the RGNNV appears to be affected by a locus with a large effect and perhaps smaller contribution from the other loci. Multiple genes were identified within the QTL region with the


The identified MTAs were four for lamina wax, three for petiole wax, three for stem thickness, two for the flowering duration, one for the normalized difference vegetation index (NDVI), and two for the normalized pigment and chlorophyll index (NPCI). Sixteen candidate genes were identified within a 15 kb distance from either side of the markers. The detected MTAs and candidate genes have prospective use towards selecting stress-hardy pea cultivars in marker-assisted selection.Heat and drought, individually or in combination, limit pea productivity. Fortunately, substantial genetic diversity exists in pea germplasm for traits related to abiotic stress resistance. Understanding the genetic basis of resistance could accelerate the development of stress-adaptive cultivars. We conducted a genome-wide association study (GWAS) in pea on six stress-adaptive traits with the aim to detect the genetic regions controlling these traits. One hundred and thirty-five genetically diverse pea accessions were phenotyped in field studies across three or five environments under stress and control conditions. To determine marker trait associations (MTAs), a total of 16,877 valuable single nucleotide polymorphisms (SNPs) were used in association analysis. Association mapping detected 15 MTAs that were significantly ( Pisum sativum L., 2n = 14) is among the world’s most cultivated pulse crops, and its economic value is mainly derived from its nutritious seed that is high in protein, slow-digestible starch, essential minerals, dietary fiber, while being low in fat [Pea  was used. Fertilization and weed management were achieved by best agronomic practices recommended for pea production in Saskatchewan, as presented by Tafesse et al. [https://climate.weather.gc.ca (accessed on 12 January 2020)) for the nearest stations for any missing weather information from our stations, as described by Tafesse et al. [For phenotypic evaluation, the accessions were grown as follows: Two years (2016–2017) at Rosthern  and three years (2015–2017) at Saskatoon , Saskatchewan, Canada. The year–location combination produced five environments  for phenotypic evaluation. The trials at each environment were laid in a randomized complete block design with two replications. The plot size was 1.37 m width × 3.66 m length in three rows seeding, and seeding density , and total monthly precipitation, the five environments were grouped into stress and control conditions. Accordingly, Saskatoon 2015 and 2017 had greater daily maximum air temperatures > 28 °C, more warm days, limited rainfall, and drier conditions during reproduction, and were therefore classified as stress environments. The remaining three environments  were mostly ambient and classified as control environments . 2), the leaves were sorted into lamina and petiole parts, and each part was first scanned using winRHIZO . Wax was extracted and quantified according to methods used on pea [2Cr2O7 (20 g K2Cr2O7 per L of H2SO4) was added to each tube that contains the wax and boiled in a water bath at 100 °C, for 30 minutes. After cooling, 5 mL distilled water was added to each tube, the mixture was vortexed, and spectral absorbance was recorded at 590 nm using an Agilent 8453 diode array spectrophotometer with 1.6 ± 0.5 nm resolution, equipped with Chem Station software for UV-visible spectroscopy . Wax concentrations were determined using a prediction equation that was developed from a linear (R2 > 0.98) relationship of a series of known beeswax concentrations  using the same reagents. For wax determination, one representative fully expanded leaf from either the second or third node on the main stem, counting down from the top of the plant, was cut from each plot on measurement days, put in a plastic bag, and transferred to the laboratory. These leaf samples were taken twice, at early flowering, and the full pod stage about 20 days later. To determine the projected surface area . Flowering duration, the number of days elapsed from when 50% of the plants in a plot had started flowering to when 50% of the plants had terminated flowering on the main stem, was determined by taking flowering notes twice a week during the reproductive growth stage. Spectral reflectance measurements on stipules were taken once per week four times per plot during the reproductive stage for each of the five environments using a portable spectroradiometer . This instrument is capable of measuring hyperspectral reflectance in a light spectrum with a range of 320–1126 nm, at a 1.6 nm reading interval. From the spectral measurements, the normalized difference vegetation index (NDVI) and normalized pigment and vegetation index (NPCI) were calculated as follows: ths used .b is the number of replications in each experiment [Prior to performing analysis of variance (ANOVA), the normal distribution of residuals and homogeneity of variances were tested using Shapiro–Wilk and Levene tests, respectively. Then, the variance components of the genotype, environment, the G × E interaction, replication, and the residual were determined using the generalized linear model (GLM) of SAS  and considering all factors as random effects. Broad-sense heritability (H2) was computed as:periment . We thenhttps://www.ebi.ac.uk/ena/browser/view/PRJEB35147 (accessed on 15 April 2021)). The designation of a marker was made according to the pea chromosome number, linkage group number, and base pair position of the SNP. SNPs aligned to non-chromosomal scaffolds were labeled based on their respective scaffold number and base pair position.The genotyping data of the GWAS panel, previously reported by Gali et al. , were uswww.r-project.org (accessed on 17 October 2021)). We conducted an association analysis for each trait using different models, including mixed linear model (MLM), multiple mixed linear model (MLMM), SUPER, and FarmCPU of the GAPIT program. For the association analysis, Q values were determined from structure analysis and K (kinship coefficient matrix) values were calculated by GAPIT and identity-by-state (IBS) methods. Based on the Q-Q plots, we selected the MLMM model to report the marker–trait associations. Marker-trait associations were declared based on both the P-value (p ≤ 0.001) and repeated occurrence of the association in multiple environments. Manhattan plots were produced to display the P-values distribution for the SNP markers. We used a p ≤ 0.001 significance level to assert the marker–trait association. Significant SNPs associated with the six traits were identified and the presence of the significant markers for each environment was observed in the quantile–quantile (Q-Q) plots.To test the association between SNP markers and the six traits used in the current study, GAPIT (Genome Association and Prediction Integrated Tool)—R package  softwareBased on the physical position of selected SNP markers, the candidate genes present within a 15 kb distance on either side of the SNP were retrieved from the genome sequence of pea . The 15 p < 0.001) effects on all traits  had a 71% higher lamina wax concentration than the mean of control environments. Similarly, the petiole wax concentration ranged from 18.2 to 140.1 µg cm−2 across the accessions in three environments, and on average, the stress environment had a 33% higher petiole wax concentration than the mean of the control environments  > 5.5; p ≤ 3.7 × 10−6  > 5; p≤ 3.20 × 10−6) associated with the average stem thickness across five environments  significantly (−Log10 (ronments C. The SNp) > 3.5; p ≤ 5.9 × 10−4) associated with the flowering duration across the mean of five environments. The detected SNPs were also significant in at least in three of the five environments.Two SNPs (Chr3LG5_18677470 and Chr5LG3_255645703) were significantly (−Log10 (p) > 4; p ≤ 1.3 × 10−4) (p) > 4.5; p ≤ 4.9 × 10−4). For all traits phenotyped at five environments, the detected SNPs also had a significant marker–trait association in at least three of the five environments. For all traits, Manhattan plots depicting the distribution of SNP markers along with the p-value of each MTAs and the corresponding Q-Q plots are presented in One SNP (Chr6LG2_21764881) on chromosome 6 (LG2) was associated with NDVI at a significance level of (−Log10 ( × 10−4) E. FinallThe 15 significant SNPs detected from the association analysis were used to identify candidate genes for the six traits. Sixteen unique genes were identified within a 15 kb region on either side of the significant SNP markers and are considered potential causative candidate genes . The biohttps://agriculture.canada.ca (accessed on 14 October 2021)). This climate scenario underlines the need to select cultivars that are able to grow and yield well under stress conditions. The selection of stress-resistant cultivars should primarily rely on the use of stress-adaptive traits or related proxies such as vegetation indices. Pea has substantial genetic diversity, and strategic use of the available variation through using allelic variation is crucial to enhance stress resistance. With the presence of affordable and cutting-edge SNP genotyping technology and vast genomic resources, GWAS has been a dependable method for detecting genetic regions associated with traits of interest in several crops [Environmental stresses, mainly heat and drought, cause substantial yield loss in pea ,4. Due tal crops ,40,41,42GWAS previously conducted on pea have identified several markers and candidate genes associated with agronomic and seed quality traits, disease resistance, root architecture, essential minerals ,33,34,43All traits accounted in this study were significantly affected by both genotype and environment effects. We observed moderate to high broad-sense heritability values for the traits, which is in agreement with previous reports on pea in related traits ,41,44. OA total of four SNPs  for lamina wax, and three SNPs  for petiole wax were detected on different chromosomes and the non-chromosomal scaffold. We believe that this is the first report evaluating waxes in pea by GWAS. Greater lamina and petiole wax concentrations were associated with the stress environment, which is in agreement with several studies conducted on different crops species ,16,45,46Epicuticular wax is a quantitative trait controlled by several genes involved in the biosynthesis and transport of wax to the outer membrane in plants, and the expression of these genes is highly dependent on the environment. Xue et al.  reportedThree SNPs, two of them  on chromosome 7 (LG7) and one (Uscaffold03985_59708) on the non-chromosomal scaffold, were detected with a significant association with stem thickness. In a study of a pea bi-parental mapping population, Gawłowska et al.  reportedBoth heat and drought stress significantly shorten the flowering duration, which means less time for flower development and pod formation, and thus lower grain yield. Flowering duration is a quantitative trait under complex genetic and environmental control . Here weOne locus Chr6LG2_21764881) was associated with NDVI. NDVI has long been recognized as a reliable proxy for effectively estimating crop biomass, canopy greenness, and grain yield ,24,58. N764881 wap ≤ 0.0005) associated with the six stress-adaptive traits. These results are believed to advance the knowledge of the genetic bases governing these traits. The detected SNPs should be useful for marker-assisted selection for breeders in developing stress-resistant pea cultivars.In conclusion, in the present GWAS, we observed significant phenotypic variation in six stress-adaptive traits , and normalized pigment and chlorophyll index (NPCI)) among 135 pea accessions in multi-environment tests. Further, we identified 15 SNPs significantly (


We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population ( Glycine max (L.) Merr.] is one of the most economically important crops worldwide, being the primary source of plant-based protein, and the second largest source of vegetable oil  unrepresentative samples; (2) destructive sampling, which limits the number of samples that can be collected from a plot, and prevents longitudinal tracking of the same target area; and (3) extensive manual handling, which may lead to sample loss, and can be restrictive in large experiments . High-thHigh-throughput phenotyping (HTP) allows time-series measurements that monitor the development of a crop through its life stages, and how it responds to the environment . These mDifferent approaches can be utilized for genomic evaluation of longitudinal traits . A simplIn this context, this study aimed to: (1) develop an HTTP methodology to estimate soybean AGB throughout the growing season; (2) reveal the genetic architecture and estimate time-dependent effects of single-nucleotide polymorphisms (SNPs) associated with this longitudinal trait using RRM; and (3) investigate the feasibility of implementing genomic selection for longitudinal traits in soybean using RRM.We used a set of 383 recombinant inbred lines (RILs) representing 32 families from the Nested Association Mapping (SoyNAM) population  to grow in a trail of eight-row plots (0.76 m × 3.35 m). This trail was defined as the biomass sampling panel and it was used as sampling plots for destructive AGB measurements throughout the growing season.The RILs were grown under a randomized complete block design with two replications at the Purdue University Agronomy Center for Research and Education (ACRE), West Lafayette, IN, United States (40°28'20.5”N 86°59'32.3”W) and Romney, IN, United States (40°14'59.1'N 86°52'49.4'W). Planting occurred on May 31, 2017 and May 22, 2018 at ACRE, and May 17, 2018 at Romney. Soil fertility information and environmental conditions summarized by days after planting (DAP) for this experiment are described in 2. In the biomass sampling panel, AGB was collected approximately every 10 days during the growing season between 27 to 83 DAP, from a linear section of 0.56 m in a row with borders. In 2017, we randomly picked plots to measure AGB in replication one for every sampling date, while in 2018, three full AGB sampling  were performed for both locations in the two full replications. The fresh AGB was dried at 80°C using a dry-air system until achieving constant weight. Finally, we obtained the dry AGB weight and rescaled it to g/mThe SoyNAM founder parents were genotyped by RGB and multispectral imagery were collected with fixed-wing SenseFly eBee unmanned aerial vehicle (UAV). RGB imagery was collected using a S.O.D.A. camera . Multispectral imagery was collected with a 1.2 MP Parrot Sequoia camera , which captures four discrete spectral bands: green , red , red-edge , and near-infrared . Flights were performed close to solar noon at an altitude of approximately 120 m with both RGB and multispectral cameras. The forward and side overlap for flights were set to at least 85 and 70%, respectively. Ground control points were installed at the corners of the trials and their GPS coordinates were recorded using the TOPCON RTK .® 4  was used to measure the true reflectance of the panels while the multispectral images were collected. We used the reflectance values from the panels, along with radiance values of the panels, extracted from the generated ortho-mosaics, to correct the radiance values for the plots using the empirical line method  and Vegetation Indices Derivation version 1 VID 1.0; . The mule method , which ie method  previouse method .®  and the multilayer mosaic approach as described by From the RGB imagery, we calculated canopy coverage (CC) using the software ProgenyTo predict the AGB for all DAP, including days when ground truth data were not available, we considered a linear model using the imagery features as the predictor variables within each environment across all observed DAP. We observed that the distribution of the residuals was highly asymmetric, suggesting that a linear model was not suitable to fit the data . To corrRegularization methods, such as LASSO, can reduce model complexity using a “penalty” parameter that minimizes the sum of squared error. As such, LASSO performs both regularization and variable selection, by shrinking variable coefficients to zero, and eliminating variables from the model when their coefficients reach zero. The PLSR is an extension of the multiple linear regression and principal component analysis that can also effectively handle the issue of multicollinearity among predictor variables . EssentiR2), which is equal to the fraction of AGB variance explained by the model, and by the RMSE, which measures the average error magnitude. Pearson’s correlation coefficient (r) was also considered to quantify the linear correlation between the observations and their estimates, being an indication of model prediction ability. Both models were implemented in the R software  and validation set (10% of the plots). The predictive accuracy of the model was measured by the coefficient of determination  for the thi line; j ; and ijke is the random residual effect. The number of regression coefficients m varies according to the functions used for random regressions. For the Legendre orthogonal polynomials, thm Legendre orthogonal polynomial coefficient for DAP j  from RIL i. In the case of B-splines, thm interval given the previously mentioned knots associated with DAP from RIL i. According to p=0 has values of unity for all points in a given interval (t) and zero otherwise. For the thm interval given by knots p=1 or 2, respectively. The joined knots allow the function to become continuous.The general RRM can be described as:G0 is the (co)variance matrix of the genomic random regression coefficients, G is a genomic relationship matrix, I is an identity matrix, R represents a matrix containing residual variances, and G matrix was calculated using the method presented by The models’ assumptions are as:The AIREMLF90 and BLUPF90 software from the BLUPF90 family  were useΣ) for all DAP within the interval of AGB collection was obtained as (T is a matrix of covariates associated with the function assumed for RIL i and G is the genetic (co)variance matrix for the coefficients. The narrow-sense heritability, defined as the proportion of phenotypic variance due to additive genetic variation, for each DAP (j and T is a matrix of covariates associated with the assumed function.The genetic (co)variance matrix  were estimated using linear regression of the GEBV estimated based on the full dataset on the GEBV estimated based on the reduced dataset from each CV fold (1b) was calculated as the average of CV folds for each DAP.The performance of the genomic prediction of breeding values for AGB was investigated using a 5-fold cross-validation (CV) scheme. Briefly, all RILs were randomly separated into five equal-sized groups, where one group was retained as validation, and four groups were used as training. This procedure was repeated five times, with a unique group used exactly once as the validation set. Variance components and SNP marker effects were estimated based on the training set and used to predict GEBV in the validation set (reduced data). The prediction accuracy was measured using the Pearson’s correlation coefficient (r) estimated between the GEBV predicted using the full data  and the reduced data, only for the validation RIL. To evaluate the genomic prediction bias, regression coefficients  for the D is a diagonal matrix of weights accounting for variances of SNPs markers (assumed as an identity matrix in this study), Z is a matrix relating genotypes of each locus, and T is a matrix of covariates associated with the assumed function.For the GWAS, SNP effects were derived from GEBVs for each additive random regression coefficient using the POSTGSF90 software . The prelated as :u^m=DZ′Zained as :SNP^s=TuThe SNPs were selected to be further investigated based on the magnitude of their effects, as suggested by R2 and RMSE values for each CV fold, in each environment. In general, similar performance was observed for both methods in all environments. It was found that LASSO and PLSR had the same R2 averages for 2017_ACRE (0.94), 2018_ACRE (0.92), and 2018_Romney (0.94). However, the PLSR presented a smaller RMSE average for 2017_ACRE , and LASSO presented a smaller RMSE average for 2018_ACRE . Both models presented the same RMSE average for 2018_ACRE (0.24).We used two methods to quantify the ability of image-based features to statistically predict the AGB in soybean: LASSO regression and PLSR. Both methods were evaluated using a 10-fold CV strategy and we obtained high prediction performance for AGB estimation with both methods. r ≥ 0.91) in all environments for both methods, implying that the methods captured the relationship among image-based features and AGB (The correlation between AGB predicted from UAV-based imagery and observed from ground samples was high ( and AGB . Based o and AGB . The pheh2) across the 57 days  period, and the magnitude of its effects increases with time. Also, on chromosome two, Glyma.02 g064500 possibly corresponds to rhomboid protein-related that in A. thaliana is a putative cellular component in the Golgi apparatus with unknown function. Glyma.07 g067900, which codes a disease resistance protein, was upregulated when studying the regulation of genes in mutant dwarf soybeans related to plant growth. It is known that the over-expression of disease resistance and other immune-responsive genes tend to divert resources to generate protection metabolites, thus reducing overall growth  observed when predicting AGB in rice using RRM. Prediction bias for the GEBVs also varied over DAP, suggesting that selection based on different days produces different results (n = 383). Increasing population and training set size generally increase the accuracy of predictions, especially for low heritability traits (We evaluated the effectiveness of RRM-based genomic selection for longitudinal soybean AGB. Using CV, we found that it was possible to model longitudinal AGB with RRM . Predict results . This is results . One posy traits . Xavier In summary, based on the prediction accuracy and bias, our results indicate that AGB is a potential candidate for genomic selection in soybeans. The ability to predict temporal-based GEBV allows targeting specific intervals in the growing season or selecting plants with specific growth patterns. For instance, increased temperatures and water stress can reduce AGB significantly, resulting in reduction in soybean yield ; using gThe raw data supporting the conclusions of this article will be made available by the authors upon request.FM developed the experiment, collected the field data, conducted the statistical analyses, and wrote the manuscript. FM and KR conceived and designed the study. ML assisted with field data collection. KC and BA conducted the multispectral images analyses. GG assisted with AGB phenotypic prediction. HO and LB assisted with the random regression model analyses. HO, LB, and KR critically revised and improved the manuscript. All authors read 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways  The study of gene pleiotropy has become a focus of genetic research in recent years. Pleiotropy describes the phenomenon that single genes can have multiple biological effects, so that an individual exhibits multiple traits . PleiotrIn the early 20th century, reductionism had a positive impact on the development of biological understanding . ConventOmnigenic network modeling has become a powerful and fundamental tool for analyzing interactomes and quantifying relationships among genes; many statistical networking methods have been established . HoweverIn this article, we propose a new model to explore the multilayer interactome network mediating the pleiotropic control of complex traits (in this case tree height and diameter) by integrating system mapping , functioPopulus deltoides and the male clone I-45 of Populus × euramericana, which were introduced from the United States in the 1970s ) statistical model controlled by a set of specific parameters e matrix .J is the number of QTL genotypes and Considering the difference in genotype on the growth of trees, we constructed the likelihood function:represented by parameters:and covariance matrix Σ.We incorporated the simplex , expectaIn which the log likelihood ratio is calculated and compared with a genome-wide critical threshold. When the null hypothesis above is rejected, this means that significantly associated QTLs have been detected. These QTLs can be further tested to determine whether they affect the independence and interdependence of traits:p SNPs throughout the genome, we calculated the genetic standard deviation based on the parameters from maximum likelihood estimation for both traits, to describe the genetic effect of SNPs on trait development. We utilized functional clustering to identify distinct patterns of gene expression dynamics by dividing p SNPs into L tight-knit modules  mathematical equation and the SAD (1) statistical model to fit the mean-covariance structures. A hybrid EM-simplex algorithm was implemented to estimate the parameters l is calculated by:and the proportion of module An optimal number of SNP clusters in terms of their different genetic effects can be determined using penalized likelihood criteria, such as AIC and BIC.Molecular-level genetic regulatory systems can help us to understand how genes are implicated in trait growth processes through networks. A universal property of complex networks is that the change of one component  in the system is a function of other components:e system .l on height and diameter, respectively, we can derive an ordinary differential equation (ODE)-based equation system, as follows:l on height or diameter. This can be deconstructed into two components: l with the assumption that it occurs in isolation; and l that arises from the influence of the other module Network theory states that the observed value of a variable is the sum of the components of its own strategy and those derived from the strategies of its interactive counterparts . The relThe established procedure for constructing genetic network consists of the following three steps: data smoothing, variable selection, and ODE solving. We used LOP to smooth the independent and dependent functions; by interpolating additional values on the curve of best fit of genetic effects over time, the case that the number of modules may be larger than the number of time points can be solved. The network sparsity theory states that there is a limit to the number of links to maintain the stability of the network structure . It is nIn this paper, we used forest poplar annual growth data for stem height and diameter from 1 to 14 years. The growth curves of traits over time follow a sigmoid curve. Many classical growth equations provide quantitative assessment to capture biological rule, such as Gompertz , Korf L and Rich2>0.99) . Estimat2>0.99) , indicat2>0.99) . Growth We implemented system mapping to quantitatively analyze the interaction between diameter and height  based onDetailed information on these SNPs that were significantly associated with our traits of interest, including segregation types and physical positions, are given in The detection of individual QTLs by system mapping has provided the first detailed understanding of the genetic basis of complex traits, but it may provide limited insight into how common SNPs across the genome, which are below the threshold for statistical significance, act and interact to regulate growth traits . Some coThe use of high-dimensional genome-wide SNP datasets are essential to revealing how all interconnected genes generate or regulate the expression of complex traits and pleiotropic control. The regulatory networks of gene pleiotropy in living organisms can be usefully compared the vast networks of stars in interstellar clusters, which in turn form galaxies, and then superclusters of galaxies. Although it is computationally complex, dimensions can be reduced by cluster. We incorporated modularity theory  into netGenome-wide SNPs can be classified into different modules based on similarities in the pattern of genetic effects. We implemented bivariate functional clustering to classify the height and diameter effects of 156,362 SNPs into different modules. According to the comparison of BIC values for different cluster numbers, the most parsimonious number of modules was found to be 160. Each module represents a specific temporal pattern of genetic effects on the growth of height and diameter, which differs from those from other modules . In FiguWe then explored how these 160 distinct modules are interconnected. We calculated the mean genetic effect curve for each module to construct metagalactic genetic interaction networks among modules , where nWithin metagalactic networks, 105 QTLs detected by system mapping resided in only eight different modules, M7 (3 QTLs), M12 (30 QTLs), M66 (6 QTLs), M70 (1 QTLs), M119 (1 QTLs), M151 (15 QTLs), M153 (45 QTLs) and M157 (4 QTLs), implying that the interplay between QTLs located in the same module may govern stem growth. Most QTL-containing modules played as minor roles, with more incoming than outgoing links . We grouLocal interstellar networks, at the individual SNP level, illustrate specific epistatic distinctions between height and diameter growth. We demonstrated how QTLs and other SNPs that were nonsignificant in the system mapping analysis interact with each other in QTL-containing, local interstellar networks by taking SM5/M153, which is the largest QTL-containing submodule involving 24 QTL in a total of 50 SNPs , as an eWe selected four QTLs from SM5/M153 to analyze the dynamics of inherent genetic effects and those influenced by other SNPs . QTL 488General analysis focused on the pleiotropic control of complex traits by significantly associated SNPs, ignoring those nonsignificant SNPs, which may also have indispensable roles in regulation and control. It is possible that some SNPs have an independent effect, but this effect may be diminished by negative epistatic effects from other SNPs. Here, we randomly chose a module without any significantly associated QTLs, M85, which comprised a total of 565 SNPs. According to the BIC values for clustering, 50 was found to be the most parsimonious number of clusters for the construction of intergalactic genetic networks . In the SM23/M85 was found to be a minor submodule in both the height and diameter intergalactic networks, containing 20 SNPs and inhibited by SM22/M85, SM7/M85, and SM32/M85. We constructed overall and independent local interstellar networks for height and diameter . These nFour nonsignificant SNPs, 52542, 125076, 148746, and 148824 , were foResearch on genetic structures has shown that genes often express pleiotropic effects over two or more traits; this is key to understanding pathways of gene action, assessing potential off-target effects of genetic manipulation with the aim of altering a specific pathway, and comprehending the effects of new mutations on evolution, potentially inducing both favorable and unfavorable effects on fitness . GeneticIn this paper, we present a computational model integrating a novel growth equation, system mapping, functional clustering, developmental modularity theory, and evolutionary game theory. We established a CRI differential equation to describe the interaction of complex traits (stem height and diameter in forest poplar trees), which can reasonably quantify stem growth and internal structure. By deconstructing the growth of these two traits into a self-regulated part and one that is regulated by interactions between the co-existing traits in the system, our growth model can quantitatively describe the co-operative and antagonistic interactions between these traits in order to generate an in-depth understanding of the whole growth function, and reveal growth potential. We embedded the CRI differential equation into a QTL mapping model to identify important pleiotropic QTLs that play important roles in regulating the growth structure of the traits. On the other side, CRI differential equation provides a useful tool to estimate the net genetic effect of each SNP, which can resolve patterns of change over time based on the mathematical aspect of traits development.n = 66). We also found that the accuracy of effect estimation is sensitive to increasing heritability and sample size. On the other hand, in the absence of QTL expression CRI-based simulation studies show that heritability (proportion of genetic variance in the simulated phenotypic variance) and sample size affect mapping precision and power. The simulations are conducted with sample size as 66 , 100 and 200, and heritability as 0.05, 0.1, respectively. For each simulation case, the proportion of simulation times of meaningful QTL screened out from 1,000 genetic markers of repeated simulation experiments is the mapping accuracy (Power). As seen in ow 0.055 . Accordiow 0.055 . The areThe most important element of our framework is that the genetic architecture of complex traits is explored from omnigenic, genome-wide perspective . One vieOur model can be applied in general to reconstruct multilayer genetic networks, resolving the effects of genetic interactions and pleiotropy on the development of complex traits. The connection and regulation of the network may change with time or environment . There i


The aim of this study was to estimate genotype by time-lagged heat stress (HS) variance components as well as main and interaction SNP-marker effects for maternal HS during the last eight weeks of cow pregnancy, considering milk production traits recorded in the offspring generation. The HS indicator was the temperature humidity index (THI) for each week. A dummy variable with the code = 1 for the respective week for THI ≥ 60 indicated HS, otherwise, for no HS, the code = 0 was assigned. The dataset included test-day and lactation production traits from 14,188 genotyped first parity Holstein cows. After genotype quality control, 41,139 SNP markers remained for the genomic analyses. Genomic animal models without (model VC_nHS) and with in-utero HS effects (model VC_wHS) were applied to estimate variance components. Accordingly, for genome-wide associations, models GWA_nHS and GWA_wHS, respectively, were applied to estimate main and interaction SNP effects. Common genomic and residual variances for the same traits were very similar from models VC_nHS and VC_wHS. Genotype by HS interaction variances varied, depending on the week with in-utero HS. Among all traits, lactation milk yield with HS from week 5 displayed the largest proportion for interaction variances (0.07). For main effects from model GWA_wHS, 380 SNPs were suggestively associated with all production traits. For the SNP interaction effects from model GWA_wHS, we identified 31 suggestive SNPs, which were located in close distance to 62 potential candidate genes. The inferred candidate genes have various biological functions, including mechanisms of immune response, growth processes and disease resistance. Two biological processes excessively represented in the overrepresentation tests addressed lymphocyte and monocyte chemotaxis, ultimately affecting immune response. The modelling approach considering time-lagged genotype by HS interactions for production traits inferred physiological mechanisms being associated with health and immunity, enabling improvements in selection of robust animals. The tripled number of days with extreme temperatures in European countries from 1950 to 2018 . FurtherBos taurus autosomes (BTA) 8, 10, 25, and 29. Among them, the SNP BFGL-NGS-30169 on BTA29 was also identified in Jersey cattle, and the strongest annotated candidate gene for the variation of milk yield under HS was the fibroblast growth factor 4 [Response to direct HS, i.e., HS close to the trait recording date, was observed when THI exceeds a certain threshold. For example, in Holstein cows, protein yield decreased at THI 68 . Geneticfactor 4 , and resMost of the genetic studies focused on immediate responses to HS, i.e., considering THI at the measuring day, or the average THI shortly before the measuring day. Phenotypically, HS during late gestation in dams, termed as maternal or in-utero HS, significantly impaired growth, metabolism, immune functions and survival in offspring –14, sincIn this regard, the current study focusses on enhanced genomic modelling approaches to inferring time-lagged HS impact, and considers a large dataset of genotyped Holstein Friesian cows kept in large-scale contract herds. Specifically, we firstly aim on the detection of significant SNP for the direct (main) and the interaction effect in the context of time-lagged HS on milk production traits at the first official test-day after calving and on first lactation production records. The main effect represents the SNP effect for the respective trait expressed consistently, independent from time-lagged HS. The interaction component is the difference of the SNP effects between cows undergoing in-utero HS or not. Afterwards, on the basis of the identified SNP associations, we annotated potential candidate genes. Against this background, i.e., in the context of pronounced time-lagged HS, we postulate the identification of candidate genes affecting overall robustness, health and adaptation.The official records at the first test-day for the milk production traits included milk yield (TMY), fat percentage (TFP), fat yield (TFY), protein percentage (TPP), protein yield (TPY) and somatic cell score (TSCS). First lactation records were available for milk yield (LMY), fat percentage (LFP), fat yield (LFY), protein percentage (LPP) and protein yield (LPY). The first test-day records of the genotyped cows covered the period between 5 and 35 days after calving. Lactation lengths for LMY, LFP, LFY, LPP and LPY comprised 275 to 305 days in milk. Age at first calving ranged from 20 to 40 months, and calving years from 2010 to 2015. A phenotypic dataset including 14,188 first parity Holstein cows was available for subsequent GWAS. The cows are kept in 53 large-scale herds, located in the German federal states of Mecklenburg-West Pomerania and Berlin-Brandenburg. The average number of cows per herd ranged from 55 to 775. For the test-day records, cows with extreme TMY (≤ 2 kg per day or ≥ 60 kg per day) and TSCS larger than 9,999,000 cells/ml, were excluded. Descriptive statistics for the test-day and the lactation production traits are listed in Illumina BovineSNP50 v2 BeadChip , or the Illumina Bovine Eurogenomics 10K low-density chip . The 10K SNP genotypes were imputed to the 50K SNP panel by project partner vit , as implemented in the process for national routine genetic evaluations [Bos taurus autosomes, minor allele frequency larger than 0.01, minimum animal and SNP call rate of 0.95 and no significant deviation from Hardy-Weinberg equilibrium . Furthermore, we deleted cows pairs with genomic relationships [The cows were genotyped using the luations . SNP qualuations , 19. Filionships  larger tPairwise distances (in km) were calculated between weather stations and cow herds. The calculation based on coordinates for the respective longitude and latitude of each herd and weather station, and was performed using the GEOSPHERE package in R . AccordiThe daily THI was computed by averaging hourly THI over 24 hours. Afterwards, we calculated the average daily THI within the following weeks during late gestation of the respective dam, or, in other words, before the birth date of the genotyped cows: 0–7 days (WK1), 8–14 days (WK2), 15–21 days (WK3), 22–28 days (WK4), 29–35 days (WK5), 36–42 days (WK6), 43–49 days (WK7) and 50–56 days (WK8). The range of the weekly THI across the eight weeks was from 10.43 to 73.10. Descriptive statistics for the weekly THI are displayed in y = a vector of observations for the test-day or the lactation records; b = a vector of fixed effects including herd, calving year, calving month, age at first calving classes, lactation-calving-age classes for dams, classes for days in milk (for the test-day traits), lactation length classes (for lactation traits) and a dummy variable indicating the presence of HS during 1 to 8 weeks before birth for the genotyped cows; g = a vector of random additive genetic effects following N of the dams were combined to form 21 classes . For DIM on the first test-day, 6 classes were defined: class 1: ≤ 10 days, class 2: 11–15 days, class 3: 16–20 days, class 4: 21–25 days, class 5: 26–30 days, and class 6: ≥ 31 days. Lactation length for lactation production traits was classified into 16 classes considering equal intervals between 275 to 305 days. The dummy variable indicating in-utero HS was 1 for the respective week for THI ≥ 60, otherwise, a 0 was assigned. We performed separate runs for each week before calving.Age at first calving was grouped into 6 classes . Calving age and lactation  considerhs) was The genomic heritability  equations according to the algorithm as introduced by Yang et al. . The modFor solving the equations of model GWA_nHS, we considered the variance components estimated from model VC_nHS . For solving the equations of model GWA_wHS, we considered the variance components estimated from model VC_wHS .xsnpi = a vector of centered genotypes calculated as msnpi−2psnpi ; bsnpi = a regression coefficient for the ith SNP (the main SNP effect); xinteri = a vector of centered genotypes for cows undergoing in-utero HS in the respective week before birth (consecutive runs for the 8 different weeks); and binteri = a regression coefficient for the ith SNP (the interaction effect) under in-utero HS. The remaining effects, vectors and matrices were identical with the effects as specified for models VC_nHS and VC_wHS.Matrices and vectors of models for genome wide associations were as follows: y) = Xb + xsnpibsnpi + xinteribinteri. Var(y) equals to Z was an identity matrix, because all phenotyped animals were genotyped.For GWAS models, E and V = V = i, respectively);Setting ith main SNP effect was: The test statistic for the ith interaction effect was: The test statistic for the P-value of the main and interaction effects for each SNP were determined by PBonf  =  0.05/(number of SNPs) = 0.05/41,139 = 1.22 × 10−6 (-log10(PBonf )) = 5.92. Additionally, a less stringent correction was applied and defined as suggestive threshold with Psugg = 0.05/(number of independent SNPs) = 0.05/3873 = 4.76 × 10−6 (-log10(Psugg)) = 4.89. The number of independent SNPs was calculated based on restrictions for linkage disequilibrium (R2 ≤ 0.15) for consecutive genomic windows including 500 SNPs  was calculated based on the chi-squared statistics as: LINK 2.0 , 26).Psugg were annotated to potential candidate genes as listed in the Ensembl genome database [P < 0.05 was considered to identify overrepresented GO terms for biological processes and reactome pathways. In addition, the windows with suggestive SNPs were mapped with the bovine QTL database through the online Data Analysis Tools [Suggestive SNPs according to database  for maindatabase , 29. Onldatabase , using tdatabase , 32. Theis Tools  to eluciis Tools , the pherhs in a range from 0.00 (WK6) to 0.07 (WK5). The rhs estimates for TFP were consistently very close to zero in response to climatic impact from all eight last weeks of gestation. For all traits, the interaction variances explained small proportions of the total phenotypic variance. In contrast to small interaction variances reported in our study, environment-specific genomic variances for feed efficiency traits [rhs of 0.07 in our study indicate that the effect of in-utero HS during late pregnancy is smaller than the herd effect due to geographical impact across latitudes. The time period from in-utero HS measurements until the trait recording date is longer for lactation records, but the rhs for lactation production traits were slightly larger than for the respective test-day traits. Nevertheless, the stronger impacts might be due to the accumulative in-utero HS effects during the whole lactation.The genomic heritabilities from a model without consideration of in-utero HS (model VC_nHS) as well as common genomic, interaction and residual variances estimated from a model with in-utero HS effects (model VC_wHS) are plotted in y traits  were lary traits , productrhs = 0.05), TFY (rhs = 0.05) and TPY (rhs = 0.03). For lactation yield traits, WK4 and WK5 displayed the largest rhs, with 0.06 and 0.07 for LMY, 0.06 and 0.05 for LFY, and 0.05 for LPY.In general, regarding in-utero HS, WK2 was most important for test-day yield traits, i.e., TMY  exceeded the respective thresholds . Number  content . AccordiIn general, for milk production traits, the identified suggestive SNPs for main effects, in particular associated SNPs on BTA3, 5, 6, 10, 14, and 20, were in agreement with the associated SNPs as reported in previous studies , 41. A qARS-BFGL-NGS-88748 (at 61.48 Mb on BTA19) with body size [BTB-01195369 (at 90.95 Mb on BTA3) with endocrine fertility traits [Hapmap51393-BTA-113111 (at 23.81 Mb on BTA8) with cow conception rate [ARS-BFGL-NGS-17754 (at 52.12 Mb on BTA18), ARS-BFGL-NGS-73518 (at 90.98 Mb on BTA3) and ARS-BFGL-NGS-34063 (at 118.09 Mb on BTA5), had an effect on weight, height at withers, and weaning weight, respectively, in Hanwoo cattle [Hapmap22875-BTA-155031 (at 112.90 Mb on BTA4) was significantly associated with tick resistance in Brazilian Braford and Hereford cattle [ARS-BFGL-NGS-106479 (at 79.75 Mb on BTA11) with intramuscular fat percentage in Australian beef cattle [APOB, detected as a causal mutation for cholesterol deficiency, embraces the SNP ARS-BFGL-NGS-106479 [With regard to interaction effects, model GWA_nHS generated more suggestive SNPs (51) than model GWA_wHS. This was especially the case for lactation production traits, because the inflation factors for the interaction effects of lactation traits substantially decreased when switching from GWA_nHS to GWA_wHS . The sugody size , BTB-011y traits , and Hapion rate . Anothero cattle . Hapmap2d cattle  and ARS-S-106479 . InteresDGAT1 gene, the annotated genes CPSF1, FOXH1, ARHGAP39, PPP1R16A, GRINA and MROH1 were frequently associated with milk production traits [GHR and ABCG2 were only detected as candidate genes for lactation, but not for test-day milk production traits. Only three candidate genes were located in close distance to suggestive SNPs for TSCS. However, no study reported a relationship between the three genes and TSCS. The gene ABCC9 harbors the most significant SNPs for production traits [WDR7 is associated with sperm motility and concentration in Holstein-Friesian bulls [TXNL1 has an effect on moisture content in rainbow trout [For test-day milk production traits, a total of 373 unique potential candidate genes are located in regions spanning 100 kb upstream to downstream of suggestive main SNPs from model GWA_wHS . In addin traits , 50, 51.n traits , the genan bulls , and theow trout . The resow trout . The secow trout .ABCA6-like transporters, ABCA6, ABCA8, ABCA9 and ABCA10, are all integrated in cholesterol-related pathways, either directly or based on their dynamic regulation upon cholesterol application [CCL14 and CCL16, play important roles in regulating bovine endometrial functions during early pregnancy [CCL5, HEATR9, MMP28, GIMAP family and VEGFC activate immune responses [GAS2L2 and NRIP2 participate in growth process [SCN1A and TTC21B genes cause several diseases [ETNPPL variants and the top 20 differentially expressed genes in beef calves in response to energy restriction during late gestation. GPC4 from the same family of the identified GPC5 and the directly identified gene RASL10B were favorably associated with ETNPPL [LSAMP and GRM8 are involved in emotional and motivational functions [MDGA2 gene encodes novel proteins which regulate neuronal migration and neural development [GPC5 gene was identified by Naderi et al. [FOCAD was identified by Alshawi et al. [C8H9orf85 was detected as a candidate gene for milk production traits in water buffalo [TULP3, IQSEC3 and FAM49A were identified as potential candidate genes for milk production traits in US Holstein cattle [RHNO1 and FOXM1 are linked to a variety of cancers and are defined as DNA damage repair regulators [FKBP4 gene in recurrent fetal losses in humans. Bourneuf et al. [de novo deleterious mutations in the FAM189A1 gene. The zinc-finger proteins constitute one of the most abundant groups of proteins in the mammalian genome and are involved in several cellular processes, differentiations of serval tissues, development of several diseases, as well as in tumorigenesis, cancer progression and metastasis formation [Due to the limited number of suggestive SNPs for interaction effects from model GWA_wHS, a smaller number including 62 unique candidate genes in the vicinity of the SNP locations, were identified Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Tablea: TMY = first test-day milk yield; TFP = first test-day fat percentage; TFY = first test-day fat yield; TPP = first test-day protein percentage; TPY = first test-day protein yield; TSCS = first test-day somatic cell score; LMY = first lactation milk.(XLSX)Click here for additional data file.S1 Appendix(R)Click here for additional data file.S1 FigDotted line = inflation factor of 1.05; TMY = first test-day milk yield; TFP = first test-day fat percentage; TFY = first test-day fat yield; TPP = first test-day protein percentage; TPY = first test-day protein yield; TSCS = first test-day somatic cell score; LMY = first lactation milk yield; LFP = first lactation fat percentage; LFY = first lactation fat yield; LPP = first lactation protein percentage; LPY = first lactation protein yield.(TIF)Click here for additional data file.


We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African  and Hispanic/Latino  ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1 × 10−4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including  Hematological measures  play critical roles in oxygen transport, immunity, infection, thrombosis, and hemostasis and are associated with many chronic diseases, including autoimmunity, asthma, cardiovascular disease, and viral infections. Hematological traits vary between self-reported race/ethnicity groups ,2,3, in Along with the lack of diversity in included populations, GWAS of hematological traits, similar to other complex phenotypes, also identify regions of associated variants whose biological function and target genes are often not clear. One approach to linking variants to genes  that has demonstrated success in various complex traits is transcriptome-wide association studies (TWAS) ,15. TWASWe examine here the utility of TWAS methods to understand previously identified GWAS loci for blood cell traits, as well as to identify new candidate loci and genes. We focus on underrepresented AA and HL populations and evaluate whether use of relevant ancestry-specific eQTL reference panels from the Multi-Ethnic Study of Atherosclerosis (MESA) improves power for blood cell trait genetic discovery (above primarily European TWAS reference panels).We used three different eQTL datasets, namely Depression Genes and Networks (DGN), MESA AA, and MESA HL, to train gene expression models and then performed association analysis for AA and HL populations and then replicated the results in larger UKB (UK Biobank) EA populations. The study overview is showed in Genotype Data. Using genotype data from 6 African ancestry cohorts  and 4 Hispanic/Latino cohorts   on the Michigan Imputation Server  studies , and our own recent TOPMed imputed GWAS analyses . These previous efforts all included some but not all of the cohorts included here. In our comparisons of expression reference panels, using one of the largest expression datasets in African Americans (GENOA LCL data) as a testing dataset for comparing imputed to true gene expression , we observed that the larger sample size for DGN  led to slight improvements in model performance over MESA AAs. The generalizability of these comparisons in GENOA is limited by tissue heterogeneity between the DGN and MESA reference panels, however. However, we see additional significant gene-trait associations using the ancestry-matched monocyte eQTL reference panels not captured using DGN, and these TWAS models generally include a high percentage of variants more common in non-European populations. This is true even for red blood cell and platelet traits; in theory, a monocyte-specific eQTL dataset should be a poorer model for these traits than whole blood, as unlike in whole blood no transcripts from platelets or red blood cells would be included ). Finding new, replicated loci in much more modest sample sizes than for current trans-ethnic GWAS efforts  in European ancestry populations and smaller but ancestry-matched reference panels from MESA monocytes. To our knowledge, this is the largest genetic discovery effort yet conducted in these populations for blood cell indices, exceeding available sample sizes from recent trans-ethnic blood cell traits GWAS  also hiPrevious work has also suggested the value of ancestry-matched TWAS reference panels. Recent work from Geoffroy, et al.  found moTNFAIP2. Previous studies reported that TNFAIP2, identified by TWAS with the MESA HL reference panel for platelet count, is related to acute promyelocytic leukemia [TNFAIP2 was involved with the protective mechanism of Amentoflavone in the hematopoietic system of mice against γ-irradiation [Our work also identified biologically relevant genes for hematological traits at loci not containing previous GWAS signals, as well as genes which may explain known GWAS signals in gene-dense regions or highlight additional sub-genome-wide significant conditionally distinct signals at known loci, such as leukemia ,59 and mleukemia  and angileukemia . It is aleukemia  and in mleukemia , and plaleukemia ,64,65. Oadiation . While tadiation ,38 ; also, the tissue used for GENOA gene expression quantification, LCLs, differs from that in MESA (monocytes) and DGN (whole blood). Comparisons in GENOA are thus complicated by tissue heterogeneity, though we note that prior studies have performed comparisons (for example across eQTLs) across these relatively similar blood cell related tissues . Thirdlhreshold  might beIn summary, TWAS analysis can help to enhance our understanding of the genetics of complex traits, including hematological indices. TWAS not only helps to identify potential causal genes at known GWAS loci, but can also increase power for new candidate gene and locus discovery. Some gene-trait associations in our analysis were only revealed by ancestry-matched TWAS reference panels due to the difference in MAF of included variants for expression prediction across populations. More work is needed to both generate large expression datasets in non-European-ancestry populations and apply these datasets to TWAS discovery for complex traits across ancestrally diverse cohorts.