This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1–13, 15, 17–20, 24, 26–28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail. Quail production traits are important characteristics in the poultry industry, and quantitative trait loci (QTLs) associated with these traits have been identified3.Japanese quail is a model bird from the Galliformes order that is raised for production and biological research purposes. The Japanese quail holds significant importance as a bird species owing to its compact size, efficient productivity, quick generational turnover, and early sexual maturity, typically around 6 weeks of age4 and provides a link for breeders to select based on genetic information5. Among the molecular markers, single nucleotide polymorphism (SNP) markers are widely used9 due to their abundance in any genome and the cost-efficient identification methods10. Genotyping by sequencing (GBS) is a next-generation sequencing (NGS) platform that opens new possibilities for SNP marker identification and can be used in a simple, highly multiplexed system for constructing reduced representation libraries involving inexpensive barcoding, reduced sample handling, no size fractionation, and which requires fewer polymerase chain reaction and purification steps11. Restriction-site associated DNA sequencing (RAD-seq)14 is a GBS that can identify, verify, and score thousands of SNPs simultaneously, reduce complexity across genomes, deliver high-resolution population genomic data, and is convenient for non-model species at a reasonable cost15. Existing genotyping platforms represent efficient tools for association studies to increase the output of marker-assisted selection (MAS).Molecular markers have become a key prerequisite for association mapping and are of increasing importance in animal breeding and genetics. Association analysis is an advantageous technology that identifies QTLs underlying phenotypic traits. Quail production traits are complex traits that are controlled by QTLs and can be affected by environmental factors. The QTLs related to production traits of Japanese quail have been previously identified. Reference2 identified 22 QTLs for body weight, egg weight, number of eggs laid, and age of the first egg using SNP markers. References17 discovered four QTLs for body weight and nine QTLs for egg-related traits using the RAD-seq method. Moreover, microsatellite markers have been used to detect QTLs for traits associated with body weight traits23, growth and egg production24, and egg-laying curves25. Previous studies have also investigated the QTLs underlying features other than production traits of Japanese quail30. Although molecular breeding helps breeders select the production traits expressed in Japanese quail, association analysis studies in poultry have mostly focused on chickens rather than Japanese quail. In recent years, genome-wide association studies (GWASs) have been widely used in studies of different chicken breeds using a high-density genotyping platform to identify SNPs and candidate genes associated with production traits37. Numerous studies used GWASs to identify SNPs that control the candidate genes responsible for body weight traits of chickens41. Similar studies have been conducted for identifying SNPs associated with chicken egg-related traits44. References48 performed GWASs for body composition and meat quality traits of chickens.Association analysis is a viable approach for linking phenotypes and genotypes in poultry genetics as well as dissecting complex traits. This type of analysis can boost the information obtained from QTL studies through MAS implementation49, prolactin receptor genes and growth traits50, and gut microbial architecture of efficiency traits51. Existing reports on the genetic map of Japanese quail have been integrated and aligned with assembled chicken sequence data52. As Japanese quail shows close phylogenetic relatedness to chickens, having a similar genome length (1.2 × 109 base pairs), chromosome number (2n = 78), and homology of chromosome morphology, a high rate of synteny conservation is expected between the two species53. In addition to studies on the quail genome55, reports on the chicken genome will help breeders investigate genetic analysis and identify SNP markers associated with the targeted traits within the poultry industry. However, to date, there are no published reports on association studies of production traits of Japanese quail using GLM and MLM models with RAD-seq data. Therefore, a study pursuing the identification of SNP markers associated with production traits will provide breeders with a useful tool to assist in selecting high-production lines, specifically in Japanese quail breeding programs and poultry in general. Here, we performed an association analysis and identified SNP markers associated with production traits of Japanese quail using the RAD-seq method. We expect these results to help improve poultry breeding programs and increase production levels through molecular breeding using SNP markers with MAS.Studies of association analysis in Japanese quail are limited and mainly concentrated on the association between polymorphisms of gonadotropin-releasing hormone genes and growth traitshttps://www.scj.go.jp/ja/info/kohyo/pdf/kohyo-20-k16-2e.pdf. Also, we confirm that all methods have been conducted in adherence to the ARRIVE guidelines (https://arriveguidelines.org). Experimental birds were reared at the Research Farm of Hiroshima University, Japan. Large- and normal-sized (LS and NS) Japanese quail strains were selected as parental strains. The LS Japanese quail is known for its high body weight, reaching around 200 g in females and 170 g in males upon maturity, making it suitable for meat production purposes. Conversely, the NS strain of Japanese quail exhibits a normal body weight, approximately 130 g in females and 100 g in males at the age of maturity, and has been primarily used for egg production1. These two strains originate from genetically distinct backgrounds concerning the specific traits of interest, which could potentially aid in accurately identifying targeted SNPs. One hundred quails from each of the two strains, consisting of 50% males and 50% females, were reared as parents. The cross involved an LS male paired with three NS females, and an NS male paired with three LS females, resulting in 100 F1 progeny in a reciprocal cross . Subsequently, a total of 505 F2 birds  were generated from a reciprocal cross between six F1 males and 18 F1 females. Newly hatched chicks were instantly pedigree leg-banded and weighed before being moved to heated brooders, where they were reared until four weeks of age. Thereafter, quail were housed in individual steel wire meshed cages  equipped with feeders and drinkers. Birds were fed ad libitum a standard starter diet (22% crude protein (CP); 2900 kcal metabolizable energy (ME)/kg−1) from 0 to 4 weeks of age and then a grower diet  from 4 to 16 weeks of age. Quail were reared under a 24-h light photoperiod for 4 weeks, followed by a 14 h:10 h light:dark cycle.Animal care, experimental protocols, and blood sample collections were approved and conducted in accordance with the Rules on Experimental Animals and Animal Experiments at Hiroshima University, Graduate School of Integrated Sciences for Life, Laboratory of Animal Breeding and Genetics  and the protocol described in the Guidelines for Proper Conduct of Animal Experiments, Science Council, Japan. 1 and F2 generations were collected from hatching until 16 weeks of age (WK 0–16). A total number of 3990 eggs obtained from 399 females were used to assess egg-related traits. Egg-related traits were measured at two different egg production stages: at the beginning of the egg production stage (first stage) and at 12 weeks of age (second stage). The two stages are indicated with subscript letters ‘1’ and ‘2’ in the abbreviations of traits. The first five eggs from each female were evaluated at each developmental stage. External and internal egg-related traits, including egg weight (EW), egg long axis (ELA), egg short axis (ESA), eggshell strength (ESS), eggshell weight (ESW), egg equator thickness (EET), yolk weight (YW), yolk diameter (YD), yolk colour, lightness  (YC-L*), redness  (YC-a*), yellowness  (YC-b*), albumen weight (AW), age at first egg (AFE), total number of laid eggs from maturation up to 16 weeks of age (TLE), and egg production rate (EPR) were evaluated using the methodology described in our previous publication17. The average data from parental, F1 and F2 generations were subjected to one-way analysis of variance (ANOVA), followed by Tukey’s HSD test using JMP v. 11.0.2 .Weekly body weight data of 805 quail  from the parental, F56, and genomic DNA was extracted from each collected sample using phenol–chloroform and DNeasy Blood & Tissue Kits  according to the manufacturer's protocol. DNA was quantified using a Qubit 3.0 assay fluorometer . DNA concentrations were adjusted to 20 ng/µL to ensure consistent measurements and were subsequently used for library preparation. Birds used for matings along with 505 F2 quail were used for RAD-seq. RAD libraries were prepared according to the method described by Ref.57. A 50-bp single-end adapter using EcoRI and BglII enzymes was performed on an Illumina HiSeq 2500  for RAD library sequencing. RAD-seq read data were deposited in the DDBJ Sequence Read Archive (accession no. DRA011153) https://ddbj.nig.ac.jp/search. The RAD-seq reads were trimmed using the TrimGalore program , and the trimmed reads were mapped onto the Japanese quail genome (GCA_001577835.1 Coturnix japonica 2.—NCBI) using Bowtie2 with default settings58. Further, the reads were converted to binary sequence alignment/map format (BAM) files using SAMtools59. Variant detection was initially performed for the F1 generation. The BAM files of F1 lines were processed using the SAMtools mpileup and varscan2 mpileup2cns60 with default parameters and then changed to min-coverage 5. The variant call format (vcf) files were merged with bcftools61, and the merged file was further screened using vcftools62 with the following parameters: minDP 5, min-meanDP 5, maxDP 100, min-alleles 2, max-alleles 2, and max-missing 1. The screened sites that were heterozygous for all F1 populations are summarised in the position list. Within the position list, polymorphisms of all samples, including birds from P1, P2, F1, and F2, were named using the SAMtools mpileup and varscan with the aforementioned parameters and merged using bcftools. After the polymorphism detection steps, only the GT fields were exported using vcftools and used for further analyses.Blood samples from all birds were collected using the method described by63 implemented by TASSEL v. 564. GLM evaluation incorporated population structure ) and MLM used both population structure (PCA) and kinship matrix in the association analysis. The PCA and covariance analysis were performed using the method reported by Ref.65 to describe the population structure. A kinship matrix was produced using the Kinship tool with the Scald_IBS method to avoid spurious associations due to relatedness and population structure. In the "Scald_IBS" method, genotypes were encoded based on the count of the alleles at that locus. By utilizing this encoded data, the kinship matrix was estimated. The efficient mixed-model association (EMMA) algorithm66 and population parameter previously determined (P3D) variance component estimation were implemented to decrease the computing time for a large dataset67. Statistically significant associations with p-values < Pthreshold = 0.01/N, where N is the total number of SNP markers, were identified, and a standard correction was performed by applying a Bonferroni procedure at a minimum value of 3.88806E−36 for GLM and 7.03385E−22 for MLM68. The coefficient of determination (R2) was subsequently calculated to quantify the extent to which genetic markers explained the phenotypic variance. The significant value and the marker effect for each SNP were exported, and Manhattan as well as quantile–quantile (QQ) plots were generated in the R Project for Statistical Analysis v. 3.6.169 using the qqman package70. The data were subjected to a correction procedure to account for the influence of fixed effects, specifically birth date, sex, and dame. This adjustment was performed using the least squares method in R-Project for Statistical Analysis v. 3.6.169.Association analysis was performed using general linear model (GLM) and mixed linear model (MLM) approacheshttps://www.scj.go.jp/ja/info/kohyo/pdf/kohyo-20-k16-2e.pdf. Also, we confirm that all methods have been conducted in adherence to the ARRIVE guidelines (https://arriveguidelines.org).Animal care, experimental protocols, and blood sample collections were approved and conducted in accordance with the Rules on Experimental Animals and Animal Experiments at Hiroshima University, Graduate School of Integrated Sciences for Life, Laboratory of Animal Breeding and Genetics  and the protocol described in the Guidelines for Proper Conduct of Animal Experiments, Science Council, Japan 1 and F2 generations are shown in Tables 1 (LS♀ ✕ NS♂) quail showed the highest body weight at hatching . The lowest body weight was detected in NS quail from hatching to 16 weeks of age. No difference was found between F2 generations in WK 0–2. From 4 to 16 weeks of age, the LS strain presented the highest body weight compared to that of the other groups. In males, the LS F1 (LS♀ ✕ NS♂) quail showed the highest body weight at birth. However, no difference was observed between the weights of the other quail groups, except for NS. Moreover, no significant differences were observed between the F1 generations in WK 1–16. The NS quail showed the lowest body weight during the entire period analysed. The LS strain exhibited the highest body weight from 4 to 16 weeks of age compared to those observed at all generations.Filial generations obtained from reciprocal matings are presented separately. The body weights of female and male birds in the LS and NS strains and their F1 and F2 hybrids in the first and second egg-laying stages. The LS strain EW, ESA, ESW, and AW exhibited the highest value among all groups in both egg-laying stages. NS females showed the highest values for the YC-a*1 and YC-b*2 traits across generations. F2 (LS♂ ✕ NS♀) females showed the lowest value in YC-a*1 (2.64) compared to the those observed in the other groups. No difference was observed in EET1 between the parental and filial generations. No significant differences were observed in ELA1 between NS and both filial generation females. Both F2 groups presented the lowest ESS1,2 among all groups, whereas no differences were observed between the parental and F1 quail in the first and second stage of egg production. F2 (LS♂ ✕ NS♀) birds started egg laying at a later age (49.01 days) than the other groups. F1 birds laid more eggs than the parental and F2 generations. Moreover, egg production in F2 was lower than that in the parental generation.Table p-values lower than the threshold were excluded, and 383 SNPs for 1,151 associations  detected by the associations ranged between 0.003 and 0.097 using the GLM approach. Figures 2, respectively, wherein 14 associations were found on chromosomes 1, 2, 4, 5, 11, and Z. The Manhattan and QQ plots for the remaining traits from the GLM are shown in Supplementary File Illumina HiSeq 2500 produced 733,233,057 RAD-seq reads for the treated samples. A total of 25,631 SNPs that passed the quality control were used for the GLM and MLM analyses. This subset of SNP genotypes was used to produce a kinship matrix for MLM analysis. The same subset of SNP markers was also used to perform the PCA in this study. General and mixed linear models produced 1,941 and 2,986 associations, respectively, after applying statistically significant loci with Bonferroni corrections. Statistically significant associations with 2. The maximum number of associations was detected on the Z chromosome (N = 516), followed by chromosomes 1 (N = 208), 3 (N = 105), and 2 (N = 96), while the minimum number of associations was observed on chromosome 25 (N = 1) for YC-L*2 and chromosome 28 (N = 1) for egg production rate. However, a maximum number of 149, 97, 76, and 69 SNPs were distributed on chromosomes 1, Z, 2, and 3, respectively. Following MLM and Kinship analyses, 739 and 703 associations were observed for body weight and egg-related traits, respectively. No SNP markers on chromosomes 11–12, 19–23, 25, and 28 were associated with body weight. The percentage of phenotypic variance detected by the associations ranged between 0.0004 and 0.1401 in the MLM analysis. The Manhattan and QQ plots for WK 1 are shown in Figs. As shown in Table 1, ELA2, ESW1, EET1, YC-b*2, AW2 in both models, and EW2, ESA1,2, YD2, YC-L*1, YC-a*1,2, AW1, TLE, and EPR based on the GLM. In the GLM, 18 SNP markers were identified on chromosome 1 for ELA1 (N = 1), ESW2 (N = 4), EET2 (N = 1), YW1,2 , YD1 (N = 1); chromosome 2 for ESS2 (N = 2); chromosome 4 for AFE (N = 1); chromosome 5 for ESS2 (N = 1); chromosome 9 for ESW2 (N = 2); chromosome 12 for ESS1 (N = 1); chromosome 19 for YC-L*2 (N = 1); and the Z chromosome for YC-L*1 (N = 1). Alternatively, in the MLM, 45 SNPs were observed on chromosome 1 for EW2 (N = 3), ESW2 (N = 5), and YW1,2 ; chromosome 2 for ESS2 (N = 2) and TLE (N = 1); chromosome 3 for TLE (N = 3) and EPR (N = 3); chromosome 5 for ELA1 (N = 1) and ESS2 (N = 1); chromosome 7 for YC-L*1 (N = 2); chromosome 8 for AW1 (N = 1); chromosome 9 for EW2 (N = 2), ESA2 (N = 1), ESW2 (N = 1), EET2 (N = 2), YW2 (N = 1), and YC-a*1 (N = 1); chromosome 12 for ESA1 (N = 1); chromosome 15 for YC-b*1 (N = 1); chromosome 19 for AFE (N = 1); chromosome 27 for TLE (N = 1) and EPR (N = 1); and Z chromosome for ELA1 (N = 3), ESA1 (N = 1), YC-a*2 (N = 1), and TLE (N = 3). Among the detected SNPs, 66 markers were associated with the same traits in both models across all chromosomes.The combination of association markers detected in both the GLM and MLM analyses is explained in Table 2 revealed 383 SNPs with 1151 associations and 734 SNPs with 1442 associations for GLM and MLM approaches, respectively. Our identified SNPs were associated with all targeted traits except EW1, AW1, and YC-b*2 in the GLM and ELA2 in the MLM analysis. To the best of our knowledge, no such association analysis of production traits in Japanese quail have been reported using GLM and MLM approaches with RAD-seq data. However, several studies on QTL analysis of production traits in Japanese quail have been reported. Reference20 recognised QTLs underlying body weight at hatching and at four weeks of age on chromosome 1 at 12–13 cM. Here, we identified associations on the same chromosome at 11.9 cM for GLM and 12.38 cM for MLM corresponding for WK 0 and TLE traits, respectively. Reference21 reported QTL associated with body weight from hatching to three weeks of age on chromosome 3 between 27 and 30 cM. The results of the present study revealed trait associations for SNPs located between 28.73 and 37.74 cM on chromosome 3 in both GLM and MLM analyses. Moreover, using GLM and MLM approaches, we showed that associations underlying body weight traits were mostly located on chromosomes 1–10 and Z. Associations affecting hatching weight were identified on chromosomes 1–9, 13, 17, 27, and Z for GLM, and 1, 3, 7, 9, 13, and 27 for MLM approaches. Detected SNPs located at 20.16 cM on chromosome 7 were associated with hatching weight in both GLM and MLM analyses, and these results were consistent with the previously identified location of QTLs associated with this trait on chromosome 5 (19–20 cM)22. The detected QTL affecting liveweight measurement at week 5 has been shown to be located on chromosome 1 in a study investigating the genetic mapping of QTLs affecting body weight18, which is in agreement with the SNPs associated with body weight and located on chromosome 1 in our GLM and MLM analyses. Reference3 identified a QTL for body weight at 4 weeks of age at the centromere of chromosome 2 which is consistent with the locus detected for this trait on chromosome 2 in both association analyses performed in the present study. The study detected three QTLs associated with body weight at 1, 4, and 6 weeks of age, located in the initial region of chromosome 2 between 0 and 15 cM. Reference2 reported putative pleiotropic loci on chromosome 3 (52.6–56.7 Mb) affecting both weight and egg traits, where a QTL for egg weight co-localised with a QTL for body weight at 65 days of age. Interestingly, our results indicated an association in the same chromosome (56.08 Mb) controlling WK 1 in both the GLM and MLM analyses. This candidate region could underlie the genetic correlation already observed in quail between body weight and egg traits73. QTLs for early and late growth stages (5 and 70 weeks of age) have been previously detected on chromosome 1, positioned 18–19 cM24. In our MLM analysis, YW associations were identified on chromosome 1 between 17.68 and 21.16 cM. It is likely that the QTLs for BW and YW located on chromosome 1 represent a single gene. In addition to QTLs for body weight traits, Ref.24 identified QTLs for eggshell weight on chromosomes 1 (191 cM), 5 (12 cM), and 20 (21 cM); egg weight, egg number until the age of 69 weeks, and age of first egg on chromosome 6 positioned at 0, 32, and 34 cM, respectively. The results of the present study revealed associations for YC-L*2 and YC-a*2 on chromosome 1 (190.34 cM) as well as TLE and EPR on chromosome 6 (31.8 cM) in GLM. Alternatively, we identified EET2 QTLs on chromosome 5 (12.02 cM), YW2 on chromosome 6 (0 cM), and YW2 as well as YD2 on chromosome 20 (20.35 cM) in the MLM. These findings are consistent with the results presented by Ref.24. In turn, Ref.3 recognised QTLs for the number of eggs laid and the egg production rate on chromosome 1 between 36 and 42 cM. The differences among the results of these studies may be explained by the different durations of egg production. In our previous report, we identified a QTL for growth-related traits on chromosome 1 using the same Japanese quail strains16. Similarly, in the present study, two associations were found on the same locus  on chromosome 1 for BW 4 and 5. In addition, we detected QTLs for ESA1 and YD1 on the Z chromosome between 22,757,726 and 31,279,475 bp17. Associations underlying WK 9 in GLM, EET1 in MLM, and WK 7–16 in both approaches were identified within the same chromosome and at the same positions. This was expected as positive correlations have been observed between the related egg trait and body weight73. Only chromosomal regions were explored in the above-mentioned studies on the Japanese quails; therefore, future studies focusing on the association between SNPs and candidate genes would be of great value for improving QTL resolution in Galliformes.The evaluation of 44 phenotypic traits from 567 birds in the F75. In the first study of its kind on Japanese quail, we used association analysis with GLM and MLM, based on RAD-seq SNPs, to examine production traits like body weight and egg-related traits. Comparative genomic studies of Japanese quail and chickens based on cytogenetics53, orthologous genes76, and linkage analysis52 have shown a high rate of synteny-conserved karyotypes and genomic structure between these species. Therefore, the GWAS results from chickens were used to identify potential candidate genes in the present study. However, due to differences in growth conditions between chickens and quail, body weight at the early growth stage, sexual maturity age, and maximum growth rate were considered in both species.GWASs are widely used to distinguish SNPs associated with production traits in chickens71. Reference77 identified a chicken QTL on chromosome 27 in the same region as the one discovered in the present study. This region on chromosome 27 contains two genes, LIM and SH3 protein 1 (LASP1) and phosphatidylinositol-5-phosphate 4-kinase, type II, beta (PIP4K2B), which regulate the decrease in mice body weight79. Associations between gene expression at the initial ages have also been reported in chickens. Reference80 identified a genomic region (169.8–175.3 Mb) on chromosome 1 associated with body weight traits. In a study of genetic dissection of growth traits in a Chinese indigenous breed with a commercial broiler cross81, the major QTL of body weight was mapped to the end of chromosome 1 (173.7 Mb). Here, associations underlying early growth stages were located on chromosome 1 between 168.74 and 172.99 Mb as indicated by both GLM and MLM analyses. Retinoblastoma 1 (RB1) was determined to be the significant gene in this position using a haplotype approach82.The detection of associations related to body weight during juvenile age is likely due to the strong correlation between body weight traits during early growth stages. It is anticipated that numerous genes control body weight during this period because growth is a highly intricate trait influenced by multiple loci that affect appetite, feed intake, body composition, nutrient utilization, and physical activity. In the present study, six shared SNPs between GLM and MLM approaches were identified on chromosomes 1 (147.6 and 167.7 Mb), 3 (29.5 and 40.4 Mb), 13 (13.2 Mb), and 27 (1.9 Mb) associated with body weight at hatching, a trait that is strongly influenced by maternal effects83. The sexual maturity in quail is between 6 and 7 weeks of age, while that in chickens is between 16 and 24 weeks of age, depending on the breed84. We considered quail’s SNPs associated with body weight around the age of the birds in our study for comparison with the information obtained from chicken studies. The genetic regulation of body weight at maturity and later ages is a multifaceted process involving a network of genes that interact with various physiological systems, hormones, and environmental factors. The complexity of these interactions contributes to the expectation that numerous genes are involved in shaping body weight traits in quails. In a GWAS of carcass traits of Jinghai Yellow chickens47, five SNPs were detected for foot weight on chromosome 4 located between 75.54 and 75.67 Mb; thus, indicating that this region is located within 2.08–2.38 Mb away from genes, such as family with sequence similarity 184 member B (FAM184B), quinoid dihydropteridine reductase (QDPR), and LIM-domain binding factor 2 (LDB2). In addition to these genes, Ref.40 indicated that the F box and leucine-rich repeat protein 5 (FBXL5) has a significant influence on chicken growth traits and important biological functions. They identified this gene to be located on chromosome 4 from 72.9 to 77.9 Mb and to be strongly associated with body weight for weeks 6–16. QDPR and LDB2 were associated with shank circumference32 and body weight in Beijing You chickens38. Reference85 detected two SNPs that were associated with the body weight at the time of oviposition and are located separately at 78.8 Mb and 78.9 Mb on chromosome 4 within the FAM184B gene. The non-SMC condensing 1 complex, subunit G (NCAPG) gene, which is located within 0.1 kb downstream of the FAM184B gene, is a well-known candidate gene for body frame and carcase traits in cattle, and is considered to modulate the body and carcase weight of various breeds87. At 73.9 and 76.1 Mb, two SNPs associated with ESS2 and ESS1 on chromosome 4, respectively, were identified by both GLM and MLM analyses. Since ESS is positively correlated with body weight84, the influence of these genes on body weight is of considerable importance. Moreover, Ref.88 reported the importance of the myostatin (MSTN) gene located on chromosome 7 on chicken body weight at 112 days of age. Similarly, we detected numerous associations underlying body weight at maturity and later ages on chromosome 7 at 22.65 Mb in both GLM and MLM analyses. The detection of QTL at later ages has also been reported in chickens89. The growth hormone-releasing hormone receptor (GHRHR) gene was mapped at position 1.7 cM and polymorphism analysis of this gene revealed three SNPs in the promoter region, which had a significant effect on body weight at 7, 9, 11, 13, and 17 weeks of age90. These findings are in line with our SNP association results. In MLM analysis, we identified 10 SNPs that explain over 10% of the phenotypic variance. This underscores their genetic importance and supports our SNP association findings. However, further investigation is needed to uncover the functional roles of these genes, shedding light on their contribution to the observed phenotypic variations at 10–16 weeks of age.Maximum growth rate and body weight at sexual maturity and later ages are similar traits that describe mature body weight and have a high genetic correlation of nearly 191 found the most significant SNP associated with egg number located within intron 12 of the growth factor receptor-bound protein 14 (GRB14) gene. GRB14 mRNA is highly expressed in the ovary, liver, kidney, and skeletal muscles of humans and mammals93. Similarly, our MLM suggested that an SNP associated with TLE is located on the same chromosome and position . It has been speculated that GRB14 influences egg production in layers91. The age of first egg is an important indicator of sexual maturation in female birds and is influenced by genetic and environmental factors94. Here, we detected 7 associations that might influence AFE, and they were located on chromosomes 1–4 and Z based on the GLM, and 18 associations positioned on chromosomes 1–3, 5, 8, 12, 19, and 21 based on the MLM analysis. A SNP in intron 2 of the odd oz/ten-m homolog 2 (ODZ2) gene has been previously identified to be significantly associated with chicken AFE91. This gene is expressed in the developing chicken brain and may affect the sexual maturity95. Additionally96, demonstrated that the gonadotropin-releasing hormone I (GnRH-I) gene is associated with chicken AFE. GnRH stimulates the synthesis and secretion of gonadotrophins, which induce steroidogenesis in the gonads, culminating in ovarian follicle growth and ovulation for egg production97. Reference98 identified two novel forkhead box L2 (FOXL2) and growth differentiation factor-9 (GDF9) genes associated with egg production in the Chinese Dagu chickens. These polymorphisms play a critical role in the regulation of ovarian development in hens. This result supports our findings in the present study, in which 53 associations were detected for EPR by both the GLM and MLM analyses.In addition to body weight, egg quality and production traits are the major selection criteria for poultry breeding. The findings of the association study on the TLE and AFE in this study hold significant implications for egg-type poultry breeding. The results have identified specific associations associated with variations in egg production traits, offering valuable insights into the genetic basis of egg production. However, the results of egg production may vary across different studies, and this variation may be attributed to the diverse durations of the egg collection period. In a GWAS investigating egg production and quality traits in chickens, Ref.17. In the present study, 49 associations were identified for EW1,2 by both the GLM and MLM analyses. The associations for EW on chromosome 4 between 35.49 and 65.59 Mb are in line with the SNP for the same trait and chromosome at 49.28 Mb located in the shroom family member 3 (SHROOM3) gene99. Shroom3 is a cytoskeletal protein involved in regulating cell shape (arrangement and remodelling) in certain tissues100. Among the external egg traits, eggshell characteristics are the most important and play a significant role in the reproductive performance and human consumption. Reference91 identified three SNPs for chicken ESW on chromosomes 2 (86.11 Mb), 3 (110.09 Mb), and 11 (9.59 Mb), and two SNPs for EST on chromosome 1 (171.22 and 179.35 Mb). Moreover, they introduced a gene for each detected SNP: polypeptide N-acetylgalactosaminyltransferase 1 (GALNT1), BLK, zinc finger protein 536 (ZNF536), ENOX1, and LOC18918. These findings support the results of our study, in which two SNPs were detected for ESS2 on chromosomes 1 (172.99 Mb) and 11 (14.96 Mb) based on the GLM, and 12 associations were found for ESS1,2, ESW1, and EET1,2 on chromosomes 1 (171.51–174.10 Mb), 2 (84.95–89.81 Mb), and 11 (5.16–14.70 Mb) based on the MLM analysis. The sodium channel (SCNN1) gene family is expressed in the active uterus during eggshell mineralisation and plays an essential role in eggshell formation101. Polymorphisms of eggshell organic matrix genes were considered to be related to eggshell thickness, eggshell strength, and dynamic stiffness102. SCNN1 has four family members, SCNN1a, SCNN1b, SCNN1g, and SCNN1d, which affect eggshell formation103. A study investigating eggshell quality traits identified SCNN1a, SCNN1b, SCNN1d, and SCNN1g genes on chromosomes 1 (80.03–80.04 Mb), 14 (7.002–7.01 Mb), 21 (2.43–2.44 Mb), and 14 (7.01–7.02 Mb), respectively, and associated them with egg weight, eggshell weight, eggshell percentage, eggshell strength, and eggshell thickness101. Our MLM analysis suggested associations for ESS2 , ESW1 , and EW1 , located near the same chromosome, as stated by Ref.101. In addition, an association study identified three candidate genes, phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma (PIK3C2G), inositol 1,4,5-trisphosphate receptor type 2 (ITPR2), and non-SMC condensin I complex subunit G (NCAPG) to be implicated in the dynamic eggshell quality and located on chromosomes 1 and 4 from 57.3 to 71.4 Mb104. PIK3C2G possesses the C2 domain and acts as lipid binding motif, ITPR2 has been shown to be important to the process of eggshell calcification, and NCAPG gene was discovered to be associated with eggshell weight for young hens in a genome-wide association analysis104. Moreover, one association analysis determined that another lipid-related gene, low-density lipoprotein receptor-related protein 8 (LRP8), a new member of the egg shell matrix protein family, was significantly associated with eggshell traits105. Our MLM findings are in support of the aforementioned studies, in which 6 associations were identified on chromosome 1 between 60.20 and 67.94 Mb for EW2 and 5 associations were detected for EW1, ESS2, and ESW1 on chromosome 4 between 58.94 and 69.85 Mb.Our association study focused on external egg quality traits. Eggshell quality is a major concern in poultry breeding due to its implications for both reproductive performance and human consumption. In this study, we successfully identified several associations associated with external egg traits in both egg production stages. These findings shed light on the genetic basis of egg quality and provide valuable information for targeted breeding efforts aimed at enhancing egg production and ensuring high-quality eggs for consumers. A number of QTL regions for egg weight, egg length, and eggshell characteristics have been already identified91 identified the ataxia telangiectasia mutated (ATM) gene located on chromosome 1 (184.63 Mb) to be associated with the YW trait. In the present study, based on MLM analysis, we introduced 17 associations for YW1,2 on chromosome 1 between 67.94 and 172.99 Mb. Cathepsin D (CTSD) is another candidate gene that might affect egg characteristics106. Yolk formation involves cholesterol uptake and transport mediated by the very-low-density lipoprotein receptor on the membrane, and CTSD is the key enzyme regulating this process107. In this study, the detection of a wide range of associations (354) on all chromosomes  suggests that CTSD is a candidate gene associated with egg yolk quality traits. The albumen makes up approximately two-thirds of an egg’s weight. Numerous QTLs have been reported for egg white characteristics according to the chicken QTL database . Two promising genes, dopamine receptor D1 (DRD1) and msh homeobox 2 (MSX2) were found to be associated with albumen characteristics108. DRD1 encodes the D1 subtype of the dopamine receptor109. In birds, dopamine has been manifested to be involved in both stimulating and inhibiting prolactin (PRL) secretion, which has been illustrated to play an important role in the onset and maintenance of incubation behaviour110. Moreover, it has been shown to be associated with egg production traits109. MSX2 is a member of the msh homeobox family and is present in various embryonic tissues108. In the developing chick, MSX2 is expressed in the apical ectodermal ridge and the ectoderm of the genital tubercle; thus, playing a crucial role in the growth and patterning of the limb mesoderm111. Since MSX2 and DRD1 are involved in embryonic and ovarian development108, they could be treated as candidate genes associated with egg quality. Here, 23 associations were discovered for AW1,2 located on chromosomes 1–3, 8–9, 14, 21, 23, and Z in the GLM and MLM analyses. Yolk colours are important internal egg traits that are influenced by genetic and environmental factors, most often through the components which are included in the quail’s diet112. A total of 231 associations were found in the present study for SNPs that might influence yolk colours  in both the GLM and MLM analyses. These associations were located on all chromosomes except for chromosomes 13, 22, 24, and 28. Genes underlying yolk colours have been previously reported in Galliformes. Reference113 introduced three candidate genes, glucagon-like peptide 1 receptor (GLP1R), calcitonin receptor (CALCR), and tissue factor pathway inhibitor 2 (TFPI2), and suggested they are significantly associated with yolk colour. GLP1R is a member of the glucagon receptor family of G protein-coupled receptors found on beta cells in the pancreas and on neurons, and is involved in controlling blood sugar levels114. CALCR encodes a calcitonin receptor whose activity is mediated by G proteins which activate adenylyl-cyclase and is involved in calcium homeostasis, bone formation and metabolism, as well as lipid metabolism115. TFPI2 is a Kunitz-type serine protease inhibitor that inhibits matrix metalloproteinase activation and extracellular matrix degradation116. These genes are located on chromosome 5 within the 21.06–21.36 Mb genomic region113. In the present study, based on the MLM analysis, the nearest located SNP associations were in the 18.53–25.60 Mb range on chromosome 5 and responsible for YC-L*1 and YC-b*2 (Supplementary File In our egg quality association study, we extended our investigation to internal aspects, such as yolk weight, diameter, color, and albumen weight. Understanding the genetic associations influencing internal egg quality is crucial due to the shift towards egg products in consumption. Through genetic marker analysis, we identified significant associations in internal egg characteristics, providing valuable insights for targeted breeding to enhance yolk weight and meet consumer preferences in the egg industry. Reference2 values conform to established statistical practices, considering multiple comparisons. The variability in R2 values can be attributed to trait complexity and genetic diversity. Comparative analysis with existing research supports the significance of our findings, contributing to the understanding of targeted traits.In this study, we applied the Bonferroni-corrected GLM and MLM to investigate the genetic basis of targeted traits. Our R1, AW1, and YC-b*2 in the GLM and ELA2 in the MLM analysis. The observed differences in GLM and MLM results may be influenced by factors like population structure, handling of missing data, genetic architecture, and assumptions made by each model. In agreement with previous studies, we hypothesize that multiple positional candidate genes are associated with body weight and egg-related traits. Future studies focusing on genotyping the SNPs within these candidate genes or linked markers would be of great use to improve the associations at the gene level.By the assessment of 44 phenotypic traits using GLM and MLM approaches, we showed the existence of SNP associations with all targeted traits, with the exception of EWTo the best of our knowledge, this is the first study to perform an association analysis of the production traits of Japanese quail. A total of 383 SNPs and 1151 associations were obtained following GLM, and 734 SNPs and 1442 associations were obtained following MLM analyses. The identified associations were significantly correlated with body weight and egg production. Moreover, 35 annotated genes were introduced as candidate genes for the targeted traits based on their nearest positions. Identifying associations and candidate genes could contribute to better understanding the genetic factors controlling body weight, egg production, and quality traits in quails particularly, and chickens generally, and may accelerate the genetic progress in breeding strategies.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Below is the link to the electronic supplementary material.


Previous observational studies have documented an inverse association of birthweight with myocardial infarction (MI) but a positive association with atrial fibrillation (AF). However, the causality of these associations and the underlying mediating pathways remain unclear. We aimed to investigate the causal effects of birthweight, incorporating both fetal and maternal genetic effects, on MI and AF, and identify potential mediators in their respective pathways.case=61,000, Ncontrol=577,000), AF , and 52 candidate mediators . Two-step MR was employed to identify and assess the mediation proportion of potential mediators in the associations of birthweight with MI and AF, respectively. As a complement, we replicated analyses for fetal-specific birthweight and maternal-specific birthweight.We performed Mendelian randomization (MR) analyses using genome-wide association study summary statistics for birthweight , MI  higher risk of MI, whereas each 1-SD higher birthweight was causally associated with a 29% (95% CI: 1.16–1.44) higher risk of AF. Cardiometabolic factors, including lipids and lipoproteins, glucose and insulin, blood pressure, and fatty acids, each mediated 4.09-23.71% of the total effect of birthweight on MI, followed by body composition and strength traits  and socioeconomic indicators , with the mediation proportion for each factor ranging from 8.08 to 16.80%. By contrast, appendicular lean mass, height, waist circumference, childhood obesity, and body mass index each mediated 15.03-45.12% of the total effect of birthweight on AF. Both fetal-specific birthweight and maternal-specific birthweight were inversely associated with MI, while only fetal-specific birthweight was positively associated with AF. Psychological well-being and lifestyle factors conferred no mediating effect in either association.Cardiometabolic factors mainly mediated the association between lower birthweight and MI, while body composition and strength traits mediated the association between higher birthweight and AF. These findings provide novel evidence for the distinct pathogenesis of MI and AF and advocate adopting a life-course approach to improving fetal development and subsequent causal mediators to mitigate the prevalence and burden of cardiovascular diseases.The online version contains supplementary material available at 10.1186/s12933-023-02062-5. Myocardial infarction (MI) and atrial fibrillation (AF) are major contributors to the global burden of cardiovascular diseases, with their prevalence nearly doubling in the past three decades –3. MI isThe Developmental Origins of Health and Disease (DOHaD) hypothesis proposes that early-life intrauterine exposures that affect fetal growth shape individual differences in the pathogenesis of cardiometabolic disease in later life –13. BirtAnother uncertainty is whether there exist different mediating pathways that may explain the observed opposite effects of birthweight on MI and AF. Experimental evidence suggests that an adverse intrauterine environment permanently influences the cellular proliferation, key organ differentiation, and biophysical profiles of cardiovascular and metabolic systems . GeneticTo fill the knowledge gap, we applied MR approaches to discern the causal effects of birthweight, incorporating both fetal and maternal genetic effects, on MI and AF, with a particular interest in identifying mediators in respective association pathways. MR approaches apply genetic variants as instrumental variables (IVs) to infer causality between related traits . For medThis MR study consisted of two analysis phases Fig. . In PhasThis study used publicly available summary statistics of genome-wide association study (GWAS) derived from reliable consortia or studies of predominantly European-descent individuals. Ethical approval and informed consent can be found in the corresponding GWAS publications cited in this work.The data sources for the exposure, mediators, and outcomes used in this study are shown in Table We extracted GWAS summary statistics of birthweight , fetal-specific birthweight , and maternal-specific birthweight  from currently the largest GWAS meta-analysis from the Early Growth Genetics (EGG) Consortium and UK Biobank  . Informa–9 and r2 < 0.10) [2 < 0.001 within a 10,000 kb window using the 1000 genomes reference panel to select independent genetic variants. Finally, we used 124 birthweight IVs to estimate the causal effects of birthweight on candidate mediators, MI, and AF. As a complement, we replicated analyses using 51 fetal-only effects IVs to determine fetal-specific birthweight and 29 maternal-only effects IVs to determine maternal-specific birthweight, in order to provide an in-depth understanding of the biological regulation of birthweight and the origins of the relationships of birthweight with MI or AF.The original GWAS study identified 205 autosomal single nucleotide polymorphisms (SNP) independently associated with birthweight (P < 6.60 × 10 < 0.10) , includiBased on literature reviews of observational and MR studies, we focused on 52 candidate mediators that may lie in the pathways from birthweight to MI or AF  and independent of each other . In MVMR analyses, genetic IVs were the combination of SNPs, which were genome-wide significant (P < 5 × 10–8) in either the GWAS of birthweight or the GWAS of each candidate mediator and were independent of each other . Where SNPs for the exposures were not available in the GWAS summary statistics of MI or AF, we used proxies of SNPs with r2 > 0.8 as substitutes by using the LDproxy search on the online platform LDlink (https://ldlink.nci.nih.gov/).In UVMR analyses, genetic IVs for each candidate mediator were at a genome-wide significant level  method as the primary analysis in UVMR and the multivariable inverse variance weighted (MV-IVW) as the main analysis in MVMR. The IVW method combines the Wald ratio estimates of every SNP in the set of IVs into one causal estimate using the random-effects meta-analysis . The MR We performed UVMR to assess the total causal effects of birthweight on MI and AF and conducted leave-one-out analyses to evaluate the influence of individual variants on these associations.We applied two-step MR analyses to assess whether an intermediate factor could mediate the association between birthweight and MI and AF, respectively , 27. TheFor UVMR analyses, we performed the weighted median, simple mode, weighted mode, MR-Egger, and MR pleiotropy residual sum and outlier (PRESSO) methods as sensitivity analyses to evaluate the robustness of the IVW estimates under different assumptions. For MVMR analyses, we performed the MVMR Egger method to validate the robustness of the MV-IVW results. The weighted median method selects the median MR estimate as the causal estimate and provides a consistent causal estimate if over 50% of the weight in the analysis is derived from valid IVs . The simTwoSampleMR’, ‘MRPRESSO’, and ‘MendelianRandomization’, and the FDR q-values were estimated using the R package ‘fdrtool’ in R software .All MR analyses were conducted with the R packages ‘In UVMR, genetically determined each 1-standard deviation (SD) lower birthweight was causally associated with an increased risk of MI , which was supported by at least three sensitivity analyses , and no horizontal pleiotropy was detected , fasting insulin, total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, apolipoprotein B (ApoB), Omega-6 fatty acids, and linoleic acid were causally associated with a 3-103% higher MI risk , WC, height, and appendicular lean mass were associated with higher AF risks after adjusting for birthweight, with ORs (95% CIs) ranging from 1.08 (1.02–1.13) for childhood obesity to 1.44 (1.28–1.62) for WC , total cholesterol (22.20%), type 2 diabetes (21.96%), fasting insulin (21.65%), SBP (21.53%), LDL-C (17.25%), appendicular lean mass (16.80%), grip strength (13.01%), triglycerides (11.21%), and 2-h glucose (10.7%) each mediated more than 10% of the total effect of birthweight on MI  between fetal-specific birthweight and MI (Additional file This MR study demonstrated the opposite causal effects of birthweight on MI and AF and shed light on their distinct mediating pathways. Genetically determined each 1-SD lower birthweight was causally associated with a 40% higher risk of MI, whereas each 1-SD higher birthweight was causally associated with a 29% higher risk of AF. Both fetal-specific birthweight and maternal-specific birthweight showed inverse associations with MI; only fetal-specific birthweight, but not maternal-specific birthweight, showed a positive association with AF. The causal association between lower birthweight and MI risk was substantially mediated by cardiometabolic traits, body composition and strength traits, and socioeconomic indicators. On the other hand, only body composition and strength traits played a mediating role in the association between higher birthweight and AF. Of 52 potential mediators of interest, psychological well-being and lifestyle factors conferred no mediating effect in either association.High birthweight is mainly caused by maternal hyperglycemia, obesity, and intrauterine overnutrition, while low birthweight strongly represents fetal poor growth and fetal programming due to intrauterine malnutrition . Our stuIn view of birthweight being determined early in life and its opposite causal effects on MI and AF, we specifically explored if there are causal mediators in the subsequent life trajectories mediating the relationships of birthweight with MI or AF. Notably, we found that cardiometabolic factors accounted for the majority of mediating roles in the causal pathway from low birthweight to high MI risk. Our findings can be interpreted by the thrifty phenotype hypothesis and evidence from experimental studies that adaptation to a nutritionally depleted intrauterine environment might result in alternations in insulin-signaling pathways, epigenetic modifications, cellular proliferation, and key organ differentiation, which induce disruptions in the metabolism of glucose, lipids, and metabolites and contribute to adult cardiometabolic diseases , 33–35. The strengths of this study included the rigorously designed MR analytical framework to detect the causality of birthweight with MI and AF and the mediation pathways, the employment of comprehensive complementary sensitivity analyses to validate the robustness of the MR results, and the use of summary statistics from large-scale GWASs, enhancing statistical power and precision of causal estimates. This study also had several limitations. First, based on the two-sample MR design, we assumed that the relationships between birthweight and outcomes are linear in UVMR and MVMR analyses. Future studies using individual-level data are warranted to investigate the potential non-linear causal relationships. Second, although we have included common and important candidate mediators to facilitate clinical practice, this study cannot fully capture all mediation pathways, especially non-heritable factors. Third, the potential exposure-mediator interaction cannot be modeled in the present two-sample MR setting. Nonetheless, the MR approach could largely alleviate the potential bias caused by the exposure-mediator interaction . Fourth,In conclusion, this study elucidated that lower birthweight was causally associated with a greater risk of MI, while higher birthweight was associated with a greater risk of AF. Cardiometabolic factors and body composition and strength traits were the primary mediators in the pathway from birthweight to MI and AF, respectively. Our findings provide novel evidence for the pathogenesis of MI and AF and advocate adopting a life-course approach to improving fetal development and targeting subsequent causal mediators to mitigate the prevalence and burden of cardiovascular diseases.Additional file 1. Additional tables and figures.


Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate  methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs. Multivariate Lassosum leverages genetic correlations among human traits to achieve better performance at predicting the risk of each trait analyzed than other multi-trait and single-trait methods selecting a limited number of SNPs (sparse solution) on simulated data and real data from a study of schizophrenia and bipolar disorder. A polygenic risk score (PRS), usually defined as a weighted sum of single nucleotide polymorphism (SNP) alleles,,The best linear unbiased predictors (BLUPs) derived from linear mixed models and their summary statistics versions SBLUP and MT-SBLUPZhang et al.Our Multivariate Lassosum approach is implemented in the R package multivariateLassosum extending the Lassosum package and retaining its convenient data handling features. We evaluate its performance and compare it to the performance of approaches involving SNP selection: the multi-trait PANPRSq, the number of SNP markers by p, and the number of subjects by n. Our starting point is the linear mixed model of Maier et al.y is a length i for the SNP j defined as j for the trait k. Maier et al.We denote the number of phenotypes by y and β, We used actual genotype data on 29,330 subjects from the CARTaGENE research platform   as we do,,,Genetic correlation between SZ and BD has been established through familial coaggregation studies,Since LDpred2 auto option does not require a validation set, LDpred2 auto was applied to the original univariate coefficients released by the PGC in their summary statistics , 442 non-affected adult relatives (NAARs), and 314 relatives whose diagnosis was considered unknown . BD subjects were evaluated for the presence of symptoms of psychosis, which were detected in 93 genotyped BD subjects. PRSs were standardized to have mean 0 and variance 1 in the NAARs group separately for the two genotyping arrays. Association between diagnosis and PRSs was evaluated under a logistic model estimated by generalized estimating equation with a variance estimator robust to familial dependence.j to SZ and SNP-specific contributions to heritabilities and covariances were predicted from the BLD-X heritability model fitted to pseudo summary statistics for the 3,324,089 SNPs with LD scores and MAF available for the EUR sample using the approach described above. Annotations were available for 3,611,681 SNPs; values were imputed for the other 28,240 SNPs as for the simulations above. In this model fit, after setting negative SNP predicted heritabilities to 0, some predicted covariances led to non-semi-positive definite genetic covariance matrices, and these covariances were replaced by The version of Multivariate Lassosum with the heritability model best fitting the simulated data  applied to the two simulated traits generally achieved higher predictive performance than competing methods analyzing either both traits jointly or each trait separately in terms of estimated PRS vs. true predictor correlation and AUC  and S2 is led to optimal or near optimal performance of Multivariate Lassosum under the reference simulation scenario  on both summary statistics simulated under a variety of scenarios and actual SZ and BD summary statistics. LDpred2 and the original Lassosum were competitive with Multivariate Lassosum only with low trait polygenicity. There are other PRS definitions involving some form of shrinkage of the SNP coefficients without forcing them to 0, but none of them consistently beat LDpred2 in a thorough evaluation on actual data for psychiatric traits,We also adapted the S-LDXR method initially proposed to estimate the trans-ethnic genetic covariance of one trait to estimate the genetic covariance between two traits as a function of SNP annotations, and we found that using such a model (BLD-X) to predict SNP-specific contributions to the heritabilities and genetic covariance of two traits improved the predictive performance when simulated SNP effects actually depended on the included SNP annotations, even though the simulation model did not coincide with the analysis model . HoweverWe focused our evaluation of the methods on a pair of traits, although the methodology and software code are general for The covariance terms of the β added to the quadratic penalty in ,,The improvements in prediction performance achieved by considering genetically correlated traits in the construction of PRS could be meaningful in conjunction with other factors. As with other complex traits, non-genetic factors play an important role in the etiology of SZ and BD, such as childhood traumaIn conclusion, the availability of summary statistics for a multitude of genetically correlated traits offers an opportunity to improve risk prediction of complex disorders through PRS in conjunction with non-genetic factors. We have made available Multivariate Lassosum as a software package to implement prediction of genetically correlated traits under a penalized regression framework.


Vigna subterranea (L.) Verdc.] landraces sourced from diverse origins. The study aimed to identify marker traits association for nine agronomic traits using GWAS and their candidate genes. The experiment was conducted at two different locations laid out in alpha lattice design. The cowpea [Vigna unguiculata (L.) Walp.] reference genome (i.e. legume genome most closely related to bambara groundnut) assisted in the identification of candidate genes.Genome-wide association studies (GWAS) are important for the acceleration of crop improvement through knowledge of marker-trait association (MTA). This report used DArT SNP markers to successfully perform GWAS on agro-morphological traits using 270 bambara groundnut [The analyses showed that linkage disequilibrium was found to decay rapidly with an average genetic distance of 148 kb. The broadsense heritability was relatively high and ranged from 48.39%  to 79.39% (number of pods per plant). The GWAS identified a total of 27 significant marker-trait associations (MTAs) for the nine studied traits explaining 5.27% to 24.86% of phenotypic variations. Among studied traits, the highest number of MTAs was obtained from seed coat colour (6) followed by days to flowering (5), while the least is days to maturity (1), explaining 5.76% to 11.03%, 14.5% to 19.49%, and 11.66% phenotypic variations, respectively. Also, a total of 17 candidate genes were identified, varying in number for different traits; seed coat colour (6), days to flowering (3), terminal leaf length (2), terminal leaf width (2), number of seed per pod (2), pod width (1) and days to maturity (1).These results revealed the prospect of GWAS in identification of SNP variations associated with agronomic traits in bambara groundnut. Also, its present new opportunity to explore GWAS and marker assisted strategies in breeding of bambara groundnut for acceleration of the crop improvement.The online version contains supplementary material available at 10.1186/s12864-023-09684-9. Vigna subterrenea (L) Verdc.] is a legume of sub-Sahara African origin that plays an important socio-economic role in semi-arid regions of the continent  a legume species. This study was initiated with the objective of identifying marker traits association in bambara groundnut for some important agronomic traits using GWAS and investigate candidate genes.Next-generation sequencing-based genotyping such as genotyping by sequencing (GBS) and diversity arrays technology-based sequencing (DArTseq) platforms have significantly contributed to the popularity of single-nucleotide polymorphisms (SNPs)  and enab 2n = 22 . Amkul e 2n = 22  have useThe distribution of SNPs across the eleven chromosomes after filtering and removing of > 20% missing data and SNP that was unable to be mapped (scaffolds) to chromosome region Table . The nump < 0.001) among the landrace for all the studied traits . The number of pods per plant had significant positive correlation with terminal leaf length (r = 0.30), terminal leaf width (r = 0.46), pod length (r = 0.13), pod width (r = 0.20) and number of seeds per pod (r = 0.11). Also, days to 50% flowering had significant correlation with days to maturity (r = 0.32), terminal leaf length (r = 0.13), terminal leaf width (r = 0.11), pod width (r = -0.13) and number of seed per pod (r = -0.17) while days to maturity showed significant correlation with terminal leaf length (r = 0.41), terminal leaf width (r = 0.40), pod length (r = 0.21), number of seeds per pod (r = 0.20) and number pods per plant (r = 0.60).The relationship among the studied agro-morphological traits Fig.  revealed2) associated with all the chromosomes were determined based on the genotyping information for 2110 genome wide SNPs in the 270 bambara groundnut landraces. A total of 104,226 intra-chromosomal pairs were generated from the LD analysis and the mean r2 of all pairs is 0.1 (Supplementary Table 2 significant (p < 0.005) pairs was 21,852 (20.97%) and a mean of 0.23. A non-linear regression curve exhibiting a decreasing trend of LD decay with an increase in the physical distance was observed  identified a total of 28 significant SNPs for the studied agro-morphological traits in the bambara groundnut population and were distributed across nine of the eleven chromosomes Table , Fig. 4.The MTAs on seed coat colour trait identified six candidate genes controlling seed pigment and seed coat colour  and the percentage of phenotypic variation among the significant SNP markers varied from 5.76% to 11.03%. However, on days to flowering three candidate genes  that regulate flowering time was identified with the percentage of phenotypic variation among significant markers ranges from 14.53% to 19.49% while for days to maturity only one candidate gene (Vigun02g095500) responsible for pod maturity was identified and the significant marker explained 11.66% of the phenotypic variation. Furthermore, candidate genes responsible for terminal leaf length (Vigun02g150200 and Vigun02g150500) and width (Vigun02g085200 and Vigun10g140300) which is one of the key organs for photosynthesis were identified. Pod length and width was analyzed as a measure of increase in organ size with one candidate gene for pod width (Vigun09g269300) identified. In addition, candidate gene identified for number of seed per pod (Vigun06g111300 and Vigun06g111300) trait which play significant importance on bambara groundnut yield.The marker density revealed that SNPs are in abundance in telemetric regions of the chromosome than the pericentrometric regions and they varied across chromosome arm positions. This could be due to bias from restriction enzymes used or difference in gene coverage. Similar findings have been reported by Serba et al. . FurtherMedicago truncatula: 3 kb [Arabidopsis thaliana: 3–4 kb [Oryza sativa Indica: 123 kb [Oryza sativa Japonica: 167 kb [This is the first report of quantification of LD decay in bambara groundnut, defining the extent of LD in the germplasm. The extent of genome-wide recombination is expected to be lower in self-pollinated crop like bambara groundnut than what is observed in cross pollinated crops . A rapidla: 3 kb , Arabido: 3–4 kb , sorghum: 3–4 kb , foxtail: 3–4 kb , cowpea:: 3–4 kb , cultiva: 3–4 kb  and Oryz: 123 kb  while is: 123 kb . However: 167 kb , common : 167 kb  and chic: 167 kb  for selfIn bambara groundnut, previous report aimed at elucidating the genetics of agronomic traits have mainly focused on the maker traits association using biparental cross. This few available report on biparental cross used genetic linkage groups and was not identified based on bambara groundnut or cowpea reference genomes. Hence, it could not be compared directly with our results because there is no available literature. Understanding the diversity and population structure is in a germplasm panel is importance prerequisite needed for GWAS and has been performed in our previous report  and it iArachis hypogaea L [Seed coat colour is an important trait used for the characterization of bambara groundnut  and affepogaea L . Furtherpogaea L  while thpogaea L .Flowering time is an important agronomic trait that plays key role in the adaptation of a variety to specific agro-ecological areas. Several significant SNPs identified on chromosome 7 and 9 in the current study have shown the important of these chromosomes in controlling flowering time and adaptation in this crop. In an earlier study in cowpea, Paudel et al.  obtainedLeaf size  is an important organ responsible for photosynthesis and plays an important role during plant growth and development. Two candidate genes were identified terminal leaf length (Vigun02g150200 and Vigun02g150500) and two for terminal leaf width (Vigun02g085200 and Vigun10g140300). The gene Vigun02g150200 and Vigun02g085200 (BTB/POZ domain protein) encodes BLADE-ON-PETIOLE1 required for leaf morphogenesis in Arabidopsis thaliana . In addiMedicago truncatula involve in seed size [The number of seeds per pod and pod size (pod length and width) plays key roles in the yield of legumes. Two candidate genes were identified for number of seeds per pod (Vigun06g111600 and Vigun06g111300) while one for pod size (Vigun09g269300). The candidate gene Vigun06g111600 (B3 DNA-binding domain protein) for number of seed per pod plays important role on development of ovule , 74 and eed size .It is important to highlight the challenges faced in the present study when finding marker trait associations because of the limited number of landraces used and unavailability of the complete reference genome of bambara groundnut. The present study identified few markers trait associations but using higher number of landraces with diverse population and reference genome of the bambara groundnut would lead to detection of higher number of MTAs. The effect of population size on GWAS has been noted and the detection power of MTAs decreased according to reduction of population size . AlqudahThis study reports marker traits association and candidate genes identified for agro-morphological traits using 270 bambara groundnut landraces diversity panel. The LD analysis revealed that the germplasm panel decayed rapidly at 148 kb. The GWAS using MLM mode identified a total of 28 MTAs for the studied agro-morphological traits with 17 candidate genes which would be beneficial in marker assisted selection and traits introgression in bambara groundnut after their validation. Overall, this finding provides new insight on the prospect or application of GWAS on bambara groundnut that would promote the use of marker assisted breeding strategies for future use in the crop.The study comprised a collection of 270 landraces of bambara groundnut representing 17 countries from different regions of Africa  and unknown origin sourced from United Kingdom germplasm. The landraces were obtained from the gene bank of International Institute for Tropical Agriculture (IITA), Ibadan, Nigeria and Crop and Soil Science Research Farm in Bunda, Malawi Fig. . The lanVigna unguiculata (L.) Walp) [Young leaves from each landrace were harvested from the greenhouse and put inside eppendorf tube with dry ice. The harvested leaf tissue was stored at − 80˚C and was lyophilized, then ground in tubes using a plate shaker. The DNA was extracted using Nucleomag Plant Genomic DNA extraction kit according to the procedure of the manufacturers and the quality of the DNA was checked. Kilian et al.  procedur.) Walp) . The mar.) Walp)  was emplijk = phenotypic response, µ = grand mean, Gi = is the genetic effect of the genotype i, Rj = is the fixed replicate effect of the replicate j; Bk(j) = is the effect of incomplete block k within replication j and, ɛijk = the residual error.The analysis of phenotypic data for alpha lattice design was computed with R software . A mixed2 is broad-sense heritability, The broad-sense heritability of the combined location analysis was calculated with the formula:Spearman’s correlation was analyzed to understand the relationship among the studied agro-morphological traits using “PerfomanceAnalytics” package .2) using TASSEL 5.2.33 [2 values were plotted against the corresponding physical distance, and a non-linear regression model was fitted to estimate the genome wide LD decay according to Raman et al. [p < 0.005 was considered the significance threshold for marker pairs to be in LD with each other. The LD decay was drawn using R statistical package [2.The Linkage disequilibrium (LD) was calculated as the squared allele frequency correlations . The significant P-values  for the plant genome wide association studies was computed as described by several authors [2 values multiplied by 100.The GWAS was conducted using mixed linear model (MLM) algorithm implemented in R package . The mod authors –92 and Bwww.phytozome.net). Candidates were selected based on whether the function of the genes had been characterized before in cowpea or if similar genes in other species had known roles.The cowpea reference genome  was usedAdditional file 1.


Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off. Farmed animals can be exposed to a wide range of environmental challenges during their development and lifespan. Climate change is known to exacerbate these challenges, e.g., through increased temperature fluctuations or more frequent occurrence of extreme weather conditions  and assoNeither the resilience nor the production potential of an animal in ideal environmental conditions are directly measurable. Instead, estimates for both traits can be obtained by using so called reaction norm (RN) models in which the performance of an animal is regressed against the environmental challenge level . In the One of the hurdles for estimating resilience is that in many cases the environmental challenge level is unknown or difficult to quantify. This is particularly the case when animals are exposed to infectious pathogens  or a whoAnother obstacle in estimating resilience through a RN approach is the lack of performance data of an individual across multiple environments. Many phenotypes have limited measurements per animal, e.g., carcass weight and hence rely on phenotypic records of related individuals . The spaNumerous studies have applied RN models to predict animals’ resilience or performance under different environmental challenge conditions . One of Therefore, the first objective of this study was to assess the benefits of using genomics over pedigree-based predictions in obtaining accurate and unbiased breeding value estimates for production potential and resilience from linear RN models. In particular, we assessed how prediction accuracies depend on the number of phenotypic records per individual, the distribution of related animals across environments, and on the ability to accurately quantify the environmental challenge level. The second objective was to assess the feasibility of RN models to improve production potential and resilience and compare these to the response to selection that can be achieved by using conventional models under a range of conditions.The potential benefit of genomic selection to select for production potential and resilience under a linear RN model was assessed using simulations of an outbred population of farmed animals. Without loss of generality, in this study the genomes of sheep populations were simulated, but it is expected that results would be similar for other population of terrestrial farmed animals. To assess the impact of genomics and other factors on prediction accuracies and bias, a sheep population undergoing random selection was simulated. To assess response to selection, a population undergoing selection for 10 generations under different selection strategies was simulated. Each simulation scenario was replicated 100 times.The phenotype was defined assuming a RN model, where the individual’s performance is affected by two components, simulated as random effects: one related to the performance under ideal condition and another being linearly related to an environmental challenge level the individual is exposed to.R) and both are assumed to be under genetic (A) and environmental (E) control. Hence, the performance, y, of individual j in an environment with challenge level X is:k corresponds to the time of measurement in case of multiple records for an individual. In the above equation, μ0 refers to the population mean performance in the absence of challenge, μR is the population average rate of reduction in performance . The terms Here, the respective components are denoted as production potential (subscripts 0) and resilience , 2N haplotypes were randomly drawn from the pool of 20,000 haplotypes. In addition, sampling of haplotypes was done independently for each chromosome to ensure independency of replicates. As shown in the A similar approach as in ep breed . Then, tOnce the base generation was sampled in each replicate, 1,500 segregating loci with the highest minimum allele frequency were selected for each chromosome: 500 being randomly assigned to be quantitative trait locus (QTL) affecting the traits and 1,000 being part of the SNP chip array used to calculate the genomic relationship matrix (GRM) needed in the genomic evaluation. The total number of QTLs and SNPs across the whole genome were 13,000 and 26,000, respectively.A0 and AR) to allow for non-zero genetic correlation between both traits. The QTL effects for A0 and AR were sampled from a bivariate normal distribution with a unit standard deviation and a correlation equal to the targeted genetic correlation between A0 and AR  were calculated as the sum of all QTL effects given the individual’s genotypes. The QTLs were assumed to have pleiotropic additive effects on both the production potential and the resilience traits , individuals were assumed to be phenotyped in flocks. These flocks represent micro-environments, defined by a range of environmental challenge levels around a flock specific average challenge level. Thus, for an overall environmental challenge level , individuals in a given flock f were exposed to a subset ranging between f and ϵ is a parameter determining the heterogeneity of the flock conditions. If multiple records were required for an animal, multiple environmental challenge levels were sampled from the same flock.To simulate the level of environmental challenge (f) was sampled from a uniform distribution within X for a given phenotype was sampled from a uniform distribution within the range Xmax was set to 2, which corresponded to an average difference of 6 phenotypic standard deviations in performance compared to the average performance at optimum condition (X = 0)  . Having ϵ. The magnitude of ϵ was set to 0.1, but other values were simulated  random (RND), where individuals were randomly distributed across all flocks, (ii) clustered (CLS), where individuals from the same half-sib family were allocated into one random flock, (iii) assortative (AST), where individuals from the same half-sib family were allocated to the same flock such that offspring from a sire with the highest breeding values for production potential were allocated to the flock with the lowest level of environmental challenge , and (iv) disassortative (DIS), i.e., similar to AST scenario but with reverse order of sires.Genetic evaluations were carried out using the best linear unbiased predictor (BLUP) or genomic BLUP (GBLUP)  defined When the environmental challenge levels were unknown, the genetic evaluation was carried out in a two-step RN approach : initialHence, the use of the proxy with the RN approach introduces two sources of error into the analysis: (i) error in the estimates of the flock effects in the first step of the evaluation, and (ii) error by assigning the same environmental challenge level to all animals in a flock, thus ignoring the within flock heterogeneity in challenge. Therefore, as flocks become more heterogeneous, the estimated proxy becomes more uncertain and less representative of the true challenge level, potentially affecting the quality of the estimates.l that animal j belongs to, j and In addition, for the comparison of responses to selection based on different breeding criteria traits, EBVs for production performance were calculated using a conventional model without the term associated to the environmental challenge  but accounting for the environmental flock effects,https://github.com/mghaderizefreh/GenEval. The genomic relationship matrix was calculated following (An in-house software was used to estimate the variance components and (G)EBVs using restricted residual maximum likelihood  and BLUPollowing . The genb is the regression coefficient between (G)EBVs and TBVs. Hence, unbiased estimation has a zero value for the standardised regression coefficient, whereas overestimated or underestimated (G)EBVs have positive or negative standardised coefficients, respectively.The quality of the genetic evaluation was assessed by the accuracy and bias of (genomic) estimated breeding values, (G) EBV. The prediction accuracy is defined as the correlation between (G) EBV and the TBV. The bias ained in ,b~=1/b−As mentioned above, two different population structures were considered to achieve the objectives of this study: To assess the impact of genomics and other factors on prediction accuracies and bias, a sheep population undergoing random selection was simulated with 3 generations and ∼1,000 animals per generation. To assess response to selection, a population undergoing selection for 10 generations under different selection strategies was simulated. The number of animals per generation in the second population was ∼3,000.The scenarios considered here aim to assess the benefit of genomic prediction in improving the quality of the prediction of production potential and resilience as well as to assess factors affecting the predictions. The population consisted of three non-overlapping generations with a half-sib structure. The base or first generation started with 100 males and 1,100 females, with each male being mated to 11 females to produce one offspring per female, resulting in one male and 10 female offspring . In the second generation, each sire was mated with 10 dams to produce 1,000 individuals in the third generation.• The effect of population parameters on the benefit of GBLUP over BLUP in terms of improved accuracy.• The effect of the distribution of individuals across environments when comparing the GEBV accuracies obtained from individuals across flocks allocated assuming the RND, CLS and AST scenarios.• The effect of uncertainty of the environment challenge level in a RN model situation by studying the impact of the within-flock level heterogeneity on the range of environmental challenge.Further, we assume that the third generation does not have phenotypes and we assess the (G)EBVs for this generation only. We use these assumption in order to have generally low accuracy so that the above effects are more pronounced.This population was used to assess the impact of different factors on the benefit of genomic prediction to evaluate production potential and resilience. They include:Performance records in this section were simulated assuming a genetic correlation of −0.5 between resilience and production potential, with flocks covering 10% of the whole environment and one record for each individual in a random distribution scenario. This population structure was used in scenarios that quantified the selection response for production potential and resilience when using a RN model. The population attempted to mimic a sheep population with large half-sib and small full-sib families. The population structure for each generation consisted of 8,100 individuals , all genotyped with the SNP chip array and having single phenotypic record. At each generation, half of the females and a tenth of males were selected to mate at random  and a litter size of 3 including 1 male and 2 females was assumed. The assumption of discrete generations and litter size of 3, albeit not very common, , was madα (0 ≤ α ≤ 1) is the weight given to resilience. Hence, α = 0 corresponds to selection for production potential only, whereas α = 1 corresponds to selection for resilience only. The selection scheme was done over 10 discrete generations and individuals were allocated using the RND allocation scenario. We also contrasted scenarios where performance records were generated in a narrow compared to a wide range of environments . In particular, as would be expected, prediction accuracies for both traits increase with increasing genetic correlations (as shown for GEBVs in For the remainder of this section (Random selection scenario), we will only show results for GEBVs assuming three records per individual and genetic correlation of −0.5 between production potential and resilience. This scenario was chosen to show how potential trade-offs between the traits may affect prediction accuracies, and because it has a low rate of failure to converge in the REML estimation ensuring that the results are true reflection of the situations compared and not to a lack of information due to inadequate population size. Furthermore, the observed effects of distribution of phenotypes across environments and uncertainty in the level of environmental challenge described below were similar for EBVs and GEBVs.α = 1 in the selection index increases the resilience by 43.3% from −3 to −1.7 after 10 generations  are lowest when animals are reared in clusters and the correlation between the exposed challenge level and the production level is negative  . The mosThe RN models are the most common approach in disentangling the environmental effect from the genetic effect on the phenotype . In manyXd > 0, compared to an environment with no disturbance, Xwd = 0, , being smaller than heritability at Xwd, which is h2 (Xwd) = h2 (0) in Equation Xwd.Over the recent decades, the animal breeding community has given much attention to breeding for resilience. However, it is desirable that by selecting for increased resilience, one does not accidentally select for poor performers when conditions are good. It has been postulated , and showd = 0, . This trIn this study, linear reaction models were used. Changes in performance of animals may however not always be linear with respect to challenge level. Therefore, non-linear reaction norms  can predict phenotypes at specific environments more accurately X. This approach is valid when there is a relatively constant challenge level over a significant period of animals’ life, e.g., prolonged nutritional shortage or heat stress, and when the effects on the animal phenotype of interest can be captured by a constant value, e.g., growth rate over a specific time period, or carcass weight. Several more recent studies, including the EU Horizon 2020 Smarter consortium (Lastly, in this paper, we defined resilience as the ability of an animal to maintain high production performance when exposed to challenge, in line with nsortium  define rnsortium . To estinsortium . Thanks nsortium . Recent nsortium . For exaIn this paper, we studied the benefits of using genomic information, random regression RN models and phenotyping strategies on genetic evaluations of resilience and production potential. We showed that simultaneous improvement of both traits is possible even under unknown environmental challenge conditions if genomic information is used in a RN model and phenotyped animals are not in family clusters. In contrast, genetic improvement in resilience without the use of RN models is possible if genotyped and phenotyped animals are reared in wide range of environmental conditions. Therefore, for the best genetic evaluation, use of artificial insemination or other methods that increase connectedness among genotypes in various environmental conditions, is recommended. Additionally, higher between-variance for different environmental conditions facilitates simultaneous genetic improvement for resilience and production potential.