MicroRNAs (miRNAs) are recognized as gene expression regulators, indirectly orchestrating a plethora of biological processes. Single nucleotide polymorphism (SNP), one of the most common genetic variations in the genome, is established to affect miRNA functioning and influence complex traits and diseases. SNPs in miRNAs have also been associated with important production traits in livestock. Thus, the aim of our study was to reveal the SNP variability of miRNA genes in the genome of the pig, which is a significant farm animal and large-mammal human model. To this end, we applied the targeted sequencing approach, enabling deep sequencing of specified genomic regions. As a result, 73 SNPs localized in 50 distinct pre-miRNAs were identified. In silico analysis revealed that many of the identified SNPs influenced the structure and energy of the hairpin precursors. Moreover, SNPs localized in the seed regions were shown to alter targeted genes and, as a result, enrich different biological pathways. The obtained results corroborate a significant impact of SNPs on the miRNA processing and broaden the state of knowledge in the field of animal genomics. We also report the targeted sequencing approach to be a promising alternative for the whole genome sequencing in miRNA genes focused studies. A pre-miRNA undergoes a second cleavage by the Dicer enzyme, which results in miRNA duplex release2. One strand of this duplex, termed the guide, is incorporated into effector RNA-induced Silencing Complex (RISC) along with Argonaute protein4. RISC exerts its influence on gene transcripts via sequence complementarity of a loaded miRNA and 3′UTR of an mRNA. It may result in its translational repression and less frequently mRNA degradation5, or in some cases, the upregulation of expression6, influencing the further synthesis of a protein. On the one hand, one microRNA may have multiple target genes, and on the other hand, one transcript can be regulated by different miRNAs. Because of such an extensive and multifaceted network of regulation, miRNAs play vital roles in myriads of biological processes and molecular functions, and, as a result, are frequently reported to be deregulated in a variety of diseases, such as, e.g. stroke, cancer, or major depression10.MicroRNAs are classified as short, non-coding RNAs, which do not code for proteins but instead orchestrate gene expression at the post-transcriptional level. They are transcribed as precursor sequences, and their maturation involves two significant steps. A long primary miRNA transcript (pri-miRNA), transcribed from a miRNA gene, is cleaved by the Drosha/DGCR8 enzyme complex to give rise to the hairpin precursor (pre-miRNA)11, SNPs have been shown to profoundly influence a gene function and regulation12. MiRNA genes are no exception, and numerous studies have revealed that SNPs substantially affect microRNA processing, stability, and functioning16. It is effectuated in two ways, namely via SNP polymorphisms within the sequences of target genes, and miRNA genes. SNPs occurring in a target mRNA may cause target alterations since they may lead to the gain or loss of a miRNA binding site or the creation of a binding site for a new miRNA17. Similarly, SNPs located in pre-miRNAs may create a new binding site, cause the loss of existing ones as well as create a binding site for new target genes.Single nucleotide polymorphism (SNP), one of the most common genetic variations in the genome25. For example, an SNP located in pre-miR-146a (rs2910164) was established to be associated with the expression downregulation of this miRNA21. Furthermore, findings are confirming the association of an SNP present in miR-499 (rs3746444) with various types of cancer25.Thus, the occurrence of an SNP may substantially change the interaction profile of a miRNA with its target genes, and, as a result, influence the whole downstream biological pathways that can be related with, e.g. intra-individual variation and disease susceptibility. It has been evidenced by a handful of reports showing the influence of SNPs in miRNAs and their targets on complex traits and diseases29, however, the number of such studies is very scarce. It may stem from i.a. a limited number of identified miRNA SNPs. For example, 1552 polymorphic human pre-miRNA regions were deposited in the miRNA SNiPer v. 4.0 database, whereas this number amounted to 741 and 89 in cattle and pigs, respectively30. The domestic pig is of the utmost importance in the meat production, and, what is more, it constitutes an excellent, alternate, large mammal model organism for the human because of the similarity of the development, physiology, and anatomy33. Hence, a further investigation of the variability of miRNA genes in this species not only will contribute to the development of biomarkers associated with traits variability, but also may serve as source information for comparative studies.In livestock, SNPs in miRNAs and their target genes are also found to be associated with significant production traits, such as growth, fatness, and meat traitsTo accelerate progress in this area, analyses on the genome-scale are necessary. It can be achieved by implementing high-throughput technologies such as next-generation sequencing (NGS). This method has a broad range of applications and advantages; however, it may generate substantial costs in the case of whole-genome sequencing. Therefore, we attempted to apply the targeted sequencing approach, which enables deep sequencing of chosen genomic regions of interest, to reveal SNP variability of miRNA genes in the pig genome for the first time. To this end, we used NimbleGen SeqCap EZ Enrichment System (Roche), which utilizes a tiling approach for probe design and is characterized by high enrichment efficiency. The obtained results not only provide information on the usefulness of this technology in similar studies but also allow better genomic characterization of the pig in terms of SNP-miRNA variability, and provide potential markers useful for association studies with crucial production traits. Moreover, such a comprehensive analysis of SNP characteristics of miRNA genes from the global, genomic point of view will constitute a basis for further interspecies research and potential applications in human diseases.https://www.ensembl.org/index.html) accession numbers  constituted on-target reads. The average depth of coverage was 57.07. The subsequent analyses revealed as many as 5,195 potential variants dispersed in the pig genome, including SNPs, deletions, insertions, and complex variants. They were identified on all pig chromosomes, and their number ranged from 95 on chromosome 8–743 on chromosome 15, including 13 variants assigned to unmapped contigs. Of the called variants, 81 were localized in pre-miRNA sequences , while for the pre-miRNA-located SNPs was slightly lower (0.262 (± 0.165). The observed heterozygosity was equal to 0.46 on average, across all the sequenced regions as well as across only pre-miRNA regions. All detected SNPs were physically distanced from each other by 636.7 (± 2044) kb on average, while the minimal and maximal distances ranging from 1 bp to 37.7 Mb. When it comes to the physical distance of pre-miRNA-located SNPs, it was substantially higher and amounted to 19.1 (± 43.7) Mb, whereas the minimal and maximal distances oscillated between 1 bp and 280.8 Mb. The density of SNPs per 1 kb of the whole targeted region was 41, while per 1 kb of pre-miRNA sequences was 2.4.To explore the impact of the SNPs on the hairpin secondary structure and its thermodynamical stability, we employed RNAfold software. One pre-miRNA was folded twice—the first time as a reference sequence and the second time after introducing an SNP. 53 SNPs elicited a non-zero minimal free energy (MFE) change (|ΔΔG|) ranging from 0.1 to 7.4 kcal/mol  to explore their influence on miRNA-mRNA interaction and target gene selection. The prediction was carried out for reference seeds and seeds altered with the detected SNPs. The analysis revealed a decrease in the number of potential targets from 890 to 542 for miR-9819-5p G>A and an increase from 2772 to 2999 for miR-9791-1-3p G>A  . It is worth noting that the pig genome build was updated, and a few chromosome regions were removed while many were moved to other locations after the targeted sequencing design had been completed. This change may be one of the causes underlying 10% discrepancies of the validation results, which may also arise from the continued presence of problematic, unassigned regions. Moreover, some discrepancies between the two methods’ results may stem from different principles behind the used methods (targeted sequencing based on hybridization of DNA and Sanger sequencing based on PCR) and the fact that each technique holds its own level of false-positive and false-negative results.35.As a result of the downstream analysis, 81 variants were mapped to porcine pre-miRNA sequences, of which 73 were SNPs. The analysis of their distribution showed that the lowest number of SNPs (2.7%) was localized in the miRNA seed region, responsible for target gene binding. 12 (16.5%) SNPs were called in mature miRNA sequences, beyond the seed region, while as many as 59 (80.8%) SNPs were mapped to pre-miRNA sequences beyond the mature sequence. This increasing accumulation of SNPs along with the distance from the seed region and mature sequences is in concordance with other studies and is believed to constitute the manifestation of the negative selection, because of the functional importance of the seed region in target gene recognition35. In general, the proportion of single transitions in pre-miRNA and mature miRNA sequences was similar. On the other hand, mature miRNAs showed only C>G, C>A, and G>T transversions, while also other types of transversions were observed in pre-miRNAs, which may partially stem from the higher overall number of SNPs located in pre-miRNA sequences than in mature miRNAs.We also looked closer into the identified SNPs, and analyzed the types of substitutions. We found that transitions were undeniably more common than transversions, both in pre-miRNA sequences (82% vs 18%), and mature microRNAs (71% vs 29%) Fig. . It corr34 and mouse35 genomes. Furthermore, an SNP-caused target gene switching of one microRNA, namely ssc-miR-378, was also computationally and experimentally established in the pig28.Next, we performed an in silico analysis to investigate the potential influence of the seed-located SNPs on miRNA-mRNA interactions. The analysis revealed that the introduction of the SNPs in the seed region of ssc-miR-9819-5p and ssc-miR-9791-1-3p caused a substantial change in the set of target genes. 85 target genes remained common for the reference and SNP-altered ssc-miR-9819-5p, while 805 were lost and 457 were gained , and cell metabolism . When it comes to ssc-miR-9791-1-3p, the most significant SNP-affected terms were related to i.a. cellular components organization and localization , and key cellular processes . Taking into consideration the importance of these SNP-affected biological processes in cell functioning, one may assume they may exert a substantial influence and be a source of inter-individual variability.36, and they are located in pig QTL regions associated with i.a. meat and carcass , and production (e.g. feed intake and body weight), which suggests their engagement in economically important traits in pigs (PigQTLdb)38.Nevertheless, the obtained results need further experimental validation to empirically shed more light on the functional implications of the SNP-caused alterations, especially since the exact functions of ssc-miR-9819-5p and ssc-miR-9791-1-3p have not been deciphered so far. The expression of these miRNAs has been detected in porcine milk exosomes34. To address this issue, we carried out the analysis of thermodynamical stability of the reference and SNP-altered pre-miRNAs with RNAfold software.Most of the SNPs identified in this study are located beyond the seed region, which means that they do not exert direct influence on target gene selection via canonical binding. However, because they may influence the secondary structure of pre-miRNAs, they may have a substantial impact on the biogenesis of miRNAs34 and mice 35. It may result from the fact that the aforementioned research teams calculated this mean energy change on the basis of all available wide-genome datasets from multiple studies.The analysis showed that 12 out of 65 identified SNPs do not cause any changes in the minimal free energy, meaning that they possibly do not change the stability of pre-miRNAs. 24 SNPs increased the MFE; thus, they have the potential to decrease the stability of the hairpins, while 29 SNPs decreased the MFE, which means they may stabilize the secondary structures. The average energy change of a secondary structure was 1.51 kcal/mol which is slightly lower from that established in humans 34. Therefore, identified in this study, energy changes may strongly affect pre-miRNA stability and miRNA production; however, only experimental validation will provide the ultimate evidence.It is worth to note that Gong and colleagues attempted to summarize the available research in this filed and stated that energy changes > 2.0 kcal/mol may significantly influence the mature miRNA production, but even energy changes below this threshold were observed to impact the miRNA biogenesisMoreover, our thermodynamical stability analysis revealed that 30 of the analyzed SNPs caused visible changes of the hairpin secondary structure, even without minimal free energy changes. It may be exemplified by ssc-miR-7141-5p, in which C>A SNP in the mature sequence created a small bulge and increased the MFE by 5.8 kcal/mol Fig. .39. Their analysis implicated that an SNP located in pri-miR-126 decreases the efficiency of further processing to the miRNA because secondary structure alterations affect Drosha cleavage. Similar findings were reported by Jazdzewski and colleagues, who showed that a common pri-miR-146a polymorphism influences pri-miR processing and protein binding to the pre-miR product20. Additionally, the research focused on the porcine miR-15b revealed that an SNP located in its pre-miR sequence exerts a profound influence, starting from pre-miR-15b processing, thermodynamical stability, secondary structure, through strand selection to the final level of expression40.Further functional research is needed to confirm whether these predicted secondary structure changes have an impact on the miRNA biogenesis in the pig. However, they seem to have the potential to exert such an influence, especially in the light of the previous findings. Changes of the secondary structure introduced by an SNP were also reported by Harnprasopwat and colleagues, who investigated the human pri-miR-126The conducted research not only tested the potential of targeted sequencing method for applications such as SNPs discovery in regions of interest, in this case microRNA genes, but also better characterized miRNA genes in terms of SNPs in the pig, and, at the same time, broadened the available repertoire of miRNA-localized SNPs. Thus, identified SNPs can be successfully used in future association studies. Moreover, the obtained results may facilitate pinpointing interesting miRNAs, which may become subjects of more extensive research, also those focused on associations with functional traits. It will reduce the cost of searching them de novo.30, the number of pre-miRNA polymorphic regions amounted to 1532 in human, 741 in cattle and as little as 89 in pigs (among which only 30 concerned mature miRNA sequences and seven embraced seed regions). Thus, broadening the knowledge in this field is essential and still a need, and our results with its 30 newly identified miRNA-SNPs make a substantial contribution, especially when taking into consideration the importance of miRNAs in cell functioning as well as their role as biomarkers. On the other hand, the identification of known SNPs constitutes additional confirmation and validation of the available data; especially since the used targeted method allowed obtaining very high read coverage, which very often cannot be achieved by whole genome sequencing at relatively low cost. Moreover, it also is crucial to broaden the SNP repertoire in as many different breeds as possible since SNPs’ occurrence differs between breeds, which also is beneficial for the state of knowledge and future association studies.Large scale studies that is those investigating large research groups and large number of regions or even the whole genomes are very time- and cost-consuming. Therefore, studies on large groups are very often focused on only a few regions, so the number of identified SNPs is not very high; or concern species that are much more frequently and extensively studied, such as mice or humans. This is why livestock species, namely the pig, still stay behind when it comes to the number of miRNA localized SNPs; for example according to Zorc et al.Altogether, the obtained results and analyses imply a great potential of SNPs to exert a multifaceted influence on the miRNA processing in the pig, which stays in agreement with the world-wide findings in different species. Furthermore, apart from broadening the available repertoire of porcine SNPs, they give a stimulus for further functional, extended research. To the best of our knowledge, we also report for the first time the use of the targeted sequencing approach for miRNA-genes focused studies. This technology appears as an interesting alternative for the whole genome sequencing, especially when regions of interest are limited.The research material consisted of whole blood sampled of 23 pigs. The pigs belonging to the Polish synthetic line 990, which is a hybrid of several breeds , were maintained and slaughtered at the Pig Testing Station of the National Research Institute of Animal Production in Pawłowice under the same housing and feeding conditions. The approval of Animal Ethics Committee was not required because meat from slaughtered animals is standard intended for consumption.42. The total length of targeted regions was 109,204 bp, while the estimated coverage by the designed probes was 105,628 bp (96.7%). The single probe length was 100 bp, while the mean size of a captured region was 240 bp. Detailed data on the localization of the targeted regions are in the Supplementary File First, gDNA was extracted with the use of the QuickGene-Mini80 Nucleic Acid Isolation System (Kurabo) and QuickGene DNA Whole Blood Kit S (Kurabo), following the manufacturer protocols. The concentration and purity of the obtained isolates were assessed using the Qubit HS dsDNA Assay Kit (Thermo Fisher Scientific) and a Qubit 2.0 Fluorometer (Thermo Fisher Scientific), as well as a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). Next, a custom targeted sequencing design was developed using SeqCap EZ Enrichment System provided by Roche Sequencing Solutions (Roche). The design and capture probe panel were prepared by Roche based on the genome coordinates of 381 pig pre-microRNA sequences deposited in miRBase (21.0)All targeted sequencing procedures were carried out following the SeqCap EZ HyperCap KAPA HyperPrep Workflow, including the double capture step and the multiplexing option. In brief, 700 ng of input gDNA for each sample was mechanically sheared with M220 Focused-ultrasonicator (Covaris) to obtain the average fragment size of 180–220 bp. The samples were ligated with adapters and subjected to dual size selection to get fragments between 250 and 450 bp. After PCR amplification and AMPureXp clean-up (Beckman Coulter), the libraries were pooled and hybridized for 16 h with the set of the custom-designed probes. The captured DNA was PCR amplified and cleaned up, and then subjected to the second hybridization, which was followed by the last PCR amplification and clean-up. Obtained libraries were checked for quantity and quality with the use of a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), and a TapeStation 2200 system (Agilent). Finally, they were clustered on an Illumina Flowcell_v3 in a cBot cluster station and sequenced using the HiScan SQ (Illumina) system applying 94 cycles of single-end sequencing.43. After quality control, the reads were filtered with Flexbar software44 by removing adapters, and reads of phread quality below 30, while the minimal read length was set to 50. Then, the mapping procedure was maintained with the use of BWA software45 followed by applying read groups to individual samples (Picard software46). After this step, the duplicate reads were marked (Picard software). The next step was the variant calling procedure with the use of Freebayes software47. After obtaining the raw vcf file, a series of filtering steps was applied with the use of VCFtools , minimal genotype quality > 30)48. The obtained data were submitted to European Variation Archive (EVA) database (project PRJEB43246).Firstly, the quality of the raw reads was assessed with the FastQC software49 to fold their secondary structures with and without the called SNPs, and calculate minimum free energy (MFE)50. The identification of target genes for miRNAs was performed using miRNAconsTarget tool (Miranda parameters) implemented in the sRNAtoolbox webserver and proving consensus target predictions52. The subsequent analysis of gene ontology enrichment was carried out with ShinyGO (v0.61) online tool applying the FDR correction for multiple testing53.Pre-miRNA sequences identified in this study to have SNPs were analyzed with RNAfold software implemented in ViennaRNA package (version 2.4.13)54 to embrace 33 SNPs distributed in 20 genomic regions (Supplementary File Primer pairs were designed using Primer3 software (v.4.1.0)Supplementary Legends.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


KLK3 SNPs, and 1083 (P < 3.5 × 10–9) and 3145 (P < 1 × 10–5) SNP–SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene–gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP–SNP interactions were supported by gene expression and protein–protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP–SNP interactions in the four pathways  associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3  Because of the substantial clinical heterogeneity of this disease, physicians often have difficulty distinguishing at the time of diagnosis between patients who will develop indolent tumors and those who will develop aggressive PCa2. For PCa patients considered to be at a low risk for aggressive PCa, conservative management and treatment are presently recommended. However, ~ 20% of PCa patients who are classified as a low risk using the known classification features  still die during conservative treatment3. This demonstrates an unmet need to identify better biomarkers for predicting PCa aggressiveness.Prostate cancer (PCa) accounts for over 10% of all cancer-related deaths, making it the second leading cause of cancer-related deaths among American men in 20214. These 41 SNPs are located across 53 genes (some are intergenic SNPs), including KLK3, ADGRG1, ARHGAP6, CASC8, and TCF44. We and others reported several polygenic risk scores (PRS) for PCa risk based on multiple individual SNP effects7, but the prediction model of PCa aggressiveness remains underdeveloped.Genetic association studies have primarily focused on the effects of individual single-nucleotide polymorphisms (SNPs), which are insufficient to explain the complexity of disease susceptibility. However, the majority of SNPs identified by genome-wide association studies (GWAS) are for PCa risk, and only a few are for PCa progression. In fact, only 41 SNPs have been suggested to be associated with PCa progression related phenotypes  in GWAS from the GWAS catalog8. Although SNP–SNP interactions have received more attention in the past decade, few have been validated, and most are without known biological functions. The limited SNP–SNP interaction findings may be due to insufficient statistical methods. The conventional approach for testing 2-way SNP–SNP interactions is the Additive–Additive full interaction (AA_Full) approach, the full or hierarchical interaction model (2 main effects + interaction) with the additive SNP inherited mode. AA_Full is the most complicated interaction pattern with the 9 distinct risk-groups, so a large sample size will be needed for detecting this complicated pattern. Using AA_Full to detect SNP–SNP interactions tends to lead to false-negative findings because it only tested one complicated interaction pattern. In order to overcome this issue, we developed two statistical methods: SNP Interaction Pattern Identifier (SIPI) and Additive-Additive 9 Interaction-Model approach (AA9int)10. The AA9int approach, which treats all SNPs in an additive inheritance mode, tests nine interaction patterns of pairwise SNP–SNP interactions associated with an outcome10. The SIPI approach is an extended version of AA9int and tests 45 biologically meaningful SNP–SNP interaction patterns by considering three SNP inheritance modes 9.It has been established that gene–gene/SNP–SNP interactions may play a larger role in the causality of complex diseases14. Androgen expression, epigenetic factors, and oxygen levels in the tumor microenvironment regulate angiogenesis, which leads to metastatic PCa16. Therapies targeting both angiogenesis and androgen for recurrent PCa patients could increase survival rates17. These findings suggest that the relationship between androgen and angiogenesis and how their interaction can impact on PCa aggressiveness. Genes involved in androgen metabolism pathways also lead to oncogenic metabolic phenotypes, such as mitochondrial respiration and cell proliferation, in PCa cells. In addition, androgen repression in PCa cells decreases mitochondrial activity18. We and others have reported that miRNAs  are involved in regulating various aspects of angiogenesis and PCa progression19. This evidence suggests that the interplay among genes in these 4 pathways may impact PCa aggressiveness.Accumulating evidence suggests that interplay among angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways may play a critical role in PCaint-PRS) associated with PCa aggressiveness. These SNPs were selected based on genes in the four PCa biological related pathways .This study was inspired by the shortage of SNP findings associated with PCa aggressiveness. Only a small number of SNPs were identified to be associated with PCa aggressiveness, and there is no PRS for PCa aggressiveness. In addition, most of the PRS is based on the sum of several individual SNP effects without considering SNP–SNP interactions. Because of the inspiration, this study's objective is to identify SNP–SNP/gene–gene interactions and to build a SNP-interaction based PRS  with a P < 0.001 in both study sets, and all of these three SNPs reached the Bonferroni significance level (P < 1.9 × 10–6) in the combined set. The best inheritance mode for these 3 SNPs was an additive mode. PCa cases with the minor G allele in rs17632542 tended to have a higher risk of PCa aggressiveness . Cases with the minor A allele in rs62113212  and rs2569735  also had a higher risk of PCa aggressiveness. The relationships for these KLK3 SNPs are shown in Supplementary Fig. 2 = 0.98), and the association between rs17632542 and rs2569735 was moderate (LD r2 = 0.42).The process of identifying the significant effects of individual SNPs and SNP–SNP interactions associated with PCa aggressiveness is described in Fig. 7 SNP pairs associated with PCa aggressiveness, only one pair (rs390993 + rs473640 in RIPK2 and NOS1) qualified the selection criteria: P < 0.001 in the discovery, validation, and combined sets  satisfied the validation criteria. The interaction p-values of rs390993 + rs473640 were 4.5 × 10–5, 9.2 × 10–4, and 2.8 × 10–7 for the discovery, validation, and combined sets. The interaction patterns for this SNP pair were shown in Supplementary Fig. By applying the AA_Full approach for 1.4 × 107 SNP pairs using the AA9int approach, 66,619 pairs had a P < 0.001. Then, we applied SIPI for further interaction analyses and found 66,321 pairs with a P < 0.001 in the discovery set, of which 11,449 pairs were validated. In the combined set, 9492 pairs were promising because they had a P < 1 × 10–5, which was the criterion we selected based on the plot of the − log10 P values in Supplementary Fig. P < 3.5 × 10–9). Among 3795 SNP pairs, 1083 pairs’ interaction had a lower p-value than their 2 constituent SNP effects. Of the 9492 promising pairs, 3144 SNP interaction pairs were more significant than their constituent SNPs. All 1083 SNP pairs and 98.6% of the top 3144 promising pairs associated with PCa aggressiveness were involved with KLK3 SNPs. Among the top 3144 SNP pairs associated with PCa aggressiveness, 4 KLK3 clusters contributed 91% of these pairs (n = 2856/3144). These 4 KLK3 clusters were rs17632542 (769 pairs), rs2569735 (1132 pairs), rs1058205 (601 pairs), and rs174776 (350 pairs). The most significant 5 SNP pairs within these 4 KLK3 clusters are listed in Table KLK3 pairs with P < 1 × 10–6 are listed in Supplementary Table We applied the 2-stage Additive–Additive 9 interaction model approach and SNP Interaction Pattern Identifier (AA9int + SIPI) approach to search for SNP–SNP interactions associated with PCa aggressiveness in the discovery set to reduce computation burden and maintain prediction power Fig. . When teCYB5B: LOC105371325 and KLK3, with a P value of 4.6 × 10–16 in the combined set, 2.2 × 10–8 in the discovery set, and 3.1 × 10–9 in the validation set. As shown in Table AA_int_ro, which indicates the additive–additive interaction-only pattern with the reverse and original mode for the first and second SNPs, respectively. PCa cases with the major allele G in rs4783709 and the minor allele G in rs17632542 in KLK3 had a higher risk of PCa aggressiveness  in the combined set. In the SNP pair of rs4783709 + rs17632542, the prevalence of PCa aggressiveness was 20%, 26%, 33%, and 41% for PCa cases with the genotype combinations of AA + AA, GA + AG, GG/AG, and GG + GG, respectively, with a respective coding of 0, 1, 2, and 4. The rs2569735 + rs4802754 (2 KLK3 SNP interactions) pair in the combined set  compared with the group with other genotypes for this SNP pair. As shown in Fig. GA/AA + GG group in the SNP pair of rs2569735 + rs4802754. Although the validation set had a different interaction pattern of AA_int_or compared with the discovery and combined sets, their trends were similar.The most significant SNP pair was rs4783709 + rs17632542 in M1_int or M2_int). Some examples of these complicated interaction patterns are listed in Table HAND2 + rs174776 in KLK3, which was associated with PCa aggressiveness, was AA_M1_int_o1. This is an additive–additive model plus the main effect of rs10520259 (rs10520259 + rs10520259 × rs174776) with an original mode for both SNPs. Among PCa cases with the rs174776 GG genotype, an A allele of rs10520259 was associated with a lower risk of PCa aggressiveness . For PCa cases with the rs174776 GA/AA genotypes, the rs10520259 A allele had an opposite effect .The majority of the identified SNP interactions are interaction-only models with only 1 interaction term in each model; only a few SNP interactions had a complicated model with > 1 terms  for the binary-mode interaction models and  for the AA models. For AA models, there were 20 pairs with OR ≥ 1.3, and the top 5 pairs with OR ≥ 1.35 are shown in Table KLK3 rs17632542.There were 8 significant SNP pairs with a large effect size (OR ≥ 2.00) associated with PCa aggressiveness for the binary-mode interaction models, including the RR_int_oo pattern, a recessive–recessive interaction-only pattern with an original mode. The interaction pattern in the discovery set was not the same but had a similar trend. Though the prevalence of aggressive PCa for the entire cohort was 23%  in the combined set. Another example of an SNP pair with a large effect size was rs1250240 + rs174776 (FN1 + KLK3) with the DR_int_ro pattern, a dominant–recessive interaction with a reverse mode in the validation and combined sets . The interaction pattern of this pair was similar in the discovery set. As shown in Fig. As shown in Table ITGB3 + MTCYBP42) with the DR_M2_int_o2 pattern, a dominant–recessive interaction with the main effect of the second SNP (rs7802277) and an original mode for both SNPs. As shown in Table AG/GG + AA genotype  and only 16% for cases with the AA + AA genotype , whereas the overall prevalence of aggressive PCa was 23% in the combined set. In addition, there were 88 SNP pairs associated with PCa aggressiveness, with a medium effect size (0.5 ≤ OR < 0.67 or 1.5 ≤ OR < 2) for SNP pairs with binary modes.The pair with the second largest effect size was rs2317676 + rs7802277  + rs7446 (KPNA3), PCa cases with the CA/AA + AG/AA genotype had a higher risk of PCa aggressiveness  compared to cases with other genotypes for this SNP pair. The SNP pair with the largest effect size was rs2317676 (ITGB3) + rs7802277 (MTCYBP42).Only 43 SNP pairs among the top 3144 SIPI identified pairs did not involve A–SNPB showed up in both cluster A and cluster B), and so we dropped the duplicated pairs for model building. Nine of these 11 clusters were involved with KLK3. By deleting the highly correlated SNP pairs and performing variable selection associated with PCa aggressiveness within each cluster . After applying stepwise selection for pairs associated with PCa aggressiveness with P < 0.1 as the selection criteria within a cluster, only 14 SNPs pairs were selected. The number of the selected pairs for other clusters are listed in Supplementary Table Among the 3144 candidate pairs selected using the AA9int + SIPI approach, the majority were in the 11 clusters identified in Supplementary Table P < 0.01 and P < 1 × 10–5, respectively. We evaluated the 24-pair model by adding 1 SNP pair (rs390993 + rs473640 in RIPK2 and NOS1) selected using the AA_Full approach to the list of 23 SNP pairs. The 24-pair model comprised interactions between 42 SNPs . The AUC values for the 24-pair model, 12-pair model and individual-effect model were 0.696, 0.687, and 0.668, respectively. The 24-pair model performed significantly better than the individual-effect model with rs17632542 and the 12-pair model . We also performed an internal validation using the bootstrap approach. The AUC’s 95% confidence interval for the 24-pair interaction model was 0.686–0.702 based on 1000 bootstrap runs.For the individual-effect model, only rs17632542 was selected based on the stepwise selection with a significance level of 1 × 10P value < 1.1 × 10–8 (Bonferroni criteria = 0.05/4.5 × 106). The distribution of − log10-transformed P values for the eQTL tests is shown in Supplementary Fig. KLK3 SNPs , and 16 tests involved SNP pairs within the top 3144 pairs. One SNP in FHIT, rs995633, interacted with 2 KLK3 SNPs (rs1058205 and rs2569735), which link to SLC25A21 expression. In addition, the genotype combinations of rs7224135 (CAVIN1) + rs174776 (KLK3) had a significant effect on SRD5A2 expression . These promising eQTL results support that the KLK3 SNP pairs may have an impact on PCa aggressiveness by altering specific gene expression.To evaluate the biological functions of the identified SNP pairs, we performed 2-way expression quantitative trait loci (eQTL) tests, which evaluated associations between 1 identified SNP pair (2 SNPs) and 1 gene expression in the 4 target pathways. The linear model-based AA9int approach was applied. As shown in Supplementary Table KLK3 and were associated with PCa aggressiveness. As shown in Fig. KLK3-COL4A1: COL4A2, KLK3-CDH13, KLK3-TGFBR3, KLK3-EGFR, KLK3-FGFR2, and KLK3-PRKCA. We applied the STRING database (https://string-db.org/)21 to analyze the gene–gene (protein–protein) interaction network. We also added 2 genes (FN1 and the androgen receptor [AR]) based on our literature review. Among the top 3145 pairs, there were 27 pairs for KLK3-FN1 and 4 pairs for KLK3-AR. This gene–gene interaction network supports the idea that the majority of our identified SNP–SNP interactions are directly or indirectly linked with KLK3. KLK3 had a direct link with FN1 and AR, which both link to EGFR. EGFR is also the hub for this gene–gene interaction network. For pathway-level analyses, the summary of the within- or between-pathway interactions for the top 3145 SNP pairs is shown in Supplementary Table KLK3 in the androgen pathway. This explains why the androgen pathway is the hub for pathway–pathway interactions.For gene-level interactions, there were 6 common genes with > 50 SNP pairs from the top 3145 SNP pairs that interacted with KLK3 SNPs  and 3145 SNP interaction pairs that were associated with PCa aggressiveness. The KLK3 SNP rs17632542, which is in a strong LD with rs62113212, had been previously identified in GWAS as being associated with several PCa-related outcomes, such as the patient’s PSA level, PCa risk, and age at PCa diagnosis and the tumor’s volume, aggressiveness, and Gleason score22. The SNP rs2569735 has been shown to influence miRNA functions23. The SNP rs2735839, which has a strong LD with rs2569735, is associated with PCa aggressiveness24.Our study identified 3 KLK3, have been previously implicated in PCa risk and progression25. KLK3 is a protein-coding gene on chromosome 19q13.4, and its protein product, PSA, is a serine protease exclusively secreted by the prostate gland into the seminal fluid, where it plays an important functional role in the normal physiology of the prostate26. PSA is not normally secreted into the bloodstream; therefore, serum PSA levels are used for screening, diagnosing, and prognosticating PCa27.The prostate is an androgen-dependent organ, and SNPs in genes involved in the androgen metabolism pathway, such as KLK3 individual SNPs rs17632542, rs2569735, and rs1058205 were significantly associated with PCa aggressiveness and PSA level. KLK3 was involved in 98.6% of the top 3145 SNP pairs associated with PCa aggressiveness; the 4 KLK3 SNPs  contributed to 90.8% (2856 pairs) of the identified SNP pairs. Interestingly, the 2 most common SNPs (rs17632542 and rs2569735), which interacted with many other SNPs, also had significant individual effects. According to the quality-controlled gene–gene (protein–protein) association network  transition process, and COL4A1 gene expression has been associated with the Gleason score36. COL4A2 provides a structural component for basement membranes, is known as an inhibitor of angiogenesis, and has been considered as a biomarker for screening for benign prostatic hyperplasia37.As shown in Fig. CDH13 is located on chromosome 16q24 and is a well-known tumor suppressor gene involved in cell–cell adhesion. The expression of CDH13 has been associated with poor PCa prognosis and low proliferation rates of prostate tumor cells38. TGFBR3 is one of TGF-β receptors and is abundantly expressed in PCa cells. TGFBR3 is downregulated during the transition from benign to malignant and metastatic prostate tissues, especially in bone metastases39. Several studies consistently report downregulation of TGFBR3 in prostate tumors, suggesting that it has an important role as a tumor suppressor gene41.KLK3 can attenuate the response to FGF2, an angiogenesis-stimulating factor that binds to FGFR2 and reduces endothelial cell proliferation and migration, indicating FGF2 suppressive effect during metastasis42. PRKCA is a key negative regulator of the TGF-β pathway and is downregulated in PCa tumors. Cell migration, invasion, and bone metastasis are suppressed by overexpression of PRKCA. Further, expression of PRKCA has been correlated with clinical variables, such as PSA levels, Gleason score, and metastasis status of patients with PCa39.KLK3 SNP pairs, KPNA3, with 21 SNP pairs, was the most common gene, and there were 5 pairs for KPNA3-AR interaction. KPNA3 is one of the subunits of the nuclear pore complex and plays a role in nuclear protein import43. In addition, the expression of KPNA3 was inversely associated with survival. Baker et al. demonstrated that KPNA3 knockdown inhibited cell proliferation, migration, and invasion. KPNA3 may regulate protein transfer to promote colorectal cancer growth, metastasis, and relapse43.For the non-RIPK2 + rs473640 in NOS1) using the AA_Full model. The receptor-interacting serine/threonine-protein kinase 2 (RIPK2) was one of the key regulators of the immune response44. NO synthases (NOS) generate nitric oxide (NO), which can regulate tumorigenesis. Increased nitric oxide level by NOS is cytotoxic to cancer cells45. NOS1 downregulation reduced the growth of chemokine expressing fibroblasts and their ability to promote tumor formation in prostate cancer cells46. Although these genes' role has not been studied in PCa aggressiveness, RIPK2 polymorphisms were associated with risk of gastric47 and breast48 cancers. NOS1 polymorphisms were associated with the risk of various cancers, such as pancreatic49, glioma50, colorectal cancer, and melanoma51.We identified one additional SNP pair  do not have a link to KLK3 according to the protein–protein interaction network , the prevalence of aggressive PCa cases from our cohort was 49.8%, 21.9%, and 7.0% with top 1%, middle 50%, and bottom 1% risk profiles, respectively. We observed that SNP pairs in the same cluster tended to be highly correlated. This may explain why we identified > 9000 significant SNP pairs. By dropping the highly correlated pairs within a cluster, we effectively reduced variable dimensions for building a multi-pair prediction model. Further biological studies will be needed to distinguish driver or passenger effects for the identified SNP pairs.Importantly, our study demonstrated that SNP–SNP interactions could explain PCa aggressiveness better than individual SNP effects focusing on genes from the 4 selected pathways. Our study also showed that the 2-stage AA9int + SIPI approach is a powerful tool for identifying and validating SNP–SNP interactions associated with a selected phenotype. The AA9int + SIPI approach is powerful because it allows genotype subgroups with similar risk profiles or small sample sizes combined. Our risk classification system based on the model with 24 validated SNP interaction pairs is promising. Using this SNPKLK3), especially androgen-angiogenesis (49.1%) and androgen-mitochondria pathway interactions .This study included 20 270 PCa cases (22.6% of which were aggressive PCa) with European ancestry from 21 studies within the Collaborative Oncological Gene-Environment Study (COGS) in the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) Consortium. Details of the PRACTICAL Consortium study have been previously reported35, the literature, and PCa GWAS. These SNPs were genotyped on a custom iCOGS Illumina array  using blood DNA samples. SNPs were excluded if they had a call rate < 95%, a call rate < 99% with minor allele frequency (MAF) < 5%, MAF < 1%, or if their genotype frequencies departed from Hardy–Weinberg equilibrium at P < 1 × 10−12.We evaluated 8587 SNPs from 2122 genes within 4 pathways: the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways. These pathways were selected based on our previous studies20. For evaluating individual SNP effects, we considered three different inheritance modes: dominant [D], recessive [R], and additive [A] modes, which were assigned based on the minor allele. Logistic models were applied, and the best mode with the lowest P value was selected for each SNP.The effects of individual SNPs and 2-way SNP–SNP interactions on the outcome of PCa aggressiveness were evaluated. The analyses were conducted separately for the discovery set and validation set. To control for population substructure, the principal component analysis was performed. All models were adjusted for the study site and the first 6 principal components of population stratification as suggested by the PRACTICAL study2. In order to avoid multicollinearity in modeling, we tested correlations of neighborhood SNPs within 100 kilobases using PLINK. SNPs with a strong LD of r2 > 0.8 were excluded from interaction analyses. A total of 5345 SNPs with a MAF > 0.05 and no strong LD (r2 < 0.8) were included for SNP–SNP interaction analyses.We assessed 2-way SNP–SNP interactions. LD among all testing SNPs was examined based on r10. The SIPI approach is an extended version of AA9int and tests 45 biologically meaningful SNP–SNP interaction patterns by considering 2 more inheritance modes (dominant and recessive)9. Thus, SIPI’s test patterns include additive–additive (AA_), dominant–dominant (DD_), dominant–recessive (DR_), recessive–dominant (RD_), and recessive–recessive (RR_) models. The AA9int and SIPI models are composed of the full-interaction model (both main effects plus interaction), the models with 1 main effect and 1 interaction, and models with interaction only. The corresponding patterns’ names are Full, M1_int, M2_int, or Int_, which represent various types of interactions with 3, 2, 2, and 1 term(s) in the statistical model equation, respectively (see the equations below). For easy interpretation, these SIPI interaction patterns can be shown as the 3 × 3 table with a heat table format using the plot3by3 function in the SIPI R package10.For SNP–SNP interactions, we applied the conventional AA_Full approach using PLINK in the discovery, validation and combined sets. In addition, we applied a powerful 2-stage AA9int + SIPI approach to search intensively for SNP–SNP interactions associated with PCa aggressiveness. The AA9int approach, which treats all SNPs as having an additive inheritance mode (additive–additive patterns [AA_]), tested 9 interaction patterns of pairwise SNP–SNP interactions associated with an outcome10. We considered the original  and reverse coding directions. For the binary outcome of PCa aggressiveness, the logistic-based AA9int was applied, and the best model was selected based on the lowest BIC value for each SNP pair. For a large-scale study, the 2-stage AA9int + SIPI approach is suggested. For performing AA9int and SIPI analyses, we used the ‘parAA9int’ and ‘parSIPI’ functions in the SIPI R package in this study. There are four related functions in the SIPI R package: SIPI, parSIPI, AA9int, and parAA9int. The parSIPI and parAA9int functions are parallel computing versions of SIPI and AA9int, which can be used for large-scale studies. We applied logistic-based AA9int and SIPI. The details of SIPI and AA9int parameter settings are listed in the SIPI R package manual. The SIPI R package and its manual are freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).The SNP pairs with binary modes (such as DD and RR) categorize the subjects into 2, 3, and 4 distinct risk groups for interaction-only, main + interaction, and full-interaction models, respectively. For AA models, the coding for an SNP with the additive mode is 0, 1, or 2 for the number of the target alleles, so the coding of the interaction term of 2 additive SNPs (SNP1 × SNP2) is 0, 1, 2, or 4. The details of these 2 methods were described previously7 candidate SNP pairs associated with PCa aggressiveness. Promising SNP pairs with P < 0.001 in the screening stage were further evaluated using the SIPI approach. The SNP pairs in the discovery set with P < 0.001 were further validated in the validation set. We defined significant results using P < 0.001 in both the discovery and validation sets and a P value less than the significance level of Bonferroni correction . As it is well-known that Bonferroni correction is too stringent, we also used a P < 1 × 10–5 as the cutoff to select promising SNP–SNP interaction pairs. For testing whether an SNP interaction pair performed better than their composite individual SNP effects, we also compared the P-value of an SNP pair with P values of their composite individual SNP effects for SNP pairs with an interaction-only pattern. For SNP pairs with > 1 terms, the stepwise selection within a pair was applied. The methods used for SNPint-PRS, comparison of model performance, eQTL analyses, and the gene–gene interaction networks are listed in As shown in Fig. Supplementary Information.


Gavia immer), an iconic migratory aquatic bird that is under increasing threat on both its breeding and wintering areas. Using 300 samples collected from across the breeding range, we develop a panel of 158 single‐nucleotide polymorphisms (SNP) loci with divergent allele frequencies across six genetic subpopulations. We use this SNP panel to identify the breeding grounds for 142 live nonbreeding individuals and carcasses. For example, genetic assignment of loons sampled during botulism outbreaks in parts of the Great Lakes provides evidence for the significant role the lakes play as migratory stopover areas for loons that breed across wide swaths of Canada, and highlights the vulnerability of a large segment of the breeding population to botulism outbreaks that are occurring in the Great Lakes with increasing frequency. Our results illustrate that the use of SNP panels to identify breeding origins of carcasses collected during the nonbreeding season can improve our understanding of the population‐specific impacts of mortality from disease and anthropogenic stressors, ultimately allowing more effective management.Understanding how risk factors affect populations across their annual cycle is a major challenge for conserving migratory birds. For example, disease outbreaks may happen on the breeding grounds, the wintering grounds, or during migration and are expected to accelerate under climate change. The ability to identify the geographic origins of impacted individuals, especially outside of breeding areas, might make it possible to predict demographic trends and inform conservation decision‐making. However, such an effort is made more challenging by the degraded state of carcasses and resulting low quality of DNA available. Here, we describe a rapid and low‐cost approach for identifying the origins of birds sampled across their annual cycle that is robust even when DNA quality is poor. We illustrate the approach in the common loon ( Samples for genetic analysis included blood samples collected from birds captured in known breeding areas across North America during the breeding season, and samples collected from wintering or migratory birds, including the following: (1) feathers from bird carcasses resulting from botulism outbreaks in the Great Lakes or collisions at solar facilities in southern California, (2) blood or feathers collected from birds that were moribund or dead from mostly unknown causes from Florida and the northeastern seaboard, and (3) blood from living, healthy birds from Morro Bay, California, and coastal Louisiana. For loons that were sampled on the Great Lakes, which serve primarily as stopover sites, we used both collection date and location on the lakes to assess whether birds were breeding or migrating . Tissue samples and previously extracted DNA were sent to UCLA for library preparation and/or SNP genotyping. Samples were transferred to UCLA under a protocol approved by the Research Safety and Animal Welfare Administration, University of California Los Angeles: ARC# 2017‐073‐03, approved 10/28/2017.2.2Qiagen's DNeasy 96 blood and tissue kit (Qiagen Inc.) was used to extract DNA from each sample. The amount of starting material varied by sample type: we used between 10 and 50 µl of blood for whole blood, 10 mg of sample for tissues and at least one calamus for feathers. Each sample was incubated overnight at 56°C in 20 µl Proteinase K and 180 µl of lysis buffer. To break down the keratin in feathers, 10 µl of Dithiothreitol (DTT) was also added to the lysis mix. All samples were eluted the next day per the manufacturer's protocol, yielding a final elution of 120 µl of DNA solution per feather sample and 200 µl per blood or tissue sample.2.3In a preliminary effort to characterize population structure, we employed a double‐digest restriction site‐associated (ddRAD) sequencing method detailed in  genome assembly  sequencing to generate additional markers. RAD‐PE libraries were constructed at UCLA following Ruegg et al.  using thgenoscapeRtools , a R software program that visualizes the tradeoff between discarding SNPs with low coverage and discarding individuals with missing genotypes. Here, we discarded low quality SNPs and low coverage individuals for a final set of variants with <20% missing genotypes per SNP and <25% missing data per individual. This final set was used to assess population structure and develop a downstream SNP panel for population assignment.We called SNPs using the GATK 4.9.3 Haplotype Caller . We assessed isolation by distance among 16 sampling regions, regressing pairwise FST values on the geographic distance between the centroids of each region using a Mantel test in the vegan package , we used custom R scripts to identify a set of 259 SNPs with the largest pairwise allele frequency differences among the conservation units identified by the analysis of population structure described above Figures . From thThe Fluidigm Corporation EP1™ Genotyping System was used to screen 452 individuals at 192 SNPs in batches of 94 individuals per run with two non‐template controls. To ensure amplification of low quality or low concentration DNA from feathers, an initial preamplification step , where K ranged from 1:7  and clipped to the common loon breeding range using a shapefile  as set by BBS. We further filtered to include only those surveys for which both starting and ending wind conditions were <=2 on the Beaufort scale and for which both starting and ending sky conditions were <=2 . Note that inclusion of surveys conducted in poorer but acceptable weather conditions did not alter abundance trends significantly.We used Breeding Bird Survey (BBS) data  and RAD‐PE (204) sequencing and Fluidigm assays (452). The samples were of several types: blood (594), blood dots (72), feathers (100), and other tissues . Samples from breeding birds (560) were from blood (497) or blood dots (63). Wintering samples (252) came from the remaining blood (97) and blood dot (9) samples and from the 146 feather and tissue samples listed above.3.1In our initial ddRAD sequencing effort, 106 breeding individuals and 1852 SNPs passed our quality and minor allele frequency (MAF > 0.01) filters. RAD‐PE sequencing of 204 individuals from across the breeding range resulted in 180,198 SNPs passing our initial filters. After filtering for MAF > 0.03, relatedness, and additional missingness thresholds, our final dataset included 39,912 variants in 129 individuals. Most of the individuals removed from the RAD‐PE sample set were removed due to exceeding our threshold for missing genotypes. Twenty‐one individuals were removed due to relatedness, 11 of which had at least one first degree relative in the sample. Ten of the individuals removed due to relatedness were from the New England conservation unit, eight from the Midwest, two from central Canada, and one from the Pacific Northwest unit. Of the 200 additional breeding individuals genotyped using Fluidigm SNPtype assays, 173 passed filtering. Combining these 173 with the 127 RAD‐PE samples that passed filtering for the sites used in the Fluidigm assay gave us 300 individuals with which to define conservation units, build the spatial map, and conduct STRUCTURE analysis. Of 252 nonbreeding individuals genotyped using Fluidigm SNPtype assays, 164 passed filtering and could be used to perform assignments. All of the breeding loons were sampled while alive; however, the wintering loons were a mix of living birds and carcasses. We were able to genotype 88% of samples from living birds and 49% of the samples we received from common loon carcasses.3.2Our analyses rely on SNPs derived from two different reduced representation (RAD) sequencing efforts. Our initial analysis based on double‐digest RAD‐seq  Figure , Table 1e Figure . Both cre Figure .We did not identify any first degree relatives (relatedness 0.25) among the sampled wintering loons. We did identify 77 pairs of individuals with relatedness scores between 0.125 and 0.196  and 1440 with relatedness scores between 0.0625 and 0.125 .3.4n = 23) were most often (n = 22) inferred to have mixed ancestry between central Canada and the Midwest. These included birds from Florida (six), Louisiana (five), Michigan (five), Ontario (three), California (one), Massachusetts (one), and Maine (one)  Table . An addie) Table .Our data also suggest a previously undocumented migratory pathway from the Midwest Figure . There i3.5Migratory and wintering birds sampled from the Great Lakes during botulism outbreaks in 1999 and 2012 were identified as being from the Midwest and central Canada conservation units Figure . Birds s4en route to or from wintering grounds and identify their conservation unit of origin enhances our ability to predict the impacts of threats beyond the breeding grounds to population trends in those units.A variety of techniques including banding, satellite tracking, and isotope analysis have been used in efforts to assess migratory connectivity , and we lacked samples of wintering birds from this area and coastal Pacific Northwest generally. The migratory behavior of Alaskan birds may mirror the short‐distance migratory behavior we observed in birds from the New England conservation unit. Our failure to detect loons migrating from the eastern Canada conservation unit is most likely because we lack samples from the region to which they are known to migrate. Banding returns indicate significant use of the North Carolina coast by loons from this conservation unit  due to climate change. In addition, our detection of a possible transcontinental migration route was only possible because this approach allows efficient and cost‐effective screening of a large number of samples. Our current analysis was limited to 158 diagnostic genetic markers, but future research will reveal the extent to which additional genetic markers and more extensive sampling would allow us to further delineate structure in the loon population and increase the resolution of assignments of birds to conservation units.The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.


Mangifera indica, a pollinator-dependent crop in South Florida, and how fruit yield is affected by either leaving weeds or removing them.There is an urgent pollinator decline crisis across the globe, with fewer pollinators and yet increasing agricultural reliance on them to produce food and fiber crops for growing populations. Habitat loss and chemical eradication of unwanted plants has limited the floral resources for pollinators, and in farms with only one crop, there are limited resources solely during the flowering season. Weeds, or unwanted vegetation, are often the only remaining floral resource for pollinators, yet they are compulsively removed using chemicals. This article examines how weedy floral resources affect pollinators in a mango farm, Mangifera indica, a tropical fruit tree dependent on insect pollination, when weeds were present in cultivation versus when they were removed mechanically. The pollinating insects on both weeds and mango trees were examined as well as fruit set and yield in both the weed-free and weedy treatment in South Florida. There were significantly more pollinators and key pollinator families on the weedy mango trees, as well as significantly greater fruit yield in the weedy treatment compared to the weed-free treatment. Utilizing weeds, especially native species, as insectary plants can help ensure sufficient pollination of mango and increase biodiversity across crop monocropping systems.Agriculture is dependent on insect pollination, yet in areas of intensive production agriculture, there is often a decline in plant and insect diversity. As native habitats and plants are replaced, often only the weeds or unwanted vegetation persist. This study compared insect diversity on mango,  Cultivated crops are often subject to insect–plant interactions for high yield. There has been a growing interest in environmentally and ecologically sound agriculture using beneficial insects rather than pesticides to produce food and fiber without harmful chemicals in produce and the environment ,2. EcoloPollinators are especially important in crops that require pollination by insects ,15, suchThere is a pollinator crisis in areas of intensive human land use and landscape simplification, including farmlands . InsectsWeeds have the potential to increase biodiversity of native pollinators by providing alternative floral resources for beneficial insects and encouraging them to remain in an area between crop flowering events ,35,36. PMangifera indica L., Anacardiaceae) [Effective insect pollination is essential for good fruit set and yield in mango (diaceae) . Mango fdiaceae) . Nectar diaceae) . Manageddiaceae)  or insufdiaceae) ,43,44; hdiaceae) ,46, and diaceae) . Pollinadiaceae) . These idiaceae)  or that diaceae) ,33.Chrysomya, Lucilia, and Musca sp. (Diptera) were reported as mango pollinators because of their visiting frequency and abundance [Bombus terrestris), and the housefly—resulted in much higher yield of mango vs. caged pollinator-free trees [The insect orders Diptera, Hymenoptera, Lepidoptera, and Coleoptera are the most common insect visitors to mango flowers and carry mango pollen ,41. Mostbundance . Flower bundance , as theybundance . The intee trees . Common ee trees .Mangifera indica) crop cultivation. We asked the following questions:How do the abundance and diversity of insect species  differ on mango in the presence or absence of weeds?What is the impact of weeds on mango fruit yield?The aim of this study was to examine if weeds can increase biodiversity of pollinators and ultimately benefit Mango  in comparison to mango trees that have been cleared of all weeds (weed-free treatment). Perhaps this enhanced pollinator abundance will increase fruit production.The field experiment was installed at a conventional mango farm (20 acres), variety “Keitt”, within the major agricultural area of Homestead, Florida (25°29′42.9″ N 80°29′30″ W). Trees were evenly spaced in 20 × 20 feet intervals. Distance between rows was approximately 20 feet. Two treatments were applied to the trees: weedy vs. weed-free. Three sections of 10 trees were assigned per treatment, with buffer rows and trees surrounding the treatments . For theThe mango cultivation area of this farm is made up of 24 rows with 47 mature mango trees each and a mix of Tommy, Keitt, Kent, and Florida Red varieties. The weed treatment was placed in row 24 , with buThe management practices used at the study site are ecologically oriented and minimal for a large conventional farm compared to other mango farmers in the area, and no insecticides are used by the grower to allow balanced insect biological control of pests. Similarly, mango farmers in the United States are limited in what chemicals they can apply to trees compared to those in other countries; for example, the use of Topsin to treat mango malformation is outlawed in the US . The majM. indica were recorded and collected weekly for six months including the major 8-week blooming period of mango. Five-minute watches were made recording insect interactions, observing, and collecting specimens on each of the 30 mango trees in both treatments totaling 7500 min of observations across the 25 weeks. Timed focal point observations were conducted weekly before, during, and after the peak mango flowering season (from November until May), similar to methods outlined in Carvalheiro et al. 2012 [Observations and collections of insects were made from the mango trees in both treatments. Insects interacting with the crop al. 2012 .The timing of observations per treatment per day was changed for each data collection day by alternating the order each tree was visited, to have a breadth of observations across the day. Close focusing binoculars were used to observe specimens in the upper canopy of the mango trees if needed. In each watch period per tree, all insects and flower visitors were recorded and collected for identification. Each insect specimen was collected if novel or if sight identification was not possible. Additionally, specimens were collected if they displayed notable behavior such as pollinating flowers using an insect aspirator, collection bag, or net. Specimens collected from the field were immediately placed into a plastic bag that were kept cool and stored in a freezer (0 °C) until further identification. Voucher specimens were pinned or stored in ethanol in vials and are maintained in the authors’ collection; they will eventually be deposited in the Florida State Collection of Arthropods. There were two flushes of inflorescences on the mango trees during this season, allowing insects to be collected from mango flowers from 12/05/2019–05/08/2020.Statistical analyses were performed with SPSS software. Descriptive information is presented to describe the variables of interest overall and by treatment. These include means, standard deviations, frequencies, and proportions. To compare means between treatments, a two-sample t test was used, and to compare proportion, chi-square test was used to see if the observed distribution of insects differed from the expected distribution. Statistical analyses of effect of treatment on insects compared between the weedy and no-weed treatment, as well as these effects on fruit yield, was performed using a general linear model by applying multivariate analysis of variance (MANOVA) with insect/fruit yield measures as the dependent variable (multiple Y’s) as a function of treatment while adjusting for tree age. Multivariate tests such as Pillai’s Trace, Wilk’s Lamba, Hotelling’s Trace, and Roy’s Largest Root examined overall model significance, followed by further analyses using simplified F-test comparison adjusting for multiplicity. All differences or associations were considered significant at the alpha level of 0.05 after the adjustment for multiplicity where appropriate.Fruit counts per tree were performed visually when nearly mature fruit were set on the tree as well as counted when harvested by the grower. Visual fruit yield counts were performed after bloom and all fruit had been pollinated and set .When comparing arthropod numbers on mango trees in both treatments, there was a significant effect of treatment (weeds) on the number of individual visitors to mango flowers  (There were significantly more Hymenoptera on weedy mango trees (< 0.001) .p = 0.057; p = 0.06) on the weed-free mango trees than the weedy treatment trees. There was a significant difference in beetles (Coleoptera) F = 10.59, df = 1, 56, p = 0.002, with more feeding in the mango flowers with weeds present.Additionally, flies were abundant flower visitors, and there were (nearly significantly) more on weedy mango trees than weed-free ones  , potenti = 0.031 .Insect family observations were aggregated by individual mango trees from both treatments; the aggregates were compared between the two groups using a t test. Of the total 126 insect families observed on the weed-free and weedy mango trees, 10 families differed significantly on the mango trees when weeds were present or not .p =< 0.001, Calliphoridae  T = 3.5, DF = 57, p = 0.001, and Syrphidae (hoverfly) T = 3.34, df = 44. p = 0.002. Calliphoridae is especially an abundant and effective mango pollinator who preferred to visit the mango flowers in the weedy treatment. Flower visiting insects such as Vespidae (wasps), T = 2.2, df = 18, p = 0.041 and Chalcididae (parasitoid wasps), T = 2.61, df = 29, p = 0.003, were also present in greater numbers in the weedy treatment.The representation of many mango pollinating insect families was significantly greater on mango trees with weeds present: Apidae (bees) T = 4.08, df = 55, p = 0.016 and Drosophilidae (fruit/vinegar/pomace fly) T = −2.68, df = 33, p = 0.011. While these insects were observed visiting flowers in small numbers, they do not serve as key mango pollinators. There was no significant difference in the number of ants .Other insect families had significantly greater numbers on the weed-free mango trees, such as Chironomidae (non-biting midges) T = −2.5, df = 58, p < 0.0001) and harvested  fruit from trees in the weedy treatment vs. those that were weed-free  increased the floral diversity for beneficial insects without negatively competing with the crop. Mango is one of the most widely cultivated tropical fruits worldwide and one of several drought-tolerant plants with minimal nutrition supplementation needs . Its deeHymenoptera preference of weedy mango flowers indicates that although bees (Apidae) tend to not be attracted to mango , if attrFrankliniella gardeniae reported as a mango visitor [Thrips are a threat to the production of mango, as mango flower thrips feed on petals, anthers, pollen, and floral nectaries, resulting in discoloration, malformation, weakening of the inflorescence, and reduction of fruit set ,55. Thri visitor .Previous research states that weeds act as a reservoir of thrips, enhancing populations in farms . HoweverCetoniinae) and ladybird beetles (Coccinellidae). The mango flower beetle can be a pest of mango and avocado in South Florida, where large numbers of the beetles destroy the flowers and reduce the number of fruits produced [There were significantly more beetles (Coleoptera) feeding in the mango flowers with weeds present. Many beetles are herbivores but may also visit flower parts and transport pollen. Beetles are a dominant group of potential visitors and pollinators of more open, unspecialized flowers, such as mango flowers . Cantharproduced . Their nVespidae (wasps) and Chalcididae (parasitoid wasps) were present in greater numbers in the weedy treatment. These insects have high energy needs and feed in flowers for key resources such as nectar and pollen. Wasp pollination has been observed in Vespidae that act as pollen vectors and both generalist and specialist pollinators, even surpassing bees as pollinators in some cases . PredatoThere was no significant difference in the number of ants (Formicidae: Hymenoptera) between weedy or weed-free mango trees. Ants have been found to be important in pollination of mango ,62 and hAdditionally, elimination of herbicide applications in the weedy treatment can reduce a farmer’s production costs, chemical use in the adjacent suburban neighborhood, and potential runoff and negative effects of chemicals on surrounding ecosystems. It also leads to a substantial gain in fruit yield in the weed treatment. Each Keitt fruit weighs on average 1.5–2.0 lbs . The average current price of a pound of Keitt mango across a spread of outlets, after deducting all marketing and packaging costs, is USD 0.143/lb . As treeHowever, given the constraints and application of the weed treatment in this experiment within one row of mangos and not spread across the farm, potential confounding abiotic factors such as sunlight/adjacent landscapes could have also impacted fruit yield. Further studies should clarify how field and surrounding weedy vegetation affects insect biodiversity and mango fruit yield, as well as soil abiotic and biotic functions and nutrition. Using other sources of insectary plants, such as sweet alyssum , or natiAnecdotally, we also observed more small mammals and snakes in the weed treatment compared to the rest of the farm, as well as flies feeding on carrion (the bodies of deceased rodents). As pollinators require distinct and diverse resources throughout their life cycle, including larval habitat and adult food resources, the presence of rat carrion has been found to facilitate pollination services to plants by bolstering the pollinator community across an urban gradient. Rat carrion has been found to increase pollinator abundance more than two-fold, and plants received 11.2% greater pollination service across landscapes and higher viable seed set, especially in densely urban landscapes . MutualiIn summary, we found that weeds increase the diversity of pollinating insects on mango flowers, and mangos with weeds growing below produced more fruit than those without weeds. Weeds can provide ground cover that can support insect natural enemies ,67, pollOverall, the use of weeds in increasing beneficial insects has shown promise ,80,81. HThis research has shown that studies are needed to understand not only the behavior of insects in floristically diverse vs. depauperate landscapes, but also how anthropogenic manipulation can affect ecological interactions among crops and pollinators. In attempting to increase pollinators by promoting the presence of weeds, it is inevitable that interactions between weeds and other species will occur, and should be investigated in various crops, as well as monitored in adjacent natural systems. The best management practices moving forward are to quantify the economic ramifications accompanying the increased habitat complexity provided by weeds, and in cases where there are benefits to this approach, to take advantage of the free services they may provide.


Birds have various plumage color patterns, and spot is a common phenotype. Herein, we conducted genome-wide association studies (GWAS) in a population of 225 ducks with different sized black spots to reveal the genetic basis of this phenomenon. First, we quantified the black spot phenotype within the duck population. The results showed that the uncolored area of the body surface first appeared on the ventral side. With increasing duck age, the area of the black spots was highly conserved across the whole body surface. The GWAS results identified a 198 kb  genetic region that was significantly associated with the black spot phenotype. The conditional GWAS and linkage disequilibrium (LD) analysis further narrowed the ultimate candidate region to 167 kb . A key gene regulating melanoblast migration and differentiation, EDNRB2 (Endothelin B receptor-like), was found in the candidate region and having significant mRNA expression level changes in embryonic duck skin tissue with different spot sizes. The significant SNPs (single nucleotide polymorphisms) associated with the EDNRB2 gene were annotated, and two mutations  were found to result in the loss of binding sites for two trans-factors, XBP1 and cMYB. The phenotypic effect of these two mutations suggested that they can regulate the size of black spots in a dose-dependent manner, and Chr4: 10,180,939 T > C was the major allele locus.Our results revealed that EDNRB2 was the gene responsible for the variation in duck body surface spot size. Chr4: 10,180,939 T > C was the major allele that explained 49.5 %  and 32.9 %  of the variation in duck body surface spot size, while 32.1 %  and 19.1 %  of the variation could be explained by Chr4: 10,190,671 A > T. The trans-factor prediction also suggested that XBP1 and cMYB have the potential to interact with EDNRB2, providing new insights into the mechanism of action of these genes.The online version contains supplementary material available at 10.1186/s12864-021-07719-7. Birds constitute one of the most diverse classes of animals on Earth, presenting a wide variety of body types, vocalizations, and plumage displays. Plumage color is crucial for birds, as it possesses multiple biological functions, e.g., heat retention, mate attraction, communication, camouflage, and skin protection; thus, it plays an important role in the evolution and diversity of birds , 2. AddiSpotted coats/plumage are a prominent color phenotype in mammals and avians and have been observed in cattle , donkeysAnas platyrhynchos) is one of the most important domestic fowl worldwide. In China, there are 32 local domesticated breeds. Among them, the plumage color phenotypes show tremendous differences. White-spotted individuals were observed in many duck populations. Using a candidate gene study strategy, Li et al. presumed that the Arg332His mutation in EDNRB2 was associated with spotted ducks [Duck  > 0.03, a major allele frequency < 0.99 and a maximum missing rate < 0.1 were finally obtained and used for the subsequent analysis. All filtered SNPs with an average density of 168 SNPs/Mb were distributed on 29 autosomal chromosomes, ChrZ, ChrW and Un (unplaced scaffolds)   were distributed in an ~ 370 kb region from 9,981,595 bp to 10,348,068 bp on chromosome 4  . A significant signal peak was observed at the same position on chromosome 4 among all groups Fig. a. The SNp) Table . The sigp) Table . The canp) Table . The pro2 > 0.2 was considered to indicate a linked correlation between the most significant SNP and each remaining SNP. The LD block was confirmed to span an ~ 167 kb region from 10,180,939 bp to 10,348,068 bp covering 8 genes: GRID2, LOC101805249, LOC113843527, LOC110351398, LOC106014689, LOC101805050, SPRY3 and LOC113843523  from the multi-GWAS analysis. After removing the effect of the most significant SNP, the remaining significant SNPs almost dropped below the threshold line, implying that there was potential linkage disequilibrium (LD) among SNPs in this region Fig. . Therefo523 Fig. . Further523 Fig. . SNP ann523 Fig. . Among tP < 0.05). Moreover, the phenotypic values of different genotypes exhibited gradual increasing or decreasing trends  were found to have a relationship with the EDNRB2 gene Fig. . Therefonds Fig. . After gnds Fig. . AlthougTo investigate the relationship between the phenotype and EDNRB2 mRNA expression level, we examined the RNA-seq results of our previous study about the pigment deposition process during the embryonic period . The resThe spotted phenotype is a common plumage variant in avians. Through our phenotypic analysis, we found that the size of black spots on the body surface of ducks varied greatly. The size of the black spots on ducks hardly changed during adulthood (comparison between 4-week-old and 8-week-old ducks). The genetic basis of the spotted plumage pattern in ducks has been previously identified. The c.995 G > A mutation in EDNRB2 was significantly associated with a spotted plumage pattern in ducks , 16. At The shape and size of pigmented areas are related to the distribution of pigment in the body. Studies have shown that color patterns are regulated by pigmentation genes in a dose-dependent manner by modulating pigment production . The SouNotably, the 2 predicted trans-factors, XBP1 and cMYB, were both reported to be associated with pigmentation or pigment-related diseases. XBP1 is an important candidate gene for vitiligo. Ren et al. demonstrated that the modification of XBP1 expression at the transcriptional level by a germline regulatory polymorphism has an impact on the development of vitiligo . cMYB waThe present study further explained the genetic mechanism of the complex shape of the pigmented area on the duck body surface. Our results revealed that EDNRB2 was the gene responsible for the duck body surface spot size variation. Chr4: 10,180,939 T > C was the major allele that explained 49.5 %  and 32.9 %  of the variation in duck body surface spot size, while 32.1 %  and 19.1 %  of the variation could be explained by Chr4: 10,190,671 A > T. The combination of two genetic loci can regulate the size of black spots in a dose-dependent manner. The trans-factor prediction also suggested that XBP1 and cMYB potentially interact with EDNRB2, providing a novel insight into the mechanism of action of these genes. A total of 300 ducks from the intercross population resulting from a cross between the GF2 strain (a hybrid strain constructed by SICAU) and Jianchang duck  were raised on a net floor at the poultry farm of Sichuan Agricultural University. GF2 and Jianchang duck both have inconspicuous and uneven body surface pigmentation; this variation was amplified in the F1 generation. The variation in spot sizes shown in Fig. Pigmentation spots on the body surface of all ducks were photographed under the same conditions. First, the digital camera was set to manual exposure, and each image was collected under identical exposure conditions, including exposure time and aperture. Then, the obtained images were imported into IPP 6.0 software  and magnified by the same multiplication factor. Using an irregular shape tool incorporated in the software, the area of interest (AOI) of each pigmentation spot was selected, and the geometric size of each region was measured. Pigmentation spot areas on the dorsal and ventral sides of ducks were measured. The proportion of pigmentation distribution on the body surface for each duck was calculated. Three replicates were made for each measurement, and the average values were taken as the final phenotype. A total of 300 ducks were selected to measure the pigmentation phenotypes at 4 and 8 weeks old. Blood samples were collected from the veins under the wings of ducks. A total of 225 blood samples of pigmented ducks were selected for DNA extraction using the phenol-chloroform protocol. The quality and quantity of DNA were examined by Nanodrop and agarose gel electrophoresis .Our study relied on SLAF to generate the genotypes of all ducks. Thus, the duck genome was selected as the reference genome for electronic enzyme digestion prediction. A single-nucleotide (A) overhang was added to the digested fragments using the Klenow fragment (3’→5’ exonuclease and 5’→3’ polymerase) (NEB) and dATP at 37 ℃. Next, polyacrylamide gel electrophoresis (PAGE)-purified duplex tag-labeled sequencing adapters (Life Technologies) were ligated to the A-tailed fragments using T4 DNA ligase. Polymerase chain reaction (PCR) using diluted restriction-ligation DNA samples, dNTPs, Q5® High-Fidelity DNA Polymerase and PAGE-purified PCR primers AATGATACGGCGACCACCGA and CAAGCAGAAGACGGCATACG (Life Technologies) was performed. PCR products were purified using Agencourt AMPure XP beads  and pooled. Pooled samples were separated by electrophoresis using a 2 % agarose gel; 300-500-bp fragments (including indices and adaptors) were excised and purified using the QIAquick Gel Extraction Kit (Qiagen). Gel-purified products were sequenced on the Illumina HiSeq 2500 system  per the manufacturer’s directions . Reads wThe SLAF clean reads were mapped to the duck reference genome (GCA_002224895.1) with Burrows-Wheeler alignment   using thPCA was performed based on all SNPs using the GCTA tool . This soPopulation structure and cryptic relationships were considered to minimize false positives and increase statistical power. A mixed linear model program emmax was used for the association analysis . The firVCFtools was used to retrieve individual genotypes in the region of interest. LD analysis between the most significant SNPs and other SNPs in the candidate region was conducted by Plink, and a Locuszoom graph was generated by R (v3.5.1) . HaploviThe duck embryo models with three levels of spot size variation were from Peking duck (non-pigmented), GF2 duck (moderately pigmented) and Heiwu duck (extensively pigmented). The EDNRB2 expression level in samples with different spot sizes was analyzed using our previous RNA-seq data . Considehttp://alggen.lsi.upc.es) was used to predict whether there were any trans-acting factor changes caused by the 4 SNPs. The sequence used in the analysis was 10 bp of the left and right flanks of the mutation site. We constructed the sequence template before and after mutation. To increase the accuracy of prediction, the maximum matrix dissimilarity rate value was reduced to 4.The online software PROMO (P-values from the comparison and correlation analysis between the phenotypic value and genotype were analyzed by T-test and a general linear model with the SPSS 19.0 software package. Differences at P < 0.05 were considered significant.The Additional file 1: Table S1. Top 2000 SNPs from GWAS and multi-trait GWASresults. Table S2. Gene annotationin 198kb genomic region. Table S3. ConditionalGWAS result of candidate region. TableS4. Linkage disequilibrium analysis between the leader SNP and the others. Table S5. Gene annotation in theultimate candidate region on Chr4. TableS6. Significant SNP annotation in the ultimate candidate region on Chr4. Table S7. Haplotype and correlationanalysis. Table S8. The predictionof trans-factor binding sites based on the significant SNPs related to EDNRB2.


CL, BMIBL, and BMIBH, respectively, based on CL, BL, and BH phenotypic data. We identified 82 SNPs for the seven traits by GEMMA-based and FarmCPU-based GWASs. Both methods detected two quantitative trait loci (QTL) on SSC8 and SSC17 for body conformation traits. Several candidate genes  were found to be associated with body weight and body conformation traits in pigs. Notably, the BMP2 gene had pleiotropic effects on CL, BL, BH, BMICL, and BMIBL and is proposed as a strong candidate gene for body size due to its involvement in growth and bone development. Furthermore, gene set enrichment analysis indicated that most of the pathway terms are associated with regulation of cell growth, negative regulation of cell population proliferation, and chondrocyte differentiation. We anticipate that these results further advance our understanding of the genetic architecture of body conformation traits in the popular commercial DLY pigs and provide new insights into the genetic architecture of BMI in pigs.The Duroc × (Landrace × Yorkshire) hybrid pigs (DLY) are the most popular commercial pigs, providing consumers with the largest source of pork. In order to gain more insights into the genetic architecture of economically important traits in pigs, we performed a genome-wide association study (GWAS) using the GeneSeek Porcine 50 K SNP Chip to map the genetic markers and genes associated with body conformation traits (BCT) in 311 DLY pigs. The quantitative traits analyzed included body weight (BW), carcass length (CL), body length (BL), body height (BH), and body mass index (BMI). BMI was defined as BMI COL9A1, APOE, CART, INSL3, and DKFZ were significantly associated with BL. ss131324074 on SSC7 and ss107849935 on SSC9 were significantly associated with BH, and ss131389597 on SSC9 and ss478942250 on SSC10 were significantly associated with BL in a Chinese Laiwu pig population  are economic traits which are moderately to highly important in pig production. It may be of interest to consider these traits in pig breeding schemes. Body height (BH) and body length (BL) are associated with meat production and were typical polygenic quantitative traits. Several studies have revealed significant single-nucleotide polymorphisms (SNPs) associated with BH or BL using the genome-wide association study (GWAS). For instance, pulation . An SNP pulation . In addipulation  and Wadapulation  reportedpulation  proved tWith the aid of high-density SNPs across the porcine genome, genome-wide association studies (GWASs) were utilized to dissect quantitative trait loci (QTLs) and genes associated with body conformation traits in pigs. The GWAS based on the mixed linear model (MLM) is the most popular method by taking account of population structure and genetic relatedness in deciphering the genetic architecture of complex traits in livestock . Multipl2 (The Duroc × (Landrace × Yorkshire) hybrid pigs (DLY) are the most popular commercial pigs used in the Chinese pig industry. In this study, we performed GEMMA-based and FarmCPU-based GWASs for the body conformation traits including carcass length (CL), BL, and BH in 311 DLY pigs to identify the significant SNPs and then compare the SNP set respectively detected by the two methods. In addition, pigs provide a good animal model for studying the genetic basis of human disease due to their numerous physiological and phenotypic similarities with humans, including obesity. In human, the body mass index (BMI) is often used as an indicator to assess obesity , which i2 . However2 . Thus, wThree-way crossbred DLY pigs intercrossed by Duroc boars and (Landrace × Yorkshire) sows were used in this study to conduct genetic analyses for body conformation traits. The experimental animals used in this study consisted of 311 DLY boars born in 2017 and were castrated at day 90 and raised in the same farm of Wen’s Foodstuffs Group Co., Ltd. . All pigs were fed with the same diets, raised under the same management conditions, and then slaughtered at 210 ± 3 days of age in a commercial abattoir in Shenzhen, Guangdong province. Three hundred eleven pigs were in the same batch. All the pigs were measured on the following traits: body weight (BW), body height , and body length . All the pigs were measured for BH and BL on the same flat ground before slaughter .2 shows relative independence from height or body length and high correlation with weight and fatness or subcutaneous fat  for subsequent analyses for a comprehensive understanding of the similarities and differences of different “height.”After skinning, scalding, scrapping, and eviscerating, carcass weight and carcass length  were measured within 30 min postmortem by the same person to minimize measurement errors. The ratio of weight and heighteous fat , as welly length  and bodyy length  of pigs y length . In thisp < 10–6) and unmapped or located on the sex chromosomes were also removed.Genomic DNA was extracted from ear tissues using an animal tissue DNA extraction kit  following the manufacturer’s protocol. DNA quality was detected using a NanoDrop ND-1000 (Peqlab Biotechnology) and agarose gel electrophoresis. The DNA concentration of the samples was adjusted to 50 ng/μl. Samples were genotyped with the GeneSeek Porcine 50 K SNP Chip  . QualityPrincipal component analysis (PCA) was performed using the SNP dataset to assess the potential population stratification prior to conducting the GWAS. PCA was performed with the Genome-wide Complex Trait Analysis (GCTA) software  and GenoW is the incidence matrix of the appropriate dimension for the fixed effects; β is the effect of the marker; x is an n×1 vector of marker genotypes; u ∼ 0, AA being the genomic relationship matrix estimated; and ε ∼ MVN  software , one trasoftware ; W is y is a vector of phenotypes of the analyzed trait; P and j-th candidate SNP; j-th candidate SNP; and e is a vector of the residuals.The GAPIT (version 3.0) R package  was usedy is a vector of phenotypes, K being the kinship matrix computed by the pseudo-QTNs, and e is a vector of the residuals.The REM model updates the pseudo QTNs using the SUPER algorithm as follows :y=u+ewp-value was defined as follows:n represents the number of SNPs with p < 0.01 in the GEMMA-based GWAS results, ordered ascendingly by their effects, and m is the number of qualified SNPs. The phenotypic variance explained by each significant SNP was estimated by the GCTA software  controlled at 0.01 . Brieflysoftware . The Hapsoftware  was usedsoftware  after thEnsembl genome database version 99 of the Sus scrofa genome  using the “biomaRt” package. Gene set enrichment analyses were conducted with these genes in the Metascape database . ASGA0091894 is the only SNP detected by both methods, which is significantly associated with BW. There were three SNPs, ASGA0001774, ASGA0005703, and WU_10.2_6_135404715, which showed significant effects on body weight, which otherwise was not reported previously. The most significant SNP detected by MLM and FarmCPU was WU_10.2_1_306708221 (p = 3.40 × 10−5) on SSC1, and ASGA0091894 (p = 3.90 × 10−5) explained 5.32 and 8.5% of the phenotypic variance of BW, respectively.We identified seven SNPs that were significantly associated with body weight, located on chromosomes 1, 3, 6, and 10, respectively . The GEMWU_10.2_17_17981232 on SSC17 was the most significant SNP identified by GEMMA-based GWAS (p = 1.60 × 10−7) (WU_10.2_7_49907567 on SSC7 was the most significant SNP identified by FarmCPU GWAS (p = 1.71 × 10−8) . Meanwhile, ALGA0123867 was the significant SNP detected by both methods. ALGA0056836 (p = 1.07 × 10−8) on SSC10 was found by FarmCPU  and FarmCPU (p = 3.71 × 10−8)   and also × 10−7) . The WU_ × 10−8) . For BL, FarmCPU . For BH, × 10−8) . The SNPCL, 22 SNPs with BMIBL, and 13 SNPs with BMIBH by these two GWAS methods  on SSC17 and WU_10.2_7_49907567 (p = 1.02 × 10−9) on SSC7 were the most significant SNP identified by GEMMA-based GWAS and FarmCPU GWAS for BMICL  and detected by FarmCPU GWAS was WU_10.2_13_138014916 (p = 2.45 × 10−9)   . Among aFarmCPU) . This siFarmCPU) .WU_10.2_17_17479009 on SSC17 was significantly associated with CL, BL, BH, BMICL, and BMIBL, and WU_10.2_17_15712448 was significantly associated with BL, BH, and BMIBL. We further examined the LD pattern between SNPs in these QTL regions. Two LD block with r2 > 0.8 respectively distributed on SSC8 and 17 were found. The LD block on SSC8 was 15 kb, including two significant SNPs for BL and BMIBH, respectively . The other three SNPs were newly found in the present study and two of them were identified by FarmCPU. According to two main criteria for listing the lead SNPs  and candidate genes , the gene was the nearest to the index SNP, and the gene was found in the vicinity of the lead SNP and was biologically related to the trait , there was no overlap between pig and human. Among all SNPs associated with these three BMI traits, only WU_10.2_17_17479009 and DBMA0000205 were significantly correlated with two or more traits simultaneously, and they had significant effects on both BMICL and BMIBL. The gene nearby WU_10.2_17_17479009 was bone morphogenetic protein 2 (BMP2), and the nearby gene of DBMA0000205 was phospholipase C Beta 4 (PLCB4). These two genes (BMP2 and PLCB4) were related to growth, body size, and skeleton. Moreover, PLCB4 also affected adipose tissue. Although the other SNPs are different, the function of genes nearest to the significant SNPs of three kinds of BMIs was similar. TGFBR3, BNIP3L, and PLCB4 were closest to the significant SNPs of BMICL. OTOG, ITPR3, ITPR3, and RASSF2 were nearest to significant SNPs of BMIBL. FRAS1, and FBXW7 were closest to significant SNPs of BMIBH. These genes all affect the adipose tissue, growth, body size, skeleton or nervous system  were identified to be significantly associated with more than one trait. The candidate genes closest to these five SNPs were PRNP, PLCB4, ITPR3, SHLD1, and BMP2. These genes all influence skeletal growth and body size in mice. We further aggregated evidence from our data, human (https://www.ncbi.nlm.nih.gov/omim/) and mouse genetic databases (http://www.informatics.jax.org/), and proposed the candidate genes based on their functional relevance to traits. A number of genes, such as DAGLA, RASSF2, MMUT, PEBP4, NR4A2, FERMT1, SOCS6, TBX3, TGFBR3, TRAPPC9, UCHL5, VAC14, WNT2B, GRM5, ULK4, and SCN8A affect growth, body size, and skeleton in mice. Interestingly, among these genes, EGLN3, KSR1, and GRM5 were involved in the growth of adipose tissue. This hints that these genes may affect the two traits simultaneously or influence body length traits by changing the backfat thickness or conversely.Among 53 significant SNPs that are associated with CL, BL, and BH traits based on two GWAS models, five SNPs  signaling pathway, playing a role in bone and cartilage development  mice showed smaller calvaria, thoracic cavities, and shorter spines and hind limbs than Cre-negative littermates (BMP2 gene resulted in short stature, facial dysmorphism, and skeletal anomalies (BMP2 as a causal gene of pig body size traits, some studies have shown that some mutation sites in or near this gene were associated with body length, body depth, and body width (BMP2 was in two of these three pathways. This result provided further evidence that BMP2 was the most probable candidate gene for body conformation traits. Although further studies are needed to dissect the genetic architecture of body conformation traits, our findings have identified a number of novel loci pinpointing biologically relevant genes and pathways for body weights and conformations.The most strongly associated variant was often located near the causal genes . Nearly elopment . The BMPtermates . In humanomalies . Althougdy width . Then, wdy width . The topWU_10.2_17_17479009 was the only SNP that affected more than three traits and showed pleiotropic effects on CL, BL, BH, BMICL, and BMIBL in pigs. Specifically, the BMP2 gene is proposed as a strong candidate gene for body size due to the effect on CL, BL, BH, BMICL, and BMIBL and is involved in growth and bone development. In addition, we expect that our results provide a comprehensive understanding of the BMI trait, which has not been studied adequately in pigs. Altogether, this study not only benefits the molecular breeding for body conformation-related traits of the DLY pig but also advances our knowledge of the poorly understood of genetic loci or genes controlling BMI in pigs.In this study, we identified 82 SNPs associated with seven body conformation traits in DLY pigs using GEMMA-based GWAS and FarmCPU-based GWAS. We then identified three genomic regions and several genes related to body conformation traits in pigs.