PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including  GABRB, PPP3R1 and CRYAB, etc. have been identified as candidate genes related to MC and REA [Growing consumer demand for high-quality beef has promoted the selection of cattle for meat quality traits ,2. Backf and REA ,13,14. M and REA ,16,17,18 and REA . In recent years, genome-wide association studies (GWAS) have pinpointed thousands of variants of human diseases ,21,22. HNCAPG and LCORL related to BFT [ACDSB gene in the β-oxidation process, which in turn affects meat color stability, has already been reported with single and multiple-trait SNP-GWAS in Nellore cattle [ENPP2, POLI, and CPAMD8 as candidate genes for MC and FC traits [CAPN1, CAST, MYOM1, and CALCOCO1, involved in metabolic and cellular processes that have potential impacts on the meat quality of Angus cattle [So far, many previous studies have reported on causal genes for meat quality via SNP makers in cattle. For instance, one study has utilized SNPs to map the QTL on BTA6 in approximately 3500 cattle from multiple cattle breeds, encompassing d to BFT . In parte cattle . Also, aC traits . A separC traits . Moreoves cattle .However, few studies have conducted genome-wide CNV analyses for assessing meat tenderness in Nelore cattle . TherefoThe studied population consisted of 1466 Chinese Simmental beef cattle, which were born between 2008 and 2022 in Ulgai, Inner Mongolia. All cattle were uniformly managed and fed a standardized diet based on the total mixed ratio (TMR) recommended at an average age of 22 months, and then they were transferred to Inner Mongolia ZhongAo Food Co., Ltd.  for slaughter via a consistent and streamlined process. No ethics statement was required for the collection of the genetic material. The data from animals included in this study were derived from previous analyses that obtained specific permissions.Blood samples were obtained with routine quarantine inspection before slaughter. Samples were genotyped using an Illumina BovineHD SNP array containing 777,962 SNPs. The SNP positions were obtained according to the ARS-UCD1.2 genome assembly. More details were described in our previous study .Six meat quality traits, namely fat color (FC), meat color (MC), marbling score (MS), backfat thickness (BFT), pH, and ribeye area (REA), were selected for the subsequent analyses. The REA of the cattle was measured at the end of the fattening period between the 12th and the 13th rib (longissimus thoracis muscle area). Afterwards, the carcass was chilled for 48 h (partly 24 h post-slaughter). BFT was measured after slaughter in the same position as the REA. The pH was measured at three locations of steak from the twelfth rib at slaughter using a Mettler Toledo pH meter . Marbling score was assessed from 1 to 5. Meat color was graded in seven categories against a standard color palette, from pale (grade 1) to dull red (grade 7). Fat color was graded in seven categories against a standard color palette. More details of the measurement of phenotypes for meat quality were presented in previous analyses .p < 0.05) were tested using variance analyses. A general linear model was used to analyze the traits so that batch and sex were considered as the fixed effects, and weight before fattening and PCs 1–3 were regarded as the covariates. The fixed effects and covariates were adjusted in the linear regression model and the residuals were further considered as adjusted phenotypes for the association test. Heritability was calculated using GCTA [The significant effects . Genome Studio 2.0 were employed to extract signal intensity data including Log R Ratio (LRR), B allele frequency (BAF), and population frequency of the B allele (PFB) file. CNVs were detected using PennCNV v.1.0.5, which is based on a hidden Markov model (HMM) . The PFBThe CNVs were merged into CNV regions (CNVRs) using HandyCNV . The “cap-value was considered as an indicator for this segment. The significance level of the FDR correction (FDR  <  0.05) was used to determine the associated CNV segments.The CNV-based GWAS were performed using a CNVRanger package  between p < 0.05.We then employed CNVtools, a tool that amalgamates signals from multiple CNV probes to generate a one-dimensional LRR profile fitted with Gaussian mixtures . This meTo confirm the identified CNVs, we further carried out validation using a ~20× whole-genome sequencing (WGS) dataset . To measp < 0.05). STRING  was utilized to search for enriched pathways and protein domains, and identify PPI networks based on candidate genes.Next, we explored the candidate genes based on the identified CNVs via a UCSC genome browser (ARS-UCD 1.2). Genes overlapping with the detected significant CNV segments were extracted for further analysis using bedtools . GALLO sTBX1 and CSMD3  were more abundant than duplications (23.4%) across the genomes. Respectively, the length of the duplications was between 634 bp and 1.6 Mb (3–304 probes), while deletions ranged from 602 bp to 1.7 Mb (3–509 probes). The fraction of homozygous CNVs (CN 0 or 4) was low , which has moderate heritability (0.40) ,10,11. DKRT4, KRT78, KRT79, TRIM77, and TRIM64 , though it did not show significant association signals using region-based association analysis (p = 0.0629).To further investigate the significant association of CNV segments, we explored potential correlations between raw Log R Ratio (LRR) values and corresponding meat quality traits with a robust statistical analysis framework. After a likelihood ratio test, we found significant associations for MC, MS, pH, and BFT based on the nominal  on BTA5 A,B and C on BTA5 C,D for M on BTA5  and CNVR on BTA5 . In contPCDH15 gene  data from the same cattle. As shown by the Integrative Genomics Viewer (IGV), the accuracy levels of CNV calling obtained from the array were consistent with the whole-genome sequencing data . Among tH15 gene . AdditioH15 gene . In addiH15 gene . NotablyKRT4, KRT8, KRT18, etc. (10P = 5.02) was located within CNVR1, which was validated with read-depth signals from sequencing data. Using CNV-based GWAS and region-based association, we further identified an association signal at 27.1 Mb on BTA5 for MC, which was overlapped with KRT78, KRT79  on raw LRR values for the candidate regions identified using CNV-based GWAS [It was noted that the detection of CNV segments for meat quality traits using genome-wide association analysis has been demonstrated in many previous studies ,53,63,64sed GWAS ,65. LRR sed GWAS ,67,68. Bsed GWAS .KRT4 gene have been proven to encode mucosa-specific keratins which may cause white sponge naevus in humans [KRT79 gene lead to the destabilization of K14 in sebocytes, attenuating sebocyte differentiation and lipid content in the skin [KRT4 gene was found to bind Tβ4, mediating the ERK signaling pathway in cashmere goats, which might control the developmental patterning of hair follicles [KRT4 and KRT78 were higher in telogen compared to catagen and anagen through analysis of the transcriptome data of yak hair follicles at different developmental stages [In the present study, we used WGS data from the same population of cattle based on read-depth signals to validate their CNVs, and compared them to the occurrence of CNVRs from WGS samples of multiple cattle populations. Strikingly, among these twelve CNVRs, four candidate CNV segments were found to be partially overlapped with the CNVRs of high confidence from other cattle populations. Eventually, we characterized CNVR1 and confirmed the presence of the ~12 kb CNV at 27.119 Mb on BTA5 in Bos taurus and Bos indicus based on read-depth signals which have undergone selection in a previous report . The pren humans . Meanwhithe skin . In liveollicles . Previoul stages . DespitePCDH15, CSMD3, TRIM77, and TRIM64. For MS, the PCDH15 gene, which encodes protocadherin, has been reported to play an essential role in the stereocilia development of mice and glutamate levels [PCDH15 genes were related to meat production in lambs and Tibetan sheep [CSMD3 was reported to have an impact on autistic spectrum disorders and immunity via QTL information [TRIM77 and TRIM64 are members of the TRIM family of E3 ubiquitin ligase proteins. Specifically, these genes play an important role in foam cell formation and cytokine production via the NF-κB signaling pathway. Pathway analysis of the candidate genes highlighted several processes for intermediate filament regulation. Interestingly, a recent study reported that an overexpression of TRIM64 suppresses lipid accumulation [TRIM64 have been partially explored regarding cancers, these genes in livestock remain unknown [TRIM77 and TRIM64 for BFT, suggesting that these two genes may be involved in lipid metabolism. We also identified several CNV segments for BFT and MS, which were overlapping with genes such as e levels ,74,75. Man sheep ,77. CSMDormation ,79. Addimulation . While t unknown ,81. Our Notably, we found that this CNV segment harbored a 3.5 kb duplication , which have been indicated that under selection for climatic adaption in previous study . Our strKRT4, KRT78, and KRT79 for MC. Our findings provide novel insights into elucidating the genetic basis of meat quality traits from the aspects of CNV and may offer further potential markers for selection during the breeding process of farm animals.Our study identified twelve candidate CNV segments for meat quality traits by using CNV-based GWAS of beef cattle. By integrating region-based association and sequence validation based on read-depth signals, we wholly obtained two candidate CNV segments (corresponding to KRT and TRIM) for MC and BFT. Moreover, we reported a 34 kb duplication on BTA5 harboring


Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies. Evaluation of 17 published functional weighting methods for improving GWAS statistical power demonstrates a tradeoff between high sensitivity and high positive predictive value. Yet, a survey of the GWAS catalog identified 88 traits without genome-wide significant findings despite theoretically adequate sample size2. Traits with worse than expected performance even when thousands of cases are available include autism spectrum disorder3, heart failure5, major depressive disorder (MDD)7, and some addictions11. Increasing sample size to increase statistical power for discovery is not always practical, as encountered for rare diseases12, expensive phenotyping13, phenotypic heterogeneity14, hard-to-reach or socially disadvantaged populations15, and population isolates16. Our ability to discover trait-associated loci that are ancestry-specific or subject to gene-environment interaction lags in a field where the overwhelming majority of GWAS samples are of European ancestry17. Further, increasing sample size sometimes fails to achieve the expected gain in significant loci18.The genome-wide association study (GWAS) has been widely successful for discovering genetic loci contributing to complex traits19. An evaluation of gene- and pathway-based GWAS methods found low sensitivity overall for discovery, and that high sensitivity was achieved at the expense of more false positives20. Methods to combine GWAS summary statistics with additional information to perform in silico functional follow-up are plentiful25 and range from fine-mapping to determining the biological underpinnings of the variant-trait association; many of these in silico approaches to uncover the functional causes underpinning GWAS associations have been previously reviewed30. Some authors suggest that a secondary usage of these methods is to augment the ability of a given GWAS to identify novel trait-associated loci. Evaluation of the performance of such methods for locus discovery has been done ad hoc for select methods,31 but to our knowledge, a comprehensive evaluation of many methods and multiple GWAS traits against objective criteria has not been published.Attempts to improve the discovery power of GWAS without increasing sample size by incorporating functional information, defined here as regulatory annotation of variants or evidence of pleiotropy, is not newp-value threshold, applied to multiple waves of GWAS for five diseases and traits. We applied these methods to publicly available GWAS summary statistics and evaluated their ability to nominate trait-associated loci that were confirmed by a subsequently larger, more powerful GWAS, henceforth referred to as GWAS1/GWAS2/GWAS3, for the same trait. To represent varying genetic architectures, phenotypic heterogeneity, and gene regulation by tissue type, we selected three psychiatric traits: schizophrenia, bipolar disorder, and MDD available from the Psychiatric Genomics Consortium (PGC); and two blood cell traits: mean platelet volume (MPV) and white blood cell (WBC) counts, available from the UK Biobank.To identify suitable method(s) for improving GWAS statistical power to uncover novel loci, we performed the largest, most comprehensive evaluation of published functional weighting methods to date: 17 functional weighting methods, and an unweighted suggestive p-value threshold of 1 × 10−5 as an 18th method . The gene-based methods nominated fewer loci than the variant-based methods. For both EUGENE and SMR, which were applied using Brain eMETA cohort annotations, nominated genes tended to have higher MAGMA p-values  between nominated loci and gold standard loci. We performed a sensitivity analysis utilizing different minimum overlaps of one base, 500 kb, and 750 kb. In general, we found a slight reduction in SN and PPV with increasing size of the required overlap for all five traits  annotation-based; (b) pleiotropy-based; or (c) eQTL-based.ii.Utilized GWAS summary statistics, as opposed to individual-level genotype data.iii.Implemented using freely-available software or packages.iv.46).Provided either method-specific annotation or eQTL files for use with the method, or were amenable to use with publicly available annotation datasets  and applied them to each of the five GWAS1 using the signal prioritization software GenoWAP48. We used the recommended posterior probability of 0.50 to define statistical significance.We downloaded the prediction scores for the human genome smoothed over 10-kb segments49  based on the Roadmap Epigenomics Project (Roadmap) for whole blood and brain tissue and applied them to the blood traits and psychiatric traits, respectively, using the signal prioritization software GenoWAP48. We used the recommended posterior probability of 0.50 to define statistical significance.We downloaded tissue-specific functional predictions50, we calculated both binary and general eQTL-based weights for all five traits. In each case, we set α = 0.05 and power = 0.6. For binary weights, the parameter M was the number of included variants in each GWAS1, respectively, and ε was calculated as the percentage of eSNPs, defined as those with significant eQTL associations in the relevant tissue. We used the significant GTEx v7 Brain Nucleus Accumbens for PGC traits and the significant GTEx v7 Whole Blood for blood cell traits. Weights were then normalized and applied to the downloaded p-values. Statistical significance was defined as pweighted < 5 × 10−8.Following the method of Li et al.10peQTL) for eSNPs and 1 for all others, where eSNPs are defined as above. Weights were then normalized and applied to the downloaded p-values. Statistical significance was defined as pweighted < 5 × 10−8. The parameters α, power, and M were also defined as above.The general eQTL weight was calculated as √(−log51 (GPA) was performed using pairwise comparisons between two traits of interest. For each pair of traits, we matched variants on hg19 chromosome and position. In the case of duplicate variants, the variant with the smaller p-value was retained. We performed GPA with both global and local FDR strategies using a cutoff of 0.05 in both cases to determine statistical significance.Genetic analysis incorporating Pleiotropy and Annotation52, BMI53, height54, and two GWAS for HDL56 . FDR cutoffs were defined as above in all cases.For each of the three PGC traits, we used as pleiotropic traits the remaining two PGC traits. For blood cell traits, we used the second blood cell trait as a pleiotropic trait for the first. Additionally, we used SCZ133) was performed using pairwise comparisons between two traits of interest. For all traits, we used the subset of downloaded variants with valid rsIDs and allele frequencies in the downloaded GWAS1 summary statistics. Statistical significance was defined as pMTAG < 5 × 10−8. For each of the three PGC traits, we used as pleiotropic traits the remaining two PGC traits. For blood cell traits, we used the second blood cell trait as a pleiotropic trait for the first. Additionally, we used SCZ152, BMI53, height54, and two GWAS for HDL57 .Multi-Trait Analysis of Genome-wide association summary statistics , moderate impact (p < 1.1 × 10−7), low impact (p < 1.0 × 10−8), and other (p < 1.7 × 10−9). We annotated GWAS1 summary statistics using SnpEff software60 and applied the aforementioned p-value cutoffs according to the annotation category to determine statistical significance.We applied the functional-weighted GWAS method described by Sveinbjornsson et al.61) following the example annotations of the method authors, requiring three sets of input: variants and p-values from GWAS summary statistics, ANNOVAR62, and GenoSkylinePlus63 using annotations from the original source. We downloaded the hg19 annotations from the ANNOVAR website and used the dbSNP147 database to annotate GWAS1 variants. Annotations were then collapsed into nine categories: downstream, exonic, intergenic, intronic, ncRNA/exonic, ncRNA/intronic, upstream, 3’UTR, and 5’UTR, with each variant assigned a value of 0 or 1 to denote category membership.We performed latent sparse mixed model . We then used LiftOver66 to convert all five GWAS1 from hg19 to hg38 genomic coordinates. We applied the Nucleus Accumbens eQTL dataset For PGC traits and the Whole Blood eQTL dataset for blood cell traits.We performed the fastENLOC implementation of the ENLOC method67, grouped GTEx brain tissues as the eQTL data for the PGC traits46, and grouped whole blood eQTL data for the blood cell traits69 after performing additional quality control on the whole blood eQTL data to remove discrepant rsIDs. We used Satterthwaite’s approximation to calculate the gene-based summary statistics70. We then estimated the FDR thresholds using EUGENE and identified the p-value threshold closest to the FDR threshold of 0.05 to determine statistical significance . For all traits, we used the subset of downloaded variants with valid rsIDs. For blood cell traits, in the case of duplicate rsIDs, we retained the variant with the smaller p-value. Statistical significance of the results was determined by a Bonferroni correction applied to the JEPEG p-value.We downloaded the SNP annotation data (v0.2.0) and reference panel (1000 Genomes EUR Phase 1 Release 3)46. The methylation dataset used for PGC traits was downloaded from the processed data available on the GEO data repository at accession number GSE74193 and reflects the identification of meQTLs in the prefrontal cortex of 191 schizophrenia patients and 335 controls without psychiatric illness72. We defined a region to test for colocalized signal as +/−200 kb upstream and downstream from start and stop positions of a single eQTL probe, and included all GWAS1 SNPs and meQTL probes contained within that region. This was repeated for all eQTL probes available in the downloaded dataset.For PGC traits, our colocalization dataset of choice was the significant eQTL dataset for expression in the Nucleus Accumbens from GTEx, v773 at the middle-aged timepoint (https://data.bris.ac.uk/data/dataset/r9bxayo5mmk510dczq6golkmb).For blood cell traits, we repeated the same procedure using the significant Whole Blood GTEx v7 dataset. The methylation dataset used for blood cell traits was the methylation QTL results from the ALSPAC Accessible Resource for Integrated Epigenomics Studies (ARIES)74, 1% for bipolar disorder75, and 15% for major depressive disorder76. For the UK Biobank traits, the Sherlock-provided eQTL data was chosen as GTEx v7 Whole Blood, and the sample sizes from Supplementary Data 58 to match gene symbols to Ensembl IDs and evaluated the overlap with our gold standards using Ensembl IDs.Only variants with valid rsIDs were submitted to Sherlock for each GWAS. For the PGC traits, the Sherlock-provided eQTL data was chosen as GTEx v7 Brain – Nucleus accumbens. Sample sizes from Supplementary Data https://cnsgenomics.com/software/smr/#DataResource). For PGC traits, we evaluated the performance of SMR using three different eQTL datasets: GTEx v7 data from the Brain Nucleus Accumbens46, the “lite” version of the GTEx v7 data from the Brain Nucleus Accumbens, and the Brain-eMeta eQTL data77 derived from a meta-analysis of GTEx brain, Common Mind Consortium78, and ROSMAP consortium79 studies. For UK Biobank traits, we evaluated the performance of SMR using the GTEx v7 data from Whole Blood46, both full and “lite” versions. For all datasets, we evaluated with and without the requirement for a Heidi p-value of <0.05 to exclude trait-eQTL associations due to pleiotropy.For all GWAS1 inputs, we used the subset of downloaded variants with valid rsIDs and valid allele frequencies. Formatted eQTL data were downloaded from the SMR website . We downloaded (https://gusevlab.org/projects/fusion/) and applied pre-computed gene expression weights for GTEx v7 Brain Nucleus Accumbens for PGC traits and Whole Blood for blood traits46.For all GWAS1 inputs, we used the subset of downloaded variants with valid rsIDs. We downloaded reference LD data for the 1000 Genomes EUR samples provided by the Broad Institute Alkes Group (46 tissues (https://github.com/Joker-Jerome/UTMOST). For all five GWAS1, we evaluated the full cross-tissue expression UTMOST results. We additionally evaluated the single-tissue UTMOST output for the Nucleus Accumbens for PGC traits and Whole Blood for blood traits.For all GWAS1 inputs, we used the subset of downloaded variants with valid rsIDs. We used the pre-calculated covariance matrices using the 44 GTEx v7At least two “waves” of GWAS summary statistics that were publicly available for download.Both available waves were conducted in the same ancestral population.The waves had to differ in sample size to such a degree that the larger GWAS contained more genome-wide significant associations than the smaller wave.We evaluated the performance of the functional weighting methods using five traits meeting the following criteria:52 (SCZ), bipolar disorder80 (BPD), and major depressive disorder14 (MDD). We also evaluated two blood cell traits examined in the UK Biobank, mean platelet volume (MPV) and white blood cell count (WBC)81, as examples of traits with a larger explained heritability, many genome-wide significant loci, minimal heterogeneity in phenotyping, and comprehensive tissue-specific functional annotations. In addition to meeting the above criteria, these five traits, collectively, represent the spectrum of GWAS discovery, from no statistically significant variants to identifying nearly all common variation in the studied population. Additional details of the GWAS used to test the functional weighting methods are presented in Supplementary Data We deliberately chose early phase GWAS (which we refer to as GWAS1) for each trait to allow for validation of results in subsequent GWAS for the traits (referred to as GWAS2 and/or GWAS3). We evaluated three traits with summary statistics available from the Psychiatric Genomics Consortium (PGC): schizophrenia82 to convert the GWAS of the psychiatric traits from hg18 to hg19. For MPV, p-values were truncated at 7.41 × 10−323, due to the extremely small p-values not being read into R (v3.6.0).We used Liftover−8. We defined a significant locus as the region extending +/−500 kb from the variant with the smallest p-value. Additional variants with genome-wide significant p-values within this region were included within the locus of the lead variant. This procedure was repeated in a stepwise fashion until all genome-wide significant variants were captured. As a final step, overlapping one-megabase intervals were combined into a single locus, and the extended HLA region was defined as the region spanning from base pair 25,000,000 to 35,000,000 on chromosome 6.For comparison to each GWAS1, we used as our “gold standard” a larger, more powerful GWAS, hereafter referred to collectively as GWAS2, performed on the same trait and by the same consortium to reduce variability in findings due to differences in trait definition, analytic strategies, or recruitment of study participants , positive predictive value (PPV), and the F1 score . Definitions can be found in Supplementary Data p-value threshold, provided results for individual genetic variants . A TP was defined as an overlap of at least 250 kb in the 1 MB flanking window of a top locus in GWAS2, as defined above, and a +/−500 kb window of a variant nominated by a functional weighting method. A FP was defined as a +/−500 kb window nominated by a functional weighting method with less than 250 kb overlap among any GWAS2 loci. A FN was defined as a GWAS2 locus with less than 250 kb overlap with any locus nominated by the functional weighting method being evaluated. This locus-to-locus comparison was performed assessing any degree of overlap between the gold standard GWAS2 loci, excluding the significant GWAS1 loci, and the loci calculated from the results of the functionally weighted GWAS1 using the GenomicRanges packagep-values using MAGMA84. As most of the eQTL data used in these comparisons came from GTEx, we downloaded and used their GENCODE annotations58 for transcript/gene names and genomic locations. Statistical significance for GWAS2 was determined at a GWAS-specific Bonferroni correction to the MAGMA p-value after excluding eQTL-based gene results that did not yield a MAGMA p-value.To comparably evaluate methods that yield results on the level of eQTL or gene, we calculated transcript- or gene-based For methods that did not provide a significance threshold, we first excluded any results that did not result in a valid statistic, then performed a Bonferroni correction based on the number of remaining tests. To exclude established significant loci from GWAS1, we excluded nominated transcripts/genes where the midpoint of the genomic location was within +/−500 kb of the GWAS1 loci, defined above (see the section “Exclusion of significant GWAS1 hits”). We did not exclud genes from either GWAS1 or GWAS2 MAGMA results.For MAGMA-based evaluations, TP, FP, and FN were determined by matching either the Ensembl ID or gene symbol, depending on what was used by the particular functional weighting method  and the loci calculated from the results of the functionally weighted GWAS1 using the GenomicRanges package86 in R. To construct the UpSet plot, for each trait, functional weighting GWAS methods were ordered from largest to smallest number of nominated loci, defined using +/−500 kb from either the top variant or gene midpoint. For methods with multiple options, the top performing option was selected based on largest PPV. A matrix of nominated loci vs the fwGWAS methods was created in a stepwise fashion. The method nominating the largest number of loci was populated first, and then each of its nominated loci was tested for overlap of at least 250 kb with loci nominated by all other methods and these overlaps populated the matrix. For each subsequent functional weighting GWAS method, only nominated loci that had not been found to overlap with loci from previously examined methods were added to the matrix. These new additions were then checked for overlap with all remaining method nominations, and all methods nominating a new locus were noted on the matrix.To identify an optimal ensemble approach, we examined the overlap among nominations across functional weighting methods for each trait by generating UpSet plots. Plots were generated using the ComplexUpset packagep-value or a false discovery rate cutoff of 0.05, as appropriate for the statistics calculated by the functional weighting method.Full details of the application of each functional weighting method can be found in the Supplementary Note For the choice of functional database to use with each method, our default was to use a preformatted database provided by the method developers . When multiple databases were made available , we chose the largest database representing a tissue type appropriate to the model trait being evaluated.89 and the corresponding statistically significant GTEx v7 whole blood data for the two blood cell traits46.When no functional database was made available by the method authors , we used the statistically significant GTEx v7 nucleus accumbens data downloaded from the GTEx data portal to apply the functional weighting methods to the three psychiatric traits52, HDL cholesterol57 , BMI53, height54, and the other blood cell trait81.We investigated the performance of the pleiotropy-based methods GPA and MTAG in each psychiatric trait using contemporaneous GWAS of the other two psychiatric traits. For the blood cell traits, we used a variety of potentially omnigenetic traits: SCZTo determine whether the 250 kb overlap between a nomination and a novel GWAS2 locus impacted our results, we tested overlaps of 1 base, 500 kb, and 750 kb, used to replace the +/−500 kb and Ensembl locus definitions.90 and a cutoff of q < 0.05 was used to determine statistical significance.We investigated whether a less stringent cutoff resulted in better performance by applying an FDR significance cutoff for those methods  for which we used a Bonferroni multiple testing correction. The FDR correction was implemented using the fdrtool R packageWe sought to determine the accuracy of our FP definition by using wave 3 GWAS, hereafter referred to as GWAS3, recently released by the PGC for the three psychiatric traits. Known loci from GWAS1 were excluded as described above.Further information on research design is available in the Peer Review FileSupplementary FiguresDescription of Supplementary MaterialsSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Note 1Supplementary Note 2Supplementary Note 3Reporting Summary


Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the accuracy of pedigree-based estimated breeding values was 0.12 for honey yield, and ranged from 0.42 to 0.61 for the workability traits. The inclusion of genomic marker data improved these accuracies to 0.23 for honey yield, and a range from 0.44 to 0.65 for the workability traits. The inclusion of genomic data did not improve the accuracy of the disease-related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. For all traits except the Varroa resistance traits, the bias with genomic methods was on a similar level compared to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees.Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyzes the accuracy and bias of pedigree-based and genomic breeding values for honey yield, three workability traits, and two traits for resistance against the parasite  Genomic selection  of breeding values began in 1994 for the population registered on BeeBreed. The EBV enabled hundreds of mostly Central European bee breeders to improve the quality of their stock  appeared as an efficient solution . Honey yield was measured in kg, and the values were corrected for outliers as described in  were removed. This left 63,240 markers for further analysis. A total of 312 queens were removed because less than 90% of all the valid markers were called in their samples, indicating low DNA quality. After comparisons of daughter and parent based on the number of opposing homozygotes, 207 queens were removed . We used Abase = 0.40  in Brascamp and Bijma  as follond Bijma , respectWe provide two concepts of the heritability of the sum of maternal and direct effects. Firstly, heritability is usually defined as the fraction of phenotypic variance due to additive genetic effects. In honey bees, the corresponding concept is the heritability of the genetic component of the phenotype, nd Bijma  as folloSecondly, in the classical theory of animal breeding, the heritability can be used to predict short-term genetic gain, but n et al.  using thThe numerators of u et al. .y is a vector of observations on colonies; b a vector of fixed effects (year and apiary); a a vector of direct effects of queens, worker groups or sires; m a vector of maternal effects of queens, worker groups or sires; e a vector of residuals; and X, Za, and Zm are known incidence matrices for b, a, and m, respectively. For a, m, and e, the expected values were assumed to equal 0, while their covariance matrix was given by:A is the honey bee-specific numerator relationship matrix derived from pedigree  without the phenotypes of all queens born in 2017 or later, and (2) without the phenotypes of queens born in 2016 or later. For the validation procedure, the EBV of the 265 genotyped queens born in 2017 from scenario 1 were merged with the EBV of the 994 genotyped queens born in 2016 from scenario 2, and likewise for the EBV of the corresponding worker groups. Thereby, the validation sets of the two scenarios, i.e., the genotyped queens born in 2017 and 2016, respectively, could be treated as a single validation set. In the five-fold cross-validation, only apiaries with at least five performance-tested queens were included to ensure reliable estimates of fixed effects. This left 1281 genotyped queens for validation. Each apiary was randomly split into five equally sized partitions, splitting the 1281 queens into five partitions. For each partition, EBV were estimated using PBLUP, ssGBLUP and WssGBLUP without the phenotypes of the animals on this partition. The results from all partitions were merged, so that the five partitions could be treated as a single validation set of 1281 queens and their worker groups. The procedure was repeated six times from the split of the apiaries on.g, as follows:C, by C, C, and y − Xb as the vector of phenotypes corrected for fixed effects. We prove Eq. . Similar bootstrapping methods were used in other studies (Iversen et al. A bootstrap procedure was used to test whether the accuracies of WssGBLUP and ssGLUP were significantly higher than the accuracy of PBLUP. In total, 10,000 bootstrap sample vectors were constructed by sampling validation queens with replacement, and the accuracy with PBLUP, ssGLUP, and WssGBLUP was calculated for each vector. Two methods were considered significantly different, if the same method had higher accuracy in 97.5% of all sample vectors (b1, of y − Xb on b1 < 1 and b1 > 1 indicate inflation and deflation of the genetic components of the phenotypes compared to the phenotypes corrected for fixed effects, respectively.The regression coefficient, Estimates of the genetic parameters are shown in Table The accuracies of the methods under investigation in the generation validation are shown in Fig. The accuracies of the methods under investigation in the five-fold cross-validation are shown in Fig. Overall, both validations showed similar results, although the accuracy was higher in the five-fold cross-validation, and the increases in accuracy with ssGBLUP and WssGBLUP over PBLUP were higher in the generation validation.b1 of phenotypes corrected by fixed effects on the predicted genetic component of the phenotype. The results for EBV from PBLUP, ssGBLUP and WssGBLUP in the generation validation are shown in Fig. b1 deviated the most from 1 for VID with WssGBLUP and honey yield with PBLUP by −0.59, and −0.52, respectively. While WssGBLUP showed overall the most inflation, the difference between PBLUP and WssGBLUP ranged only up to 0.16, which was relatively small compared to the deviation from 1 with PBLUP. For ssGBLUP, the results were overall similar to PBLUP, although ssGBLUP was considerably less biased than PBLUP for honey yield and VID.Bias was calculated as the regression coefficient b1 < 0.8. For hygienic behavior, EBVs from PBLUP were nearly unbiased, while the genomic methods produced inflated EBVs.The results for EBV from PBLUP, ssGBLUP and WssGBLUP in the five-fold cross-validation are shown in Fig. The estimated heritabilities Table  were in The results on the difference in accuracy between WssGBLUP and PBLUP can be explained with the results on the heritabilities Table . Traits Varroa resistance-related traits were also affected by problems in gathering data. The number of genotyped queens with phenotype for both traits was about 200 queens lower than for honey yield, gentleness, and calmness. Furthermore, the number of phenotyped queens on apiaries with a genotyped queen (Table Varroa-related traits, which might have led to less accurate fixed effects. The results for VID are also due to the low heritability of the genetic component of the phenotype for this trait, because simulation studies in honey bees and other species show that traits with low heritability also have low accuracy of pedigree-based and genomic EBV (Gowane et al. Varroa-specific hygienic behavior is the subject of ongoing research (Conlon et al. Varroa-related traits.The results for the The accuracy of ssGBLUP was slightly lower than the accuracy of WssGBLUP for most traits. This result is common in studies for several other agricultural species using WssGBLUP (e.g., Lu et al. The accuracies in the five-fold cross-validation were for the majority of the traits higher than in the generation validation. This is due to the fact that in the five-fold cross-validation, sibling groups are evenly distributed across the partitions, while the phenotypes of whole sibling groups might be removed for the calculation of EBVs in the generation validation. Therefore, the five-fold cross-validation is a validation within sibling groups, while the generation validation is similar to a validation across sibling groups. Studies in other species found that validations within sibling groups show higher accuracies than validations across sibling groups (Gao et al. According to a simulation study in honey bees (Bernstein et al. The larger reference population is likely to obviate the need to run WssGBLUP instead of ssGBLUP, since a simulation study showed that WssGBLUP and ssGBLUP yield the same results for large reference sets (Lourenco et al. Ag in Eq. (In the generation validation, inflation was observed with all methods Fig. . Howeverg in Eq.  (McMillag in Eq. .The availability of genomic breeding values offers new possibilities in breeding schemes for honey bees. In classical breeding schemes, queens spend the first months of their life building a colony. When the queens are 1 year old, they are used as drone-producing queens to inseminate other virgin queens, or phenotyped to be selected as dams of new queens when they are 2 years old. A simulation study of innovative genomic breeding schemes (Bernstein et al. Gathering genomic data from honey bees requires special considerations, due to their small body size, and their genetic diversity within a hive. Non-lethal ways to genotype queens are available (Jones et al. Varroa resistance traits, the bias with WssGBLUP and ssGBLUP was on a similar level compared to the bias with PBLUP. For the Varroa resistance traits, the genomic methods offer too little improvement over PBLUP to be recommended based on the current data set, which is likely due to the size of the reference population. A larger reference population or the discovery of new causative SNPs for Varroa resistance are required to increase the accuracy of genomic methods for hygienic behavior and VID. The results suggest that genomic selection can be successfully applied to honey bees.WssGBLUP offers significantly greater accuracy than PBLUP for honey yield, calmness, and swarming drive. For gentleness, the accuracy of WssGBLUP was greater than the accuracy of PBLUP to a similar degree as for calmness, but the difference remained below the threshold for significance. For all traits, except the Approximation of the prediction accuracy for the genetic component of the phenotype


The present study was aimed primarily for estimating various genetic parameters  of reproduction ; production  and lifetime traits  in Tharparkar cattle to check the association of reproduction traits with lifetime traits through two different methods (Frequentist and Bayesian) for comparative purpose.Animal breeding data of Tharparkar cattle (n = 964) collected from Livestock farm unit of ICAR-NDRI Karnal for the period 1990 through 2019 were analyzed using a Frequentist least squares maximum likelihood method  and a multi-trait Bayesian-Gibbs sampler approach (MTGSAM) for genetic correlations estimation of all the traits. Estimated breeding values of sires was obtained by BLUP and Bayesian analysis for the production traits.Heritability estimates of most of the traits were medium to high with the LSML (0.20±0.44 to 0.49±0.71) and Bayesian approach (0.24±0.009 to 0.61±0.017), respectively. However, more reliable estimates were obtained using the Bayesian technique. A higher heritability estimate was obtained for AFC (0.61±0.017) followed by first lactation fat yield, first lactation solid-not fat yield, FSP, first lactation milk yield (FLMY), PL ; while a lower estimate for HL (0.38±0.034) by MTGSAM approach. Genetic and phenotypic correlations were negative for AFC-PL, AFC-HL, FSP-PL, and FSP-HL  by the multi-trait Bayesian analysis.Breed and traits of economic importance are important for selection decisions to ensure genetic gain in cattle breeding programs. Favourable genetic and phenotypic correlations of AFC with production and lifetime traits compared to that of FSP indicated better scope of AFC for indirect selection of life-time traits at an early age. This also indicated that the present Tharparkar cattle herd had sufficient genetic diversity through the selection of AFC for the improvement of first lactation production and lifetime traits. Tharparkar, the lyre-horned dual-purpose zebu cattle with its origin in the Tharparkar district of the Sindh province, Pakistan is currently one of the most well-established cattle in Indian and the world. The breed is more famed as a dairy animal owing to its desirable attributes of high production capacity and climate resilience under hot arid unfavorable environmental conditions . The breEconomic dairy production rests upon superior production and efficient reproduction of the herd along with sufficient diversity. The information on genetic parameters like heritability, repeatability and genetic correlation is a prerequisite for making efficient selection strategies. Desirable selection in livestock can be done by either prediction equations or by considering association studies of early expressed traits viz. age at first calving (AFC), first service period (FSP), first dry period, first calving interval which affects production in long term viz. First lactation milk yield (FLMY), peak milk yield, lactation length (LL), dry period, herd life (HL), productive life (PL), breeding efficiency, and lifetime milk yield (LTMY). The economics of these dairy traits are directly affected by reproduction and herd survival as significant association was observed in different cattle and buffalo breeds –6. A comMost of the economic traits in dairy animals are quantitative in nature and follows non-Gaussian distribution with continuous values having wide range of variability determined by the genetic makeup of the individuals and the environment in which they are reared. As such maximal production is achieved when animals are sound in early expressed traits of the fertility cycle. Consequently, reproductive management enables improvement in herd performance in terms of production  and functional  traits. Progress in future fertility and long-term survival of high-producing cows aids in balanced selection and culling decision-making systems . Hence, Sample size is a prime consideration in conducting any statistical analysis. The Bayesian approach has no such prior assumption about sample size as it assures higher coverage, more powerful estimates and robust results even in small sample populations . It was Efficient selection strategies used by breeders are based on variability of data distribution for getting improvement in desired characters. The application of advance methods of genetic analysis provides a crucial tool for improving the production traits in indigenous milch breeds and helps economize the herd productivity. Quantitative analysis for viable traits of these economically important breeds is a complex phenomenon owing to continuous scale on phenotypic as well as genotypic line . The likIt is important to know the effectiveness of Bayesian approach vis-a-vis other methods for genetic evaluation and ranking of sires based on the 1st lactation traits. To our knowledge, no studies have been carried out in the Tharparkar cattle for comparative genetic evaluation using the frequentist LSML approach and more advanced Bayesian multi-trait Gibbs sampling animal model (MTGSAM). This comparison between these two methods  to check their efficacy with a finite sample size of this Indigenous milk breed was one of the purposes of the present study. Bivariate analysis for the estimation of genetic parameters has not been attempted with heterogeneous contemporary groups of affecting factors for AFC and FSP in the Tharparkar breed. Therefore, present study was planned to obtain comparative analytical evidence using Frequentist and Bayesian approaches while considering reproduction, production and lifetime traits as selection criteria and to estimate the breeding values of sires for milk production and composition traits in Tharparkar cattle.viz. LL less than average days (<100 days), milk yield less than average yield (<500 kg) and fat percent less than standard (<3.5%) were expelled for final analyzable samples in our study. Non-genetic factors such as period, season of calving/birth and AFC as covariate were classified into different fixed sub-classes and animal/sire was considered as random genetic factor to assess the effect on the considered traits in the present study. The year of calving/birth had been classified into four seasons e.g. summer (April to June), rainy (July to September), autumn (October to November) and winter (December to March) based on recorded and analyzed meteorological factors at CSSRI, Karnal, India for temperature fluctuations, relative humidity, prevalent geo-climatic conditions. The region is at an altitude of 235 to 252 meters from the sea level, latitude 29.43°N and longitude 77.2°E with 10°C to 45°C temperature range.All the reproduction, milk parameters (production and composition) and lifetime traits were generated from history-cum-pedigree sheet and production records of 964 Tharparkar cows over a period of 30 years (1990 through 2019), maintained at ICAR-NDRI Karnal, Haryana. Data optimization was performed by normalization and standardization methods after compilation, editing and discarding the suboptimal records. Outlier animals from the standardizing criteria Whole data had been classified into six and seven periods by taking five years in a group based on the year of calving (1990 through 2019) and year of birth (1986 through 2019), respectively. AFC was taken as covariate for all traits except for AFC as a continuous trait. Covariate AFC has been classified in eleven classes based on formula given by Sturges [Where, N = No. of observations; range = maximum – minimumReproduction traits include calving and service parameters which affect per animal production and survival in herd. Production traits include traits of economic importance such as milk production and composition traits which have direct effect on profitability of dairy sector as well as individual farmer’s income and livelihood. Lifetime traits include PL span of animals which reflect survival and longevity in herd.Reproduction traits:Age at first calvingFirst service periodProduction traits:First lactation 305 days / Less milk yield (FL305MY)Solid-not fat yieldFat yieldLifetime traits:Lifetime milk yieldProductive lifeHerd lifeDescriptive statistics of considered quantitative traits were estimated by using R software (version 4.2.0) . Data haThe effects of genetic (sire) and non-genetic factors  were assessed by LSML method .The (co)variance component and genetic parameters viz. correlations and heritability estimates were calculated in terms of posterior densities by using multiple iterative cycles of Markov chains for GS. This Bayes theorem has given joint probability of priori and likelihood values from data distribution. More uniform environmental conditions increase the heritability and vice-versa. Data were subjected to analysis by using software packages among the list of BLUPF90 family. RENUMF90 software was a preliminary in this series which generated a renumbered file by removing alpha-numeric values. Standardized (Gaussian distribution) Gibbs samples from GIBBS2F90 software gives binary results. POSTGIBBSF90 package was used to read these binary results and present them in a readable format. Breeding Values were estimated by using BLUPF90 software. Results were based on various standardizing criteria such as Monte Carlo error (MCE) for standard deviation (SD) for accuracy, constant trace plot and Histogram. Proper standardization of Gibbs sampler can be ensured by visualization of stable and normalized marginal posteriori . The finWhere,Y = (n×1) Vector of observed dependent variables, viz. reproduction, production and lifetime traitsX = Incidence matrix (vector of 1’s) relating the fixed effects to the individualsβ = (p×1) Vector of fixed effects Z = Incidence matrix relating the random effects to each individualμ = (q×1) Vector of random effects 2e)e = Random error, normally distributed Pj = Effect of jth season of calving Snk(cov) = Effect of kth age group in first calving (k = 1 to 7), AFC will be taken as covariateAgl = Effect of lth sire (l = 8)Sijklm = Random error, N eFor AFC:Where,ijkl = Observation of lth animal calved in ith period, jth season, kth sireYμ = Overall meani = Effect of ith period of birth (i = 1 to 7)Pj = Effect of jth season of birth Snk = Effect of kth sireSijkl = Random error, N eThe Gibbs sampler was used to generate random samples from Bayes theorem with conditional joint probability by successive sampling resulted in marginal posterior distribution. Estimates generated in several standardized parameters such as iterative, burn in and thinning cycles were compared to get the representative samples for the desired convergence. First non-converging samples were discarded as burn-in. Burn-in and cycles of thinning intervals in each sampling round generate final effective samples for considered traits. MTGSAM approach was considered to analyze genetic and phenotypic correlation between all reproduction, production and lifetime traits in Tharparkar. For this a total of 2,900 Gibbs samples were finalized to get standardized sampling parameters after various trials while each 10th cycles to be stored after discarding initial 1,000 cycles from overall sample chain of 30,000 for all considered traits in Tharparkar.The descriptive statistics viz. mean±standard error (SE), coefficient of variation, SD, and minimum, maximum ranges was estimated to generate a summary of the present data set . These iLeast-square Means and effect of various genetic and non-genetic factors were estimated by the LSML (Harvey ) approacThe results for variance component and heritability estimates obtained through GS are presented in Higher variances and variability was noticed for LTMY among all considered economic traits in the present work . HighestHigher phenotypic variance was noticed for all considered traits among sources of variances for Tharparkar cattle . The totVariance and variability components were higher for production traits as compared to reproduction traits in present study . Higher Marginal posterior mean for variance component and heritability are more informative. Heritability estimates were found as 0.61±0.017 and 0.57±0.024 for AFC and FSP; 0.57± 0.020, 0.60±0.013, and 0.60±0.006 for FLMY, first lactation solid-not fat yield and first Lactation Fat yield; 0.24±0.009, 0.42±0.025, and 0.38±0.034 for LTMY, PL, and HL respectively  and 4.Highest phenotypic, genetic, and environmental variance and variability was noticed for LTMY, while Lowest for FSP from our study . The creThe number of effective samples varied from 50 to 233, 35 to 571 and 19.5 to 783 for reproduction, production and lifetime traits respectively . SufficiGeweke diagnostic values were ranging between −0.30 to 0.30 for the traits under consideration in the present study  in Tharparkar cattle . Geweke’Data visualization is another advantageous feature of Bayesian approach which enables accuracy, automation, and cross validation of statistical analysis. Statistically analyzed genetic parameters were visualized and validated by trace plot and histogram of the heritability estimate , and 3.The heritability estimates using the LSML and Bayesian approach were summarized to compare accuracy . The herHeritability estimates were higher ranging between 0.24±0.009 to 0.61±0.017 by the MTGSAM analysis  from Bayesian analysis . These rTo ensure the dissemination of superior germplasm from elite bulls estimated breeding values (EBVs) were calculated for sires by using both approaches; the Frequentist least squares method and Bayesian application using animal model. Sires having more than three progenies were considered in this study. The EBVs of Tharparkar sires for production traits were interpreted for effectiveness of sire evaluation .AFC of the Tharparkar cattle in our study was less, indicating the animals of ICAR-NDRI herd had a better reproductive potential, compared to AFC estimates of Tharparkar cattle reported earlier 1,876.17±40.66 , 1,821±3In the present study, more informativeness of the posterior estimates from the mixed model multi-trait Bayesian analysis technique explains the 95% HPD, SD, and convergence criteria in the Tharparkar  and 6. OModerate to higher genetic variance as compared to environmental variance for AFC and FSP indicated lesser environmental effect, therefore, having more scope for direct selection in this Tharparkar herd for improving the reproductive potential. This can be stabilized towards the desired direction as lesser AFC and FSP are profitable for the herd. There is also further scope of selection and improvement for production traits as well due to higher variability as compared to reproduction traits. A more disperse pattern of variables for FLMY signified a less stabilized population, indicating further scope for selection and genetic improvement with this traits, while lesser scope for selection for the FLSNFY due to lesser variance and variability. LTMY in the Tharparkar cattle had a higher selection scope due to wide variability. Among the lifetime traits, the HL had a more diverse range in genetic selection which indicated further scope for directional selection in this Tharparkar herd. Even if the sample size was small, higher estimates of the posterior distribution in this study were obtained, implying further scope of genetic improvement in Tharparkar cattle.Heritability is a dynamic trait as it varies from population to population and from environment to environment. Heritability value of various traits was estimated and visualized as graphs , and 3 iHeritability estimates for the reproduction and production traits were low to moderate by Bayesian approach  in the TConvergence diagnostics for Gibbs samplers-MCMC was indicated with the graphical plotting , and 3 aDifferent heritability estimates can be explained by flexibility and cross validation using graphs in the Bayesian approach; however, no such freedom/tool is available in the LSML approach. Interaction of traits is an important factor in the multi-trait analysis. Moderate to high heritability estimate in a narrow range by multi trait analysis explain direct or indirect influence for considered traits. In general, correlation or regression estimate is more precise if the relatives have close relationship and less precise with small sample size .i.e. MCE  it did not agree in the Tharparkar cattle [Heritability of AFC estimated as 0.52±0.07 was found in close agreement with a previous study, while for FSP  of both these reproduction traits are undesirable for economic production of any herd. Indirect selection was preferable from AFC in dairy cattle for linear type traits ,43. The Genetic and phenotypic correlations between various traits estimated through the LSML approach in the Tharparkar cattle didn’t show any definite patterns . NegativOur result indicated that the LTMY did not converge to the desired distribution in the multi-trait Bayesian analysis due to its very higher variability. High variability of the LTMY trait was not suitable for the normalization and to achieve positive definite matrix with considered economic traits for parameter estimation. First lactation production status reflected PL in the Tharparkar cattle to some extent; however, the effect of other factors and their interaction effects could also play significant role in the subsequent lactations in different parities. This indicated that the combined study of first lactation traits with the lifetime traits were required to define the genetic potential of the Tharparkar herd. Indirect selection could result in the improvement of these traits based on the correlation components. Our result revealed little scope of improvement in the lifetime traits due to their low phenotypic and genetic correlations ; high to moderate scope of improvement of production with the reproduction traits due to medium correlations among these traits ; while higher scope genetic improvement in the production and lifetime traits due to high and positive genetic and phenotypic correlations among these traits . Our study also revealed high correlations for FLFY and FLSNFY by trivariate analysis, indicating short term improvement by indirect selection, as these traits appeared simultaneously in animal’s life span and affected by common non-genetic and genetic factors. Better opportunity for improvement can be obtained by indirect selection with the PL and HL as genetic and phenotypic correlations were higher.All the reproduction, production and the lifetime traits were positively correlated, except the AFC and FSP which were negatively correlated with the PL and HL which was favourable. AFC and FSP were found as most promising traits for the indirect selection with a long-term goal based on their favourable genetic and phenotypic correlations in the Tharparkar cattle. High genetic improvement could be expected from the indirect selection of these traits in the ongoing breeding program for Tharparkar herd. The negative genetic correlations indicated that the lower estimates of the AFC and FSP were in the desirable directions for the economic benefit of the herd by ensuring higher PL and HL. Our results pointed towards the scope for long term improvement in the production and lifetime traits based on indirect selection of the AFC due to their favourable genetic and phenotypic correlations in the Tharparkar cattle.The Tharparkar sires were evaluated based on the EBVs generated by both the approaches, using the rank correlation which was intended for calculating the t-values . These cThe goals of decision-making in subsidized farms are to lower the costs of rearing and save time, which can be achieved by an accurate and precise estimate of genetic parameters with a simple analytic process. Considering such objectives, the results of the present study were important to increase profitability and genetic potential in dairy cattle reproduction, and ultimately lifetime productivity. Distributions provided by the Bayesian analysis have great flexibility in cross checking, visualization, and automation of statistical analyses from marginal posterior distributions which was not possible with the conventional methods of analysis. BLUPF90 is a user-friendly tool to compute efficient solutions for genetic parameters even with a small dataset. The moderate to higher value of heritability estimate for the considered traits indicated sufficient genetic base for efficient selection in the Tharparkar cattle. Selection of productive animals based on milk constituents (FLFY and FLSNFY) is recommended for indirect selection with short-term goals in Tharparkar. Correlations studies enhance the scope of indirect selection at an early age by AFC and FSP for production potential in long-term breeding programs of the Tharparkar herd. Our results revealed that the AFC had a favourable negative genetic correlation with the production and lifetime traits, which indicated that the AFC will be an important trait for indirect selection of these production traits, since a reduction in AFC will be beneficial for improving first lactation and lifetime traits in the Tharparkar cattle. Distinct ranking and efficient evaluation of the Tharparkar sires based on the Bayesian approach, compared to that of the frequentist LSML technique, suggested the multi-trait Bayesian analysis to be the method of choice for the reliable genetic assessment of the Tharparkar cattle.No animal were harmed and/or not fed anything for experimental purpose other than regular daily allowances, during the presented research work as previously collected information for considered traits were utilized with permission from the livestock record unit, ICAR-NDRI, Karnal, Haryana, India. All animal welfare rights were reserved while conducting this study.


G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction.Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP).We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size  improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel.Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.The online version contains supplementary material available at 10.1186/s12711-023-00823-0. Ne, where Ne is the effective population size. Given a species with a genome length equal to L Morgans, the number of independent chromosome segments (Me) segregating in a population can be calculated as 4NeL.Several factors influence the statistical power required to identify causative variants in genome-wide associations (GWA), including the number of quantitative trait nucleotides (QTN) that affect a trait, the number of single nucleotide polymorphisms (SNPs) in the discovery panel, the number of genotyped individuals , and theNe than human populations, which means smaller Me. Pocrnic et al. [Me. The same authors related the limited dimensionality to Me = 4NeL and observed that this quantity corresponds to the number of largest eigenvalues (EIG) explaining 98% (EIG98) of the variance of the genomic relationship matrix  required three times more genotyped animals with phenotypes than in populations with large Ne . However, not all simulated QTN were identified, regardless of the Ne or the amount of data. Distinguishing between noise and the true signal is more difficult in small populations because of longer chromosome segments and the uncertainty about the exact location of QTN. In addition, the level of noise may mask the signal and thus prevent the detection of associations. With sequence data, a high GWA resolution may be even harder to achieve for small populations due to the reasons mentioned above.Misztal et al.  investigNe, Me, and GWA resolution are unknown. Understanding the appropriate sample size for variant discovery, especially with sequence data, can help alleviate the economic and computational costs for practical applications. In addition, it could help manage resources when working with traits that are difficult and costly to record. Based on the limited dimensionality of genomic information, there may be an optimal number of animals that carry all the independent chromosome segments segregating in the population, and consequently, all the genomic information available in the population [Me animals with high reliability GEBV are used. Therefore, we hypothesize that the ability to identify causative variants is high when animals with high-reliability GEBV are used, and the sample size for GWA approaches Me. Thus, using a larger sample size may not improve further the GWA resolution or increase the proportion of variance explained by significant QTN. Here, we used the number of eigenvalues explaining different proportions of the variance in Ne, varying numbers of QTN, and varying amounts of information for genotyped individuals. We also evaluated the impact of incorporating pre-selected variants, from GWA with different sample sizes based on dimensionality, to a 50k SNP chip for genomic prediction using single-step genomic best linear unbiased prediction (ssGBLUP).Although it is well known that increasing the sample size for GWA improves the resolution, to date, the links between the number of genotyped individuals, pulation . When anpulation  raises tTo mimic the bovine genome, we simulated 29 chromosomes with a total length of 23.19 Morgan using the QMSim software . All simNe equal to 20 (Ne20) or 200 (Ne200) were simulated by changing the number of breeding males from 5 to 50 but keeping the number of females at 15,000. The founders of the recent populations came from generation 0 of the historical population. Twenty generations of random mating were carried out, considering a replacement rate of 80% for sires and 30% for dams. Animals were randomly selected and culled based on age. In total, 315,005 and 315,050 animals were generated in the recent population for Ne20 and Ne200, respectively. However, only animals from generations 11 to 20 had phenotypic and pedigree information that was used for the current study. Of those, 75,000 animals from generations 16 to 20 were genotyped . Each phenotype was the sum of an overall mean equal to 1.0, the true breeding value (TBV), and a random residual effect. Each simulated scenario represented a different combination of Ne, number of QTN, and heritability, and was replicated five times.A quantitative trait was simulated with a heritability of 0.3, 0.9, or 0.99 by setting the additive genetic variance to 0.3, 0.9, or 0.99, respectively, and keeping the phenotypic variance constant at 1.0. The different heritabilities mimicked limited  or extensive  phenotypic information . The hisLimited or extensive phenotypic information for simulated animals was mimicked by changing the trait heritability. Based on a single record per animal, the reliability of the EBV equals the heritability . If animBefore the GWA analyses, all genotyped animals were separated into three non-overlapping datasets: discovery, training, and test. The test set was composed of genotyped animals from the last generation , and the remaining genotyped animals  were randomly assigned to the discovery and training sets . The discovery sets were generated for GWA, and the training and test sets for genomic prediction. To test the possible bias in genomic prediction due to using the same dataset for discovery and training, two different schemes were designed: (1) discovery = training: genotyped animals used for discovery were also used for training, and (2) discovery x) was equivalent to the number of largest eigenvalues explaining x percent of the variance in x was assumed to have the values 50, 60, 70, 80, 90, 95, 98, or 99. For example, the average number of largest eigenvalues explaining 50% of the variance in Ne200 Q2000 H30 scenario  of the variance in x percent. All the simulated genotyped animals  were used in the computations, and Different scenarios based on the dimensionality of genomic information were used for discovery (GWA) and training (genomic prediction) to explore the impact of varying sample sizes on GWA and genomic prediction. The number of genotyped animals in each discovery and training set , without considering statistical significance. For Q200, v was equal to 10, 50, 100, 200, or 400, whereas, for Q2000, it was equal to 10, 100, 500, 1000, 2000, or 4000. We considered one additional scenario by selecting only the significant variants based on the statistical significance using Bonferroni corrected p-values (SIG).Different numbers of variants were selected from the GWA and were included in the 50k SNP panel for genomic prediction. Each ‘QTN’ scenario had a specific number of selected variants based on the order of the p-values  of TBV on GEBV was used as an indicator of inflation  or deflation  of GEBV.In each scenario, prediction accuracy was calculated as the correlation between TBV and GEBV. The regression coefficient  than for a more polygenic trait (Q2000).Compared to an ze Figs.  and 4. ANe. First, as the sample size increased, the proportion of variance explained by the identified QTN increased regardless of the heritability, Ne, and the number of QTN. Second, when the proportion of variance explained was analyzed according to heritability, high heritability scenarios were more efficient to identify QTN. For example, when all genotyped animals  were used in the scenario Ne20 Q200 H30, it was possible to identify QTN explaining 50.1% of the variance , EIG98, EIG99, and ALL captured QTN explaining 54.3, 61.6, and 67.6%, respectively  from five replicates are shown in Fig. x as a benchmark are in Additional file Ne20 Q200 H90 and Ne20 Q200 H99, 3626 and 1622 genotyped animals (SS_pol) are required to achieve the same level of genetic variance explained (50.1%) by scenario Ne20 Q200 H30 with ALL. The scenarios Ne20 Q2000 H90 and Ne20 Q2000 H99 required 3652 and 1599 genotyped animals (SS_pol) to identify QTN that explained 3.5% of the variance, which is equivalent to the level achieved in scenario Ne20 Q2000 H30 with ALL. We observed a similar pattern when Ne was 200. A remarkable difference between results from Ne20 and Ne200 was that the sample sizes required to achieve an equivalent level of variance explained by the QTN using ALL in H30 were comparable to the range of EIG90 to EIG98 in Ne20, but EIG80 to EIG90 in Ne200 when considering H90 and H99 . Therefore, the genomic prediction analyses were performed with training animals that were different from those in the discovery set.1), respectively, with their corresponding standard errors. Those accuracies and b1 were calculated as the average of all genotyped scenarios: 50k, TOP10, TOP50, TOP100, TOP200, TOP400, and ‘SIG’ for the Q200 and 50k, TOP10, TOP100, TOP500, TOP1000, TOP2000, TOP4000, and ‘SIG’ for the Q2000 scenarios. Similar to Figs. Ne of 20 and 200. Our results for Ne200 showed that as the size of the training set increased from EIG50 to EIG90, prediction accuracy also increased, as demonstrated in Fig. Ne200 Q200 H30, the prediction accuracy increased by 0.08, from 0.64 to 0.72. A similar pattern was observed in Ne200 Q2000 H30, where a gain of 0.07 (0.64 to 0.71) was achieved when the training set was upgraded from EIG50 to EIG90. However, in the same scenarios with Ne20, the gain was only about 0.02, at most. Generally, Ne20 showed greater prediction accuracy than Ne200. For example, when the smallest sample size (EIG50) was used for Ne20 Q2000 H30, H90, and H99 scenarios, prediction accuracies were 0.77, 0.85, and 0.83, respectively. Conversely, the same scenarios with Ne200 showed lower prediction accuracies of 0.64, 0.79, and 0.82, respectively. However, this difference became smaller with the largest sample size (ALL). Similar patterns were observed for the Q200 scenarios. Prediction accuracies were highly influenced by the heritability of the trait, particularly in populations with a larger effective size (Ne200). For instance, when EIG50 was used for Ne200 Q200 H30, H90, and H99 scenarios, the prediction accuracies were 0.64, 0.82, and 0.81, respectively. Even with the largest training set (ALL), prediction accuracies were 0.85, 0.97, and 0.99, following the same order. Similar trends were also observed for Ne200 Q2000 scenarios. PBLUP always showed lower prediction accuracies than the other scenarios with ssGBLUP.Figures 1) are presented in Fig. Ne was 200 than 20. However, using a large training set reduced the inflation for both effective population sizes. When ALL was used for training, all scenarios had b1 values close to 1, except for the Ne20 Q200 H30 scenario. In addition, this scenario exhibited the largest SE between the replicates (~ 0.04) compared to other scenarios. We also found greater inflation of EBV and more variation between the models based on PBLUP with Ne20 compared to Ne200.The regression coefficients (bNe200 (ranging from 3.27 to 8.36%) than with Ne20 (ranging from 0.75 to 1.28%). Moreover, Q200 showed a higher percentage of gain than Q2000 in both Ne scenarios. Notably, the maximum accuracy gain was typically observed when the largest number of top SNPs (TOP400 for Q200 and TOP4000 for Q2000) was added to 50k chip data, which represented twice the number of simulated QTN than by adding SIG. However, in the scenarios Ne20 Q200 H90, Ne20 Q200 H99, Ne200 Q200 H90, and Ne200 Q200 H99, the highest increase in accuracy was observed when using 50k plus SIG. This is probably because identifying significant QTN was easier for a less polygenic trait (Q200) with H90 and H99 but more challenging in Q2000 or for a low heritability trait (H30).Figure Ne, Me, the number of QTN, and the trait heritability . According to our findings, a sample size with the same number of genotyped animals as that of EIG98 was suitable for detecting genetic variants in GWA, particularly in populations characterized by large Ne .In this study, we conducted a thorough investigation of the impact of various sample sizes in GWA, which followed the concept of dimensionality of genomic information, the implications of using different trait heritabilities (to mimic GEBV reliability) in GWA, and the inclusion of preselected variants into a typical 50k SNP panel using ssGBLUP. Our analysis provided valuable insights into how different data structures can affect the performance of GWA and genomic prediction under the ssGBLUP framework. We observed that the concept of limited dimensionality of genomic information  could beThe most common approach for implementing genomic prediction using sequence data involves a three-step workflow. First, significant variants are preselected. Next, these selected variants are incorporated into commercial SNP chip data , or separate genomic matrices are fitted in the model , 22, 23.https://queries.uscdcb.com/Genotype/cur_freq.html), and approximately 1.3 million American Angus cattle  as of April 2023, it is crucial to determine the minimum number of genotyped animals required to detect significant variants. Our findings indicate that a sample size with at least the same number of genotyped animals as that of EIG98 could effectively identify the most informative QTN when Ne is large. In populations with Ne200, using EIG99 or all available genotyped animals only slightly improved the detection of significant QTN beyond using EIG98 for both Q200 and Q2000 scenarios. For example, we observed that the average increase in the proportion of variance explained by identified significant QTN when using ALL  as opposed to EIG98 was 8.1% in populations with Ne200 and 18.9% in populations with Ne20. However, it is important to note that some scenarios had very small variances explained at EIG98, such as 0.3% for Ne20 Q2000 H30 and 8.3% for Ne200 Q2000 H30. While the percentage increase in variance explained may appear large in some scenarios with small variances explained at EIG98, the limited impact on predictions may not justify genotyping many more animals. . This result could be helpful for both small and large genotyped populations with large Ne. In breeding populations with limited resources, the number of animals that can be genotyped or sequenced may be restricted. In such scenarios, determining the required sample size can aid in cost-effective genotyping or sequencing practices. Our study suggests that in populations with large Ne (Ne200), it is not necessary to use all available animals for variant discovery. Instead, a balanced dataset should be constructed for discovery, training, and testing to avoid biases and optimize the ability to detect significant variants. In populations with small Ne (Ne20) and highly polygenic traits (Q2000), most sample sizes were not sufficient to identify any significant QTN until it reached ALL and EIG98 for H30 and H90, respectively. However, the availability of more information on genotyped animals, as seen in H99, allowed the identification of a few QTN with a sample size equivalent to EIG95, indicating the importance of sample size and amount of information for identifying significant signals in livestock species, such as chicken and pigs, which have smaller Ne (32–48) compared to cattle [Ne. Gozalo-Marcilla et al. [https://www.animalgenome.org/QTLdb), their discovery is supported by our findings in populations with small Ne , moderate to high heritability (0.3 to 0.9), and more polygenic traits (number of QTN = 2000) when using 30k genotyped animals for GWA.With the current increase in the number of genotyped animals in many species, such as the 6.4 million U.S. Holsteins ; however, the prediction accuracies were generally smaller for the Ne200 when the same number of genotyped animals was used. Daetwyler et al. [Ne and Me) has a significant impact on the prediction accuracy of GBLUP. Their study revealed that a smaller Ne achieved better accuracy than a larger Ne, regardless of the number of QTL when the same number of individuals were used in the training sets.In general, genomic prediction accuracy in this study improved as training data size increased, and combining selected variants to a 50k SNP panel enhanced the accuracy when the genomic prediction was performed with the suitable training set sizes. However, the magnitude of this improvement was limited, where only a minor (< 1.0%) increase in accuracy was seen when using training sets with the number of genotyped animals equal to EIG50 to EIG70 for ediction –37. In cediction  found nor et al.  demonstrv). We demonstrated that incorporating a large number of variants  in the training set improved prediction accuracy by up to 8%. Previous studies have used selected variants from imputed sequence data to improve genomic predictions in single-breed populations. Veerkamp et al. [The selected variants in our study were above the threshold set by a p-value of 0.05 with the Bonferroni correction for multiple testing in GWA. Although this correction is known to be stringent and potentially leads to an increased number of false negatives, we aimed at testing the predictive power of combining the significant variants with a subset of variants based on sample size  improved the resolution of variant detection. This suggests that a precise detection of the most significant variants is feasible when the number of genotyped animals for discovery is close to the expected number of independent chromosome segments (Me). Using a larger sample size marginally increased the resolution of GWA studies. The genomic information available for such studies is limited in its dimensionality and can be quantified in various ways, such as the number of non-redundant SNPs, genotyped animals, or Me [Our study has shown that when the number of genotyped animals used corresponded at least to the number of eigenvalues explaining 98% of the variation in s, or Me . InvestiAccurate identification of causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. Based on this dimensionality, the expected number of independent chromosome segments contains the additive genetic variance in a population. Consequently, using a GWA sample size larger than the expected number of independent chromosome segments has a limited impact on improving the resolution of the GWA and the identification of QTN. This is particularly relevant for populations with larger effective sizes, where the detection of QTN may be more effective. Therefore, the dimensionality of genomic information can provide valuable insights into selecting the suitable sample size for GWA and aid in identifying the most informative variants. Assigning genotyped animals with high reliability breeding values to the GWA discovery set helps better identify the significant QTN. As sequence data become available, preselecting variants and adding them to the regular chip data could improve prediction accuracy when the dimensionality of the genomic information is considered; however, the improvement is primarily limited.Additional file 1. QMSim parameter files for simul_Ne20_200QTL_h2_0.3.Additional file 2. QMSim parameter files for simul_Ne20_200QTL_h2_0.9.Additional file 3. QMSim parameter files for simul_Ne20_200QTL_h2_0.99.Additional file 4. QMSim parameter files for simul_Ne20_2000QTL_h2_0.3.Additional file 5. QMSim parameter files for simul_Ne20_2000QTL_h2_0.9.Additional file 6. QMSim parameter files for simul_Ne20_2000QTL_h2_0.99.Additional file 7. QMSim parameter files for simul_Ne200_200QTL_h2_0.3.Additional file 8. QMSim parameter files for simul_Ne200_200QTL_h2_0.9.Additional file 9. QMSim parameter files for simul_Ne200_200QTL_h2_0.99.Additional file 10. QMSim parameter files for simul_Ne200_2000QTL_h2_0.3.Additional file 11. QMSim parameter files for simul_Ne200_2000QTL_h2_0.9.Additional file 12. QMSim parameter files for simul_Ne200_2000QTL_h2_0.99.Additional file 13: Figure S1. a–l GWAS results for each scenario from one replicate: (a) Ne20 Q200 H30; (b) Ne20 Q200 H90; (c) Ne20 Q200 H99; (d) Ne20 Q2000 H30; (e) Ne20 Q2000 H90; (f) Ne20 Q2000 H99; (g) Ne200 Q200 H30; (h) Ne200 Q200 H90; (i) Ne200 Q200 H99; (j) Ne200 Q2000 H30; (k) Ne200 Q2000 H90; and (l) Ne200 Q2000 H99.Additional file 14: Tables S1. a–d Number of significantly identified QTN, SNPs and the variance explained by QTN (mean ± SE) of five replicates: (a) Ne20Q200, (b) Ne20Q2000, (c) Ne200Q200, and (d) Ne200Q2000. Tables S2. a–d Estimated sample size using local polynomial regression Additional file 15: Table S3. This table contains 5 sheets with the prediction accuracy and b1 values of each scenario Ne20 Q200, Ne20 Q2000, Ne200 Q200, and Ne200 Q2000, and PBLUP.