Anopheles gambiae complex of malaria vector mosquitoes, due to their role in local adaptation and range expansion. The study of inversions in natural populations is reliant on polytene chromosome analysis by expert cytogeneticists, a process that is limited by the rarity of trained specialists, low throughput, and restrictive sampling requirements. To overcome this barrier, we ascertained tag single nucleotide polymorphisms (SNPs) that are highly correlated with inversion status (inverted or standard orientation). We compared the performance of the tag SNPs using two alternative high throughput molecular genotyping approaches vs. traditional cytogenetic karyotyping of the same 960 individual An. gambiae and An. coluzzii mosquitoes sampled from Burkina Faso, West Africa. We show that both molecular approaches yield comparable results, and that either one performs as well or better than cytogenetics in terms of genotyping accuracy. Given the ability of molecular genotyping approaches to be conducted at scale and at relatively low cost without restriction on mosquito sex or developmental stage, molecular genotyping via tag SNPs has the potential to revitalize research into the role of chromosomal inversions in the behavior and ongoing adaptation of An. gambiae and An. coluzzii to environmental heterogeneities.Chromosomal inversion polymorphisms have special importance in the  A chromosomal inversion is a structural mutation that arises when a chromosome segment breaks and reattaches in reverse orientation. Those that are retained as long-term polymorphisms often span hundreds or thousands of genes . SuppresAnopheles gambiae complex studied here, An. gambiae sensu stricto (hereafter An. gambiae) and An. coluzzii. It has been suggested that these inversion polymorphisms promote ecological flexibility, enabling the successful exploitation of heterogeneous environments across tropical Africa . The availability of this assay, which targets 2La inversion breakpoints, simplified the search for phenotypic traits and single nucleotide polymorphisms (SNPs) associated with the inverted orientation (2La). Studies employing this tool in the laboratory suggested that the inverted orientation was associated with increased thermal and desiccation tolerance, a thicker cuticle, higher body water content, a more aggressive upregulation of heat-responsive genes such as heat shock genes, and a higher energy budget relative to the alternative (2L+a) arrangement  have been lacking, with the sole exception of a newly available set of polymerase chain reaction (PCR) restriction fragment length polymorphism (RFLP) assays for 2Rb  , referred to hereafter as OA, is a 5′-exonuclease method that genotypes tag SNPs based on PCR in the presence of allele-specific probes, both labeled with different reporter dyes. The other, Genotyping-in-Thousands by sequencing (GT-seq), is a custom amplicon sequencing approach that genotypes tag SNPs by next-generation sequencing of multiplexed PCR products . Our data suggest that these methods not only offer efficiency of scale and cost, but also represent substantial improvements in genotyping accuracy relative to the classical cytogenetic approach.We recently described a strategy that exploited the An. gambiae and An. coluzzii are highly polymorphic for chromosomal inversions , labeled with its unique numerical code. The corresponding carcass was placed in an individual 1.5 ml tube containing a desiccant (silica gel) and a matching numeric label unique for that mosquito. Ovaries were stored at -20°, and carcasses maintained at ambient temperature before further processing.Burkina Faso lies in the arid Sudan savanna belt of West Africa. In this region, vs. even-valued numerical codes. For this study, we selected a subset of 960 mosquitoes based on their cytogenetic karyotypes, with the goal of achieving maximum possible inversion genotype representation for the purpose of validating the molecular tag SNPs.Mosquito DNA was extracted from the carcass using a CTAB method . The banin silico inversion genotyping , heterozygous, homozygous inverted], with the minimum value taken as the conservative genotypic concordance. The other methodological difference was that ascertainment of tag SNPs in 2Rc was based on slightly different data partitions: (i) An. coluzzii, and (ii) An. gambiae after exclusion of BAMAKO  Genotyping and Genetics for Population Sciences Core, a unit of the Partners HealthCare Center for Personalized Genetic Medicine. Assays designed for this platform require forward and reverse PCR primers which produce ∼100 base amplicons containing the tag SNP, and additionally require two allele-specific fluorescently labeled 30-bp probes (‘reporters’) that discriminate between the reference allele at the tag (VIC dye) and the alternate allele (FAM dye). Candidate SNPs were filtered out if they were surrounded by runs of nucleotides and low complexity regions that interfered with acceptable primer design parameters. Further filtering was performed if candidate SNPs were surrounded by high frequency variants in the 25 bases immediately upstream or downstream of the tag SNP. High frequency was defined as >5% in at least one inversion genotype  or at least two population samples analyzed for a given inversion in the Ag1000G database. ; 64 thrGT-seq: GTseek LLC conducted multiplex primer design and consulted on GT-seq optimization. Because this multiplexed amplicon sequencing approach uses only unlabeled PCR primers to produce 50-100 bp amplicons spanning a tag SNP, high frequency variants neighboring the tag are not a limitation. However, the highly multiplexed nature of GT-seq, allowing simultaneous amplification of up to 500 SNP loci per individual for thousands of individuals, requires that the primer pool be optimized not only for individual amplicons , but also to minimize primer interactions across loci and mis-priming with other amplicons. The initial list of candidate tag SNPs ranked by concordance was filtered based on the output of custom perl scripts to evaluate primer pools (https://github.com/GTseq). Minimum concordance values varied by inversion . Candidate tag SNPs for 2La, which were overly abundant, were pruned by selecting every third candidate from a list ordered by chromosome position.Following The final libraries prepared by the GBCF included the same 192 specimens used during optimization and 765 additional specimens. They were constructed without optional exo-SAP treatment following An. gambiae and An. coluzzii individuals was sequenced on a single lane of Illumina NextSeq 500 v2.5 (75 cycle) High Output flowcell using a dual indexed 75bp single-end read. Base calling was done by Illumina Real Time Analysis (RTA) v2 software.Each of the 10 plate libraries was quality assessed on an Agilent Bioanalyzer 2100 High Sensitivity chip and quantified by qPCR using the Illumina Kapa Library Quantification Kit . The libraries were then normalized to a concentration of 4 nM and pooled for sequencing. The final pooled library containing 957 https://github.com/GTseq), sequencing data were demultiplexed into single fastq files for each individual sample. Individuals were genotyped at each locus with a perl script (GTseq_Genotyper_v3.pl) that counts the occurrence of each allele at a locus within individual fastq files. The ratio of allele 1 to allele 2 counts was used to generate a genotype for each locus with total read counts >10, following the methods and cut-offs of Using scripts described in the bioinformatics pipeline of An. gambiae-specific, and defined subsets of 2Rc tags (referred to in this work as 2Rc_col and 2Rc_gam) apply respectively to An. coluzzii or An. gambiae individuals.The procedures for filtering and calling molecular inversion genotypes were the same for both OA and GT-seq platforms. Filtering steps were as follows. For each tag SNP, we calculated the percentage of mosquito specimens in the sample with a genotype call at that tag . If SNP call rates were <80%, the underperforming tag SNPs were eliminated from further analysis. In addition, for each mosquito specimen analyzed, we calculated the percentage of tag SNPs with a genotype call . If the specimen call rate was <80%, that specimen was excluded from further analysis. Note that the mosquito specimens in the sample varied according to the inversion under consideration: 2La, 2Rb and 2Ru tags perform in both species, 2Rj and 2Rd tags are To calculate the multilocus inversion genotype for each specimen, we converted the raw genotype data for individual tag SNPs to the count of alternate alleles, where ‘0’ is a homozygote for the reference allele, ‘1’ is a heterozygote carrying one reference allele, and ‘2’ is a homozygote for the alternate allele. Next, we averaged the number of alternate alleles present across all tag SNPs in a given inversion, and used this value to predict a multilocus genotype based on three bins of equal size covering the possible range from 0 to 2: values falling between 0-0.67 were considered as 0; those between 0.68-1.33 were considered as 1; and those between 1.34-2 were considered as 2. Multilocus molecular genotypes were then compared to each other and with cytogenetically determined inversion genotypes.https://figshare.com/projects/Anopheles_coluzzii_and_An_gambiae_molecular_inversion_karyotyping/85157 and https://figshare.com/projects/Anopheles_gambiae_An_coluzzii_and_An_funestus_molecular_inversion_karyotyping_raw_amplicon_sequencing_data/81128. Code used to generate the data can be found on Github (https://github.com/GTseq and https://github.com/rrlove/molec_karyo_notebooks). Supplemental material available at figshare: https://doi.org/10.25387/g3.12670649.Figure S1 shows the tag SNP and specimen call rates for OpenArray and amplicon sequencing (GT-seq) approaches. Table S1 contains OA tag SNP ID numbers, locations, reference and alternate alleles, forward and reverse primer sequences, and probe sequences. Table S2 contains GT-seq tag SNP ID numbers, locations, reference and alternate alleles, and forward and reverse primer sequences. Table S3 contains specimen ID numbers and inversion genotypes  for each individual mosquito. Data can be found at An. gambiae and An. coluzzii mosquitoes at 54 tag SNP loci. The SNP call rate for one of the 54 loci fell below the 80% threshold (77.7%) and was eliminated from the panel . After filtering, the SNP call rate averaged 99.4% for the remaining 53 tags . Three specimens were dropped from analysis due to the belated determination that their cytogenetic genotypes were ambiguous. Four additional mosquito specimens were dropped from further OA analysis due to unacceptably low specimen call rates . The remaining 953 specimens had an average specimen call rate of 99.3% . The final number of OA tags per each inversion, and their approximate genomic position within the inversion, are shown in Custom OA plates were used to genotype 960 individual An. gambiae and An. coluzzii, as well as another GT-seq pooled library of 235 An. funestus mosquitoes pertaining to an independent experiment to be described elsewhere. This produced ∼359M total reads, of which ∼236M could be assigned to the 957 An. gambiae and An. coluzzii specimens based on their barcode sequences. Read counts from each of the ten An. gambiae-An. coluzzii sample plates ranged from 17.5M to 27.6M reads per plate and read counts per individual mosquito averaged 246,925 . The tag SNP call rate was below the 80% threshold for three tags , which were subsequently dropped from the genotyping panel. For the remaining 131 tags, SNP call rates averaged 98.2% . As was the case for OA analysis, three specimens were dropped due to ambiguous cytogenetic genotypes. Of the remaining 954, two were considered to have failed because they had specimen call rates below the 80% threshold , and were thus dropped. Those 952 within acceptable limits had average specimen call rates of 98.2% . The final number of GT-seq tags per each inversion, and their approximate genomic position within the inversion, are shown in Sequencing from one NextSeq lane included the pooled GT-seq library of 957 An. gambiae, and 513 for An. coluzzii). As summarized in An. gambiae genotyped for 2Rc, where three-way concordance fell to ∼81%. We discuss the special case of 2Rc in An. gambiae more fully below; here, we concentrate on the five other inversions and 2Rc in An. coluzzii.The inversion genotypes inferred for each specimen by the three methods  are provided in Table S3. We compared these genotypes to assess their concordance. Due to our filtering rules, not every specimen had genotype calls by both molecular methods. We focused our assessment on the subset of specimens that were successfully genotyped with all three methods . Although cytogenetic karyotyping may be considered the gold standard for inversion genotyping, two important considerations lend considerable confidence to molecular genotypes, particularly when both molecular approaches concur. First, while none of the tag SNPs are deterministic , OA and GT-seq infer genotypes based on multiple predictive tags scored per inversion, thus providing weight of numbers. Second, the final set of tags used for OA and GT-seq are almost completely non-overlapping, an outcome produced by distinct filters imposed on the initial list of candidate tags during assay development , the cytogenetic We hypothesized that some genotypic discordances, specifically those in which the two molecular methods agree but conflict with cytogenetics, are caused by cytogenetic errors rather than systematic biases in the molecular approaches. This is difficult to demonstrate conclusively, because the specimens used to compare the three genotyping methods have not been subjected to whole genome sequencing. Furthermore, it was not possible to double-check the cytogenetic karyotypes of the specimens with discordances in the majority of cases, because neither slides nor ovaries were available. However, there is strong evidence consistent with cytogenetic error. During sampling in the field, specimens were assigned numerical identifiers that incremented by “1” throughout the process; cytogenetic karyotyping was later split between two institutions on the basis of even- or odd-numbered identifiers (see Methods). This procedure virtually eliminates the possibility of biases owing to temporal or spatial heterogeneities during the course of the mosquito sampling. Because even- and odd-numbered specimens should be random subsamples from the same populations, we would expect no difference in discordance rates between them. This was not what we found. Of the specimens assayed by both molecular methods in this study, 280 were odd-numbered and 677 even-numbered. Focusing on the inversions with the largest numbers of specimens whose cytogenetic and joint molecular genotypes disagreed  population structure that could affect the performance of these tags. Population structure could arise from several non-mutually exclusive scenarios: (i) a lack or reduction of connectivity between natural populations; (ii) local heterogeneity in selection pressures acting on targets inside the inversion; and/or (iii) violation of the assumption that the focal inversion arose uniquely . We suspect that at least one of these scenarios applies to 2Rc tags ascertained in An. gambiae, probably explaining their lower rate of apparent success in genotyping  alleles segregate at low frequencies in the population. Consistent with this, if 2Rd tags in the present study were applied to An. coluzzii where they are not correlated with inversion status, the vast majority (38 of 39) of specimens genotyped cytogenetically as heterozygotes would be molecularly genotyped as standard homozygotes (Table S3). Based on this, we expect the systematic underestimation of the number of alternate alleles to produce a distinctive pattern where ‘true’ standard homozygotes are correctly identified, but heterozygotes and inverted homozygotes would be incorrectly genotyped molecularly as standard homozygotes. An. gambiae either skew toward molecular genotypes of ‘1’ or ‘2’, or they are roughly equally distributed between ‘1’ or ‘2’ and ‘0’. The pattern for 2Rc in An. gambiae is distinctive, in that the skew is strongly toward molecular genotypes of ‘0’, which is consistent with tags that may not be appropriately suited for the An. gambiae population in which they are applied. Further study is both required and merited, to understand the cause(s) of population structure between the populations used to develop the 2Rc tags for in An. gambiae in Ag1000G and those used to test the tags in the present study. Interestingly, 2Rc contains cytochrome P450 genes implicated in insecticide resistance in An. gambiae and An. coluzzii , OA remains a cost effective option and is still widely used . Molecular genotyping methods, both OA and GT-seq, can be performed at scale and the results are comparable or superior to traditional cytogenetic karyotyping. These tools invite a renewal of investigations into the role of chromosomal inversions in anopheline behavior and environmental adaptation.Chromosomal inversions have been viewed as instruments of ecotypic differentiation in anopheline mosquitoes . Insight


Improving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships among feed efficiency traits and metabolizable efficiency traits in 180 male broilers. Significant loci and genes affecting the metabolizable efficiency traits were explored with an imputation-based genome-wide association study. The traits measured or calculated comprised three growth traits, five feed efficiency related traits, and nine metabolizable efficiency traits.P < 0.01 or P < 0.05), whereas the coefficients of MDM and MCP of low-RFI birds were greater than those of high-RFI birds (P < 0.01). Five narrow QTLs for metabolizable efficiency traits were detected, including one 82.46-kb region for DEW and GEE on Gallus gallus chromosome (GGA) 26, one 120.13-kb region for MDM and AMEn on GGA1, one 691.25-kb region for the coefficients of MDM and AMEn on GGA5, one region for the coefficients of MDM and MCP on GGA2 (103.45–103.53 Mb), and one 690.50-kb region for the coefficient of MCP on GGA14. Linkage disequilibrium (LD) analysis indicated that the five regions contained high LD blocks, as well as the genes chromosome 26 C6orf106 homolog (C26H6orf106), LOC396098, SH3 and multiple ankyrin repeat domains 2 (SHANK2), ETS homologous factor (EHF), and histamine receptor H3-like (HRH3L), which are known to be involved in the regulation of neurodevelopment, cell proliferation and differentiation, and food intake.The residual feed intake (RFI) showed moderate to high and positive phenotypic correlations with eight other traits measured, including average daily feed intake (ADFI), dry excreta weight (DEW), gross energy excretion (GEE), crude protein excretion (CPE), metabolizable dry matter (MDM), nitrogen corrected apparent metabolizable energy (AMEn), abdominal fat weight (AbF), and percentage of abdominal fat (AbP). Greater correlations were observed between growth traits and the feed conversion ratio (FCR) than RFI. In addition, the RFI, FCR, ADFI, DEW, GEE, CPE, MDM, AMEn, AbF, and AbP were lower in low-RFI birds than high-RFI birds (Selection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing the weight gain. Five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, combined through nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species. Feed efficiency is the most important trait in the poultry industry because feed accounts for approximately 70% of the total production cost . In poulhttps://www.animalgenome.org/cgi-bin/QTLdb/GG/index, 10/24/2019). To our knowledge, only three studies have reported 22 QTLs significantly associated with metabolizable efficiency traits, such as AMEn, dry excreta weight (DEW), and crude protein excretion (CPE) [Quantitative trait loci (QTLs) for economically important traits in animals have been studied for more than 20 years. In chickens, 587 and 40 QTLs associated with FCR and RFI, respectively, have been detected in the Animal QTL Database  . Few stuA genome-wide association study (GWAS) is a powerful tool that can be used to explore the genomic variation associated with complex traits in farm animals. GWAS studies for RFI have been performed in broilers  and laye. The birds were slaughtered at 43 d of age after a 12-h overnight fast to obtain records of carcass traits, including abdominal fat weight (AbF).In the present study, the workflow for the experiment is illustrated in Fig. 1A metabolism trial was conducted on 189 male broilers subjected to an adaptation period of 4 d followed by a subsequent collection period from 28 to 42 d of age. A total excreta collection method was used. During the collection period, the individual body weight and total feed intake were measured, and the daily (24 h) excreta was collected in white enamel trays. Contaminating materials  were carefully removed. Then a small amount of 10% hydrochloric acid solution was sprayed to prevent nitrogen loss . ExcretaAt the end of the collection period, dried excreta were weighted, ground through a 40-mesh screen, and stored in airtight plastic bags at − 20 °C for dry matter (DM), gross energy (GE), and crude protein (CP) analyses. The moisture content of the feed and excreta was removed by drying in forced-ventilation ovens at 105 °C until a constant weight was reached (AOAC method 934.01) . GE valu0.75) for each bird. RFI was computed as the difference between the observed and predicted ADFI. The predicted ADFI was calculated as:μ represents the intercept, MWT represents the metabolic body weight at mid-test. ADG represents the average daily gain, β1 and β2 represent partial regression coefficients, and e represents the residual. The estimated e was taken as the measure of RFI.Seventeen traits were measured or calculated: body weight at 28 d (BW28), BW42, average daily feed intake (ADFI), average daily gain (ADG), FCR, RFI, DEW, gross energy excretion (GEE), CPE, metabolizable dry matter (MDM), AMEn, metabolizable crude protein (MCP), coefficients of MDM, AMEn, and MCP, AbF, and percentage of abdominal fat (AbP). The total feed intake for each broiler was calculated by summing the feed consumption during the test period, which was then used to calculate the ADFI. The individual body weight gain was calculated on the basis of BW28 and BW42, and was then used to derive the ADG. The FCR was obtained by using the total feed intake divided by the total weight gain. For AMEn, 8.22 kcal/g of nitrogen was used as the correction factor . The coeQuality control of phenotypes was applied to RFI, FCR, and the coefficients of MDM, AMEn, and MCP, and data that deviated more than three standard deviations from the mean were removed. Nine birds were excluded from the analysis, and the remaining 180 records were available for further analyses.Genomic DNA was extracted from blood samples with the phenol-chloroform method. The 189 broilers were genotyped with the customized chicken 55 K SNP array from Beijing Compass Biotechnology Co., Ltd.  . To imprFor the target panel, a total of 3,638 broilers  were used for genotype imputation. Quality control criteria were applied to the target panel: individual call rate ≥ 90%, SNP call rate ≥ 90%, and minor allele frequency (MAF) ≥ 0.01. In addition, SNPs located on the sex chromosomes were removed. Ultimately, 41,856 autosome variants and 3,607 broilers  remained for further analyses.ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/002/315/GCF_000002315.6_GRCg6a/) with the Burrows-Wheeler Aligner (BWA)-MEM algorithm [Gallus gallus chromosome (GGA) 16 were omitted because there were fewer common SNPs between the target panel and the reference panel. The sex chromosomes were also removed. In addition, quality control of the reference panel was conducted with the criteria of individual call rate ≥ 90%, SNP call rate ≥ 90%, and MAF ≥ 0.01. After filtering, a total of 12,377,431 autosome variants remained for the 230 sequenced birds.For the reference panel, 230 broilers  in generation 7 were randomly selected from 3,449 birds and re-sequenced with 150 bp paired-end reads on an Illumina NovaSeq 6000 platform with an average depth of approximately 10×1 L coverage. The sequencing was performed by Zhejiang Annoroad Biotechnology Co., Ltd. . Variant calling was performed according to a standardized bioinformatics pipeline for all samples , 19. Spelgorithm . Then, Plgorithm . Varianthttp://faculty.washington.edu/browning/conform-gt.html). Subsequently, 40,342 autosomal SNPs from the target panel were retained. One factor, the effective population size [Genotype imputation of the 55 K genotypes of the broilers to the imputed whole-genome sequence (WGS) level was performed with Beagle 5.0 . Before ion size , affectsion size . The eff: Fig. S1). To assess the accuracy of imputation from the target panel to the reference panel, we assessed the genotype concordance rate and allelic R2 measures for each variant. The genotype concordance rate was calculated by comparing the imputed and real genotypes for the 230 birds analyzed with both panels. The allelic R2 was calculated as the estimated squared correlation of the imputed sequence genotype on the true sequence, which was given by Beagle 5.0. We applied strict post-imputation filtering criteria per SNP: allelic R2 ≥ 0.9 and MAF ≥ 0.05. Finally, 1,279,346 autosomal variants and 180 samples remained for the GWAS analyses  . Then th1P < 0.05 (*) or P < 0.01 (**) was considered significant.Pairwise phenotypic correlations of two feed efficiency traits (RFI and FCR) with metabolizable efficiency traits were analyzed in the Hmisc R package. To better understand the relationships between RFI and metabolizable efficiency traits, the lowest and highest 20% of the RFI (low-RFI and high-RFI) birds were selected from 180 chickens and compared with Tukey’s test in SAS 9.4  . P < 0.0https://github.com/genetics-statistics/GEMMA/releases) [y represents the vector of phenotypic values; W represents the vector of covariates, including a column of 1 s; α represents the vector of the corresponding coefficients including the intercept; x represents the vector of marker genotypes; β represents the effect size of the marker; u represents the vector of random polygenic effects; ϵ represents the vector of errors; τ−1 represents the variance of the residual errors; λ represents the ratio between the two variance components; K represents the centered relatedness matrix estimated from 1,279,346 variants and In represents the identity matrix. MVNn represents the n-dimensional multivariate normal distribution. The Wald test was used as a criterion to select SNPs associated with metabolizable efficiency traits.The GWAS for metabolizable efficiency traits was performed using the univariate linear mixed model (LMM) implemented in GEMMA version 0.98.1 software (eleases) . The genhttps://cran.r-project.org/web/packages/ qqman/) in R (version 3.6.0). Linkage disequilibrium (LD) blocks of target regions were performed using the Haploview version 4.2 software [http://genome-asia.ucsc.edu/cgi-bin/hgGateway?hgsid=472768848_otkBtCHKhHMTV1xrxHuq737iivJ1). Boxplots were produced by the ggplot2 package in R (version 3.6.0).The genome-wide significance was assessed using the simple M method , to infesoftware . SNP posWe determined the descriptive statistics for the traits associated with growth, feed efficiency, and metabolizable efficiency Table . In thisP < 0.01). RFI showed a low correlation with MCP  and the coefficient of MDM . RFI was phenotypically independent of BW28, BW42, and ADG . FCR was positively correlated with BW28  and CPE , and negatively correlated with BW42, ADG, and MCP, ranging from − 0.49 to − 0.20 (P < 0.01). The relationships of FCR with ADFI, DEW, GEE, MDM, AMEn, AbF, and AbP were close to zero (P > 0.05). The same negative relationships existed between the coefficient of MCP and the two feed efficiency traits (RFI and FCR). Poor phenotypic correlations were found between the coefficient of AMEn and the two traits (RFI and FCR) (P > 0.05).Pearson correlation coefficients for feed efficiency traits (RFI and FCR) with other traits are shown in Table P > 0.05). RFI and FCR were significantly lower for low-RFI birds than for high-RFI birds (P < 0.01). ADFI, DEW, GEE, CPE, MDM, AMEn, and AbP were 7.79%, 9.70%, 8.59%, 14.09%, 7.03%, 7.73%, and 20.90% lower, respectively, in low-RFI birds than high-RFI birds (P < 0.01). AbF was 18.97% lower in low-RFI chickens than high-RFI chickens (P < 0.05). Interestingly, the coefficients of MDM and MCP were greater for low-RFI birds than for high-RFI birds (P < 0.01).Descriptive statistics for growth, feed efficiency, and metabolizable efficiency traits compared between the high- and the low-RFI broiler chickens are presented in Table The average imputation accuracies of different effective population sizes on GGA28 are shown in Additional file 2-test, P = 0.15).The number of SNPs in different MAF classes for different datasets are shown in Fig. 2 according to the MAF and chromosome, as shown in Fig. 2 increased from 0.12 to 0.75. At the chromosome level, the genotype concordance rate slightly fluctuated between 0.81 and 0.90, whereas R2 fluctuated between 0.20 and 0.69. The distribution of SNPs used in the GWAS analyses after post-imputation filtering is summarized in Additional file 2 reached an average of 0.924 and 0.937, respectively.To evaluate the imputation accuracy for imputed WGS data for 3,607 birds after imputation in detail, we determined the average genotype concordance rate and R, and Table We focused on analysis of metabolizable efficiency traits in the current study. The Manhattan and quantile-quantile (Q-Q) plots are presented in Figs. 5β < 0) of DEW and GEE, respectively. These SNPs on GGA26 were located either within or near the nearest genes, including chromosome 26 C6orf106 homolog (C26H6orf106).For DEW and GEE, the same 95.17-kb region in GGA26 (4.26–4.36 Mb) was identified, which contained 61 significant SNPs. LD analysis showed that one high LD block was detected in this region . The region with one strong block contained four significant SNPs . The SNP rs315854959 was located near the ETS homologous factor (EHF). In addition, four significant SNPs associated with the coefficient of MDM were detected, which clustered within a 0.38-kb region in GGA2 (103.48 Mb). These SNPs were located near histamine receptor H3-like (HRH3L).For the coefficients of MDM and AMEn, a common 691.25-kb region (GGA5: 18.31–19.00 Mb) was detected, which contained 34 and 53 significant SNPs, respectively. LD analysis revealed one strong block in this significant region Fig. a. The toHRH3L gene.For the coefficient of MCP, 251 significant SNPs were located in GGA2 and GGA14. The 45 significant SNPs were clustered within a 74.62-kb region (GGA2: 103.45–103.53 Mb), and four of these SNPs were the same as those found for the coefficient of MDM; 206 SNPs were clustered within a 690.50-kb region (GGA14: 10.02–10.71 Mb). Extremely strong LD status was found in the two regions .In addition, low-RFI birds had significantly lower ADFI, DEW, GEE, CPE, MDM, and AMEn, and higher the coefficients of MDM and MCP than the high-RFI birds. Similar results for low-RFI and high-RFI animals have been observed by Metzler-Zebeli et al. , who repExcess AbF deposition is undesirable in the poultry industry because AbF is considered a waste product . In the 2 between imputed and true genotypes reached an average of 0.924 and 0.937, respectively, values that were higher than those reported by Huang et al. [Genotype imputation has been widely used in GWAS to boost power . This meg et al. , who useg et al.  reportedg et al.  reported2 resource population of medium-growth broilers [C26H6orf106 gene. Its homolog, C6orf106, has been investigated for activation of extracellular-signal-regulated kinase signaling pathways to accelerate cell proliferation [The genomic region of 95.17 kb on GGA26 (4.26–4.36 Mb) was detected to be associated with DEW and GEE. Previous studies have reported QTLs on GGA26 for DEW (2.4–3.2 Mb) and excreta nitrogen to phosphorus ratio (3.2–4.2 Mb) in an Fbroilers . The mosferation .–123.03 Mb) in a meat-type × egg-type resource population by Hansen et al. [A genomic region (GGA1: 93.26–93.38 Mb) was found to be associated with MDM and AMEn, and has also been found to be included in a feed efficiency QTL  is the second member of the Shank protein family and is involved in neurodevelopmental and psychiatric disorders [SHANK2 plays a key role in regulating transepithelial salt and water transport by modulating Na+/H+ exchanger 3 (NHE3) expression and activity in epithelial cells, including those in the gastrointestinal tract [SHANK2-knockout mice show hyperactivity and repetitive behaviors [EHF is a member of the epithelium-specific ETS transcription factor family, which is highly expressed in multiple epithelial cell types including intestinal epithelium [EHF plays an important role in the regulation of epidermal proliferation and differentiation [We detected one important QTL region (GGA5: 18.31–19.00 Mb) associated with the coefficients of MDM and AMEn, which was not previously reported. The top SNPs associated with DEW and GEE were within the isorders . SHANK2 al tract . SHANK2-ehaviors . EHF is ithelium . EHF plantiation .HRH3L gene. Very few studies on HRH3L are available in the literature. HRH3 is an autoreceptor on numerous neurons that inhibits the synthesis and release of histamine [HRH3 negatively regulates food intake in rodents, in a manner independent of its histaminergic tone modulation [Two novel regions (GGA2: 103.45–103.53 Mb and GGA14: 10.02–10.71 Mb) associated with the coefficient of MCP were detected and the top variant was located near the istamine . Previoudulation .In summary, selection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing weight gain. In addition, five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, obtained from both nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species.Additional file 1: Figure S1. Average imputation accuracies of different effective population sizes on GGA28.Additional file 2: Table S1. Distribution of SNPs used in the GWAS analyses after post-imputation filtering.Additional file 3: Table S2. Pearson correlation coefficients for growth and metabolizable traits in broilers.Additional file 4: Figure S2. Association results of the candidate region on GGA1 (93.26–93.38 Mb) for MDM and AMEn. (a) Linkage disequilibrium (LD) analysis of the 4 significant SNPs on GGA1. (b) Box plot for the effect of the SNP rs732655996 on MDM. (c) Box plot for the effect of the SNP rs732655996 on AMEn. MDM and AMEn represent metabolizable dry matter and nitrogen corrected apparent metabolizable energy, respectively.Additional file 5: Figure S3. Association results of the candidate region on GGA14 (10.02–10.71 Mb) for coefficient of MCP. (a) Linkage disequilibrium (LD) analysis of the 206 significant SNPs on GGA14. (b) Box plot for the effect of the SNP rs738484580 on the coefficient of MCP. Coefficient of MCP represents the coefficient of metabolizable crude protein.


Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes  were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population  challenged with sea lice,  However, applications of genotype imputation in aquaculture breeding are at a formative stage, and optimal strategies need to be defined.Genomic selection is increasingly applied in aquaculture breeding to expedite genetic gain for key production and disease resistance traits , and it is the most costly disease-related problem for the global salmon industry . Sea li in 2006 . EncouraThe aim of this study was to systematically evaluate and develop optimal genotype imputation strategies for Atlantic salmon breeding programs. Using data from a typical commercial breeding program, several genotype imputation strategies for genomic selection were tested by (a) identifying high density SNP panels with optimal density and composition; (b) testing the imputation accuracy when parents were genotyped at high density, and offspring at a range of low densities (c) comparing the genomic prediction accuracies of each of the datasets, and (d) assessing the cost-benefit of imputation for genomic prediction by quantifying the trade-off between the cost of genotyping at a given density and the achieved improvement in genomic prediction accuracy.The data used for testing genotype imputation and genomic prediction were collected from an Atlantic salmon commercial population , as described in  as part of a trial conducted at the Marine Environmental Research Laboratory  in 2007, as described in (e(SLC), with a mean of 3.11 and median of 3.16, was used in all subsequent analysis (Supplementary Information 1). The weight of the offspring ranged from 52 to 203 g with a mean of 111.8 g. The weight data were normally distributed and therefore no transformation was required.The population was challenged with sea lice , and 520 offspring passed all criteria, and were used to calculate the genomic relationship matrices for each SNP panel.All samples were genotyped with the Affymetrix Axiom 132K Atlantic salmon SNP chip  with theG) was calculated from the GenABEL/R (“ibs” function) kinship matrix  of individual i at SNP locus k where the reference allele is arbitrarily chosen; and, kp is the frequency of the reference allele in the data.The identity by state (IBS) genomic relationship matrix ; and ikH is the observed heterozygosity for animal i. Subsequently G was inverted (R “solve” function) to be used in the ASReml  did not identify any sub-clustering in the data (Supplementary Information 1).HD SNP panel’ was the full high density panel containing 76,488 SNPs after quality control; ‘reduced SNP panels’ were the reduced density panels generated from the HD SNP panel to test genomic prediction at lower SNP densities; ‘optimal SNP panel’ (medium density) was the selected 5K SNP panel ; and, ‘imputation SNP panels’ were the low density SNP panels assumed to be genotyped on the offspring, selected from the optimal SNP panel to test imputation.The SNP panels used throughout the analysis were defined as follows: ‘https://github.com/SmaragdaT/CVrep) and is available as an R package  under GNU General Public License v3.0  identification of optimal SNP panels in terms of density and SNP composition; (b) assessment of the imputation accuracy of a range of lower densities of imputation SNP panels, when imputed to the optimal SNP panel identified in (a); and, (c) estimation of genomic prediction accuracies of imputed datasets and comparison with genomic prediction accuracies of datasets without imputation. All analyses were performed via a custom pipeline that was developed integrating: (i) PLINK /1.90-beta4.1 . Briefly, SNPs were selected so that they would be on average equally spaced within each chromosome with a ‘step’ of 0.5, 1, 3, 5, 7, 9, and 11 Mbp between them. The first and last SNPs on each chromosome were always selected. If given the ‘step’ and physical position of the subsequent SNP, the same SNP happened to be selected twice, then the next SNP was included in the panel instead. The larger the ‘step’ the lower the density .v = 104). Subsequently, using the phenotypes of the remaining four groups (training set nt = 416) and the genomic relationship matrix, breeding values (BVs) were predicted for each of the validation sets fitting the following mixed model in ASReml/3.0:y is the response variable (Loge(SLC) or body weight); m is the overall mean, b is the vector of fixed effects (sex and weight for the Loge(SLC) model; sex for the body weight model) associated with the incidence matrix X; a is the vector of random animal effects with a∼MVN associated with the incidence matrix Z, where σa2 is the additive genetic variance; and, e is the residual error with e∼MVN. The mean correlation between the predicted BVs (ŷ) and the phenotypes (y) was calculated for the five validation sets.Genomic prediction accuracy in the offspring was assessed through cross validation. The offspring were randomly partitioned into five groups of equal size, each time masking the phenotypes of one of the groups  and for weight. This heritability was calculated from the whole dataset, all 520 offspring and with G calculated on all SNP markers after quality control with the same criteria as above , and using model (1). Then for each SNP density, the average accuracy (and standard deviation) was obtained over the 10 SNP panel replicates.For each SNP panel and for each replicate, prediction accuracy was calculated as the mean correlation divided by the square root of the heritability i.e., the lowest density SNP panel that rendered genomic prediction accuracies for both traits similar to those obtained with the HD SNP panel). For the imputation panels, SNPs were selected randomly within each chromosome . Due to rounding, it was possible that the total number of selected SNPs could be slightly larger than the intended density . The optimal SNP panel was assumed to be genotyped on all parents (n = 86) and the imputation SNP panels of varying densities were assumed to be genotyped for all offspring (n = 520), so that the offspring were imputed to the optimal SNP panel density (parents’ genotypes). Imputation was conducted using FImpute/2.2  (e(SLC) and body weight, and cross-validated prediction accuracies were estimated in ASReml/3.0 using model (1), with the difference that here the G matrices were calculated from both directly genotyped and imputed SNP genotypes for the offspring. Quality control analysis described in section ‘Genotypes and quality control’ was repeated for the imputed genotypes. The multiple cross-validation procedure was used as described above, and for each imputed dataset, the average prediction accuracy was calculated over 50 replicates.Estimated breeding values for logA cost-benefit analysis was performed to evaluate the economic benefit of genotyping the selection candidates at lower densities and performing genotype imputation considering the genomic prediction accuracy obtained. The scenarios tested were (i) all animals (parents and offspring) genotyped at medium density with a 5K SNP panel, and, (ii) parents genotyped at 5K and offspring at 200 SNPs and imputed to 5K. The annual cost of genotyping was estimated as follows: for a salmon breeding program assuming 300 parents and 10,000 offspring, for scenario (i) a cost of $15 per individual was assumed; for scenario (ii) 300 parents genotyped at 5K were assumed to cost $30 per SNP array (the price per array increases with lower volume of samples), and 10,000 offspring genotyped at 200 SNPs were assumed to cost $5 per individual to genotype the SNP panel.https://github.com/SmaragdaT/CVrep), and is available as an R package  under GNU General Public License v3.0  for sea lice resistance, and 0.57 (SE = 0.07) for body weight which was generally in agreement with findings from earlier studies, either based on the same . Reducing the SNP panel density from the HD SNP panel  to 200 SNPs resulted in a 14.5% decrease in genomic prediction accuracy for sea lice resistance, and a 27.9% decrease for body weight , which mRegardless of the trait, prediction accuracy started to decrease between 2,000 and 5,000 SNPs, therefore the medium SNP density of 5,000 was tested as the optimal SNP panel for the imputation analyses. This medium SNP density was used rather than the HD SNP panel because it was assumed that a 5K SNP panel would be cheaper to genotype in most circumstances.While the reduction in genomic prediction accuracy with the reduced SNP densities was modest, the standard deviations  and the Another source of variation in the genomic prediction accuracy is due to the random sampling of individuals into the cross-validation groups, which will depend to some extent on sample size. Interestingly, a single prediction accuracy from one cross-validation can be a major over- or underestimate of the prediction accuracy. For example, for one of the medium density 5000 SNP panels, the prediction accuracy for sea lice resistance ranged from 0.40 to 0.62 over the 50 cross-validation replicates, with a mean of 0.52 . Similare.g., Imputation accuracy increased as the density of the imputation SNP panels increased  which waDespite the higher imputation accuracy of higher density imputation SNP panels , increasvs. using the low-density SNP panel alone (without imputation) varied according to SNP density. As a general pattern, genomic prediction accuracies using genotypes imputed to the medium density 5,000 SNPs optimal panel were very close to those using true genotypes for the same density  with imputation to medium density  resulted in prediction accuracies comparable to using the optimal SNP panel  on all animals, and significantly outperformed low density panels alone without imputation.While the influence of the SNP density of the imputation panel on genomic prediction accuracy might be minor, the benefit of imputation  density  and 6. T density  and 6, w density . In contIn scenario (i) where all animals (parents and offspring) were genotyped with a medium density SNP panel (5000 SNPs), the total cost of genotyping was estimated at $154,500. However in scenario (ii) where only the parents were genotyped for medium density and the offspring were genotyped for 200 SNPs, the cost was estimated to reduce to $59,000. This corresponds to a 62% cost reduction between scenario (i) and (ii), with virtually no loss of genomic prediction accuracy for the traits measured.This study explores developing optimal methods for cost-effective genomic selection in aquaculture, with a focus on an Atlantic salmon sib-testing breeding program. In this study, genetic relationships were incorporated through the genomic relationship matrix which was computed from the SNP genotype data and captures identity by state (IBS). While family-based selection using phenotypes and pedigrees can only capture between-family variation, genomic selection using genetic markers can also capture within-family variation. This allows to distinguish between the genetic merit of full-siblings by predicting and utilizing the Mendelian segregation term. Previous studies have shown that using genomic relationships improves the predictive ability of the BVs compared to pedigree-based predictions , prediction accuracy is likely to be much lower, and higher SNP marker density may be more advantageous . Selecting SNPs based on genetic distance was not tested, and may be expected to impact on prediction accuracy due to major variation in recombination rate across the genome of salmon, particularly in males. However, given that reducing SNP density from the highest density to the optimal density panel did not reduce prediction accuracy, it is unlikely that genetic distance or regions of high recombination have a major impact.G matrix) were not found to be reliable, with large standard deviations that often were larger than the estimates. Hence, while the correlation between predicted BV and phenotype reduced for lower densities as expected, the accuracies calculated using the heritability estimates from lower density SNP panels appeared to remain high , which was an artifact introduced by the spurious heritability estimates. This is likely to be common in studies of genomic prediction accuracies with reduced marker densities if the reduced panel is used to calculate the h2 estimate that underlies the transformation from correlation between phenotypes and EBVs to the accuracy value Heritabilities estimated using lower density SNP panels , although the imputation accuracy was lower, the genomic prediction accuracy using the imputed genotypes was not greatly reduced. The high imputation accuracies observed in this study can be explained by the inclusion of close relatives in the reference group. A trade-off was observed between genotyping cost and loss of prediction accuracy, but using low density imputation panels with imputation to medium density appears to be a highly accurate cost-effective option. A reduction in prediction accuracy means a reduction in genetic gain, and this has a cost implication. However, this cost is difficult to quantify, as it will be specific to the trait, the breeding goal, and the company. Furthermore, it is unclear where this cost would be incurred, for example to the breeding company or to the producer. In addition, it should be noted that this is a small-scale trial and the imputation and genomic prediction performance is likely to be more consistent in larger commercial operations, and therefore needs to be tested for different populations and traits.Results from using imputed genotypes for genomic prediction were in agreement with previous studies , and the parents are genotyped at medium density , this is sufficient to obtain near-maximal genomic prediction accuracy. Since genotyping for very low density panels is considerably cheaper than genotyping for medium or high density panels, this genotype imputation approach may be a cost effective tool for genomic prediction in Atlantic salmon.High genotype imputation accuracy can be achieved in the context of a salmon breeding program by genotyping parents at medium density and offspring at low density. When offspring are genotyped at very low density (


We examined phenotypic data and identified associations for the Transmembrane protein 8B (TMEM8B) rs426272889 SNP and its genetically linked Sperm-associated antigen 8 (SPAG8) rs160159557 SNP with ewe mature weight in four sheep breeds. These data provided the first production-relevant phenotypes, as well as the first organism-level (as opposed to cellular or tumor-derived) phenotypes, associated with TMEM8B, and in so doing, improved the annotation of this gene and genomic region by adding body weight implications. Once validated, these data can be applied in genetic or genomic selection aiming to achieve desired mature body weight.Domestication and the subsequent selection of animals for either economic or morphological features can impact the legacy genome of a population in myriad ways. In sheep, the TMEM8B)rs426272889 as having been recently under extreme selection pressure in domesticated sheep, but no study has provided sheep phenotypic data clarifying a reason for extreme selection. We tested rs426272889 for production trait association in 770 U.S. Rambouillet, Targhee, Polypay, and Suffolk sheep. TMEM8Brs426272889 was associated with mature weight at 3 and 4 years (p < 0.05). This suggested selection for sheep growth and body size might explain the historical extreme selection pressure in this genomic region. We also tested Sperm-associated antigen 8 (SPAG8) rs160159557 encoding a G493C substitution. While this variant was associated with mature weights at ages 3 and 4, it was not as strongly associated as TMEM8Brs426272889. Transmembrane protein 8B has little functional information except as an inhibitor of cancer cell proliferation. To our knowledge, this is the first study linking TMEM8B to whole organism growth and body size under standard conditions. Additional work will be necessary to identify the underlying functional variant(s). Once identified, such variants could be used to improve sheep production through selective breeding.Signature of selection studies have identified many genomic regions with known functional importance and some without verified functional roles. Multiple studies have identified Transmembrane protein 8B ( Sheep were domesticated in the Fertile Crescent, where their wild relatives remain, 10,500 years before the present . These frs426272889) on OAR2 was identified with strong evidence for a signature of selection in sheep. Although Kijas et al. [NPR2) as the nearest candidate gene based on genome annotation at the time, the SNP is located in the intron of Transmembrane protein 8B (TMEM8B) according to a more recent sheep genome assembly Oar_v3.1 [SPAG8), Histidine triad nucleotide binding protein 2 (HINT2), TMEM8B, and NPR2 are found was also identified as a signature of selection in various other sheep populations. Additional signatures of selection in domestic sheep have been identified in Chinese indigenous sheep breeds [rs160159557) in SPAG8 gene, which is located near the reference SNP (rs426272889). Furthermore, to the best of our knowledge, no other research has been conducted on the association between TMEM8B and SPAG8 polymorphisms and mature ewe bodyweight and production traits.Kijas et al.  used thoOar_v3.1 . Interesp breeds , Ethiopip breeds , worldwip breeds , Brazilip breeds , Westernp breeds , and Latp breeds . Beside TMEM8B rs426272889 and the SPAG8 Gly493Cys variant with individual ewe growth and lifetime production traits in US Polypay, Rambouillet, Targhee, and Suffolk sheep.Therefore, the objectives of this study were to investigate the associations of All animal care and use practices were reviewed by the Institutional Animal Care and Use Committees of Washington State University  and the USDA, Range Sheep Production Efficiency Research Unit . Samples of distal ear tip or blood obtained via jugular venipuncture were collected from 770 Polypay, Rambouillet, Targhee, and Suffolk ewes at the U.S. Sheep Experiment Station . Specifically, there were 188 Polypay, 359 Rambouillet, 168 Targhee, and 55 Suffolk ewes. Details of recorded phenotypes were previously given in Cinar et al. [TMEM8B  and SPAG8  using TaqMan SNP Genotyping Custom Assays . Briefly, genotyping was performed using allelic discrimination with primers  and hydrolysis probes  for rs426272889  and for rs160159557  with TaqMan Genotyping PCR Master Mix  and 50 ng of DNA in a final reaction of 10 μL in an ABI StepOnePlus Real-Time PCR System . Genotyping assays aimed for 95% genotyping rates in these data sets.DNA was isolated from whole blood using the Invitrogen GeneCatcherTM gDNA 3-10 mL Blood Kit per manufacturers’ instructions . For ear tip samples, 3 to 5 mg of ear tissue had excess hair removed and then was placed in a Lysing Matrix A tube  between two ceramic beads. DNA was extracted using the ChargeSwitch gDNA Micro Tissue Kit . Briefly, the tissue was allowed to rehydrate overnight at room temperature in lysis buffer (L15) and then processed with the Fast Prep FP120 machine  followed by DNA isolation according to manufacturer’s protocol. Genotyping of single nucleotide polymorphisms included D′ and r2). Phenotypic data were checked for normality before analyses with the univariate procedure of SAS v9.4 . Genotype frequencies were calculated using the allele procedure of SAS v9.4. Individual animal data were analyzed with a mixed model using the mixed procedure of SAS v9.4. The reduced model included fixed effects of breed, year of birth (corresponded to age in years for this study), and genotype, with a random effect of sire nested within breed. Because Suffolks were monomorphic, they were not included in further analysis. In total, there were 238 sires in the data set, composed of 117 Rambouillet, 66 Polypay, and 55 Targhee. In addition, single breed analyses were run with similar models except no breed term and sire run without nesting. Genotypic comparisons were reported following Tukey-Kramer adjustment, and p ≤ 0.05 was considered significant.The Haploview software package   was usedr2 and D′ were estimated measures of linkage disequilibrium for the TMEM8B and SPAG8 SNPs on OAR2 . Three significant associations also were observed between rs160159557 SNP and ewe mature weights in spring at age 3, fall at age 3, and spring at age 4 years (TMEM8B rs426272889 (p = 0.058) and for SPAG8 rs160159557 (p = 0.087). However, no additional associations were identified between rs42627288 or rs160159557 SNPs with other measured individual and lifetime ewe production and prolificacy traits in Idaho sheep populations (p > 0.05).Descriptive statistics regarding measured phenotypes were given in  on OAR2 . The r2 ese loci , so theyanalysis . The TT analysis , and for 4 years . Similar 4 years , except TMEM8B and SPAG8 SNPs and ewe mature body weight  and was originally isolated from nasopharyngeal carcinoma ,22 and cin vitro . Althougin vitro . To the SPAG8 and any growth trait in humans or animals, loci near TMEM8B and SPAG8 have been suggested as signatures of selection both in sheep and cattle [SPAG8 was previously designated as hSMP-1 or HSD-1 and originally isolated from a human testis expression library using antisperm antibodies obtained from the serum of an infertile woman . SPAG8 hd cattle ,27,28,29TMEM8B that could be more functionally important in body size determination. The one other potential gene that is within 50 kb of the reference TMEM8B SNP is the natriuretic peptide receptor-B (NPR2) suggested by Kijas et al. [NPR2 gene lead to impairment of skeletal development and short stature [TMEM8B rs426272889 could mediate effects through TMEM8B, NPR2, or both. For example, U.S. Suffolk sheep have been heavily selected for increased weaning weights and have developed large frames. The Suffolk sheep in this study were homozygous TT for both rs426272889 and rs160159557, which could have resulted from selection for growth rates and body size [The presents results may suggest one or more additional genetic variants in linkage disequilibrium with the reference SNP in tor-B NPR suggestes et al. , while d stature ,32. Giveody size .Many signatures of selection have been detected in various livestock species, including sheep ,12,13,14TMEM8B rs426272889 SNP with ewe mature weight in a multibreed sheep population (rs426272889 SNP with direct phenotype measurements—in this case, mature body weight—in a large panel of sheep breeds. In conclusion, the present study revealed that TMEM8B and SPAG8 are significantly associated with 3- and 4-year-old ewe mature weight, suggesting that rs426272889 and rs160159557 genotypes may serve as candidate genetic variants for marker-assisted selection programs in terms of lower mature body weight in sheep.Here, we report the association of sheep selection signature pulation . Validat


Here, we present the development of a genome-wide SNP assay for the Black Tiger shrimp (Penaeus monodon) through utilisation of a reduced-representation whole-genome genotyping approach (DArTseq).The development of genome-wide genotyping resources has provided terrestrial livestock and crop industries with the unique ability to accurately assess genomic relationships between individuals, uncover the genetic architecture of commercial traits, as well as identify superior individuals for selection based on their specific genetic profile. Utilising recent advancements in Based on a single reduced-representation library, 31,262 polymorphic SNPs were identified across 650 individuals obtained from Australian wild stocks and commercial aquaculture populations. After filtering to remove SNPs with low read depth, low MAF, low call rate, deviation from HWE, and non-Mendelian inheritance, 7542 high-quality SNPs were retained. From these, 4236 high-quality genome-wide loci were selected for baits-probe development and 4194 SNPs were included within a finalized target-capture genotype-by-sequence assay (DArTcap). This assay was designed for routine and cost effective commercial application in large scale breeding programs, and demonstrates higher confidence in genotype calls through increased call rate (from 80.2 ± 14.7 to 93.0% ± 3.5%), increased read depth (from 20.4 ± 15.6 to 80.0 ± 88.7), as well as a 3-fold reduction in cost over traditional genotype-by-sequencing approaches.P. monodon industry with the ability to simultaneously assign parentage of communally reared animals, undertake genomic relationship analysis, manage mate pairings between cryptic family lines, as well as undertake advance studies of genome and trait architecture. Critically this assay can be cost effectively applied as P. monodon breeding programs transition to undertaking genomic selection.Importantly, this assay equips the  Genotype-by-sequencing (GBS) has quickly been recognised as a highly versatile and cost-effective approach to rapidly generate genome-wide marker data for an emerging aquaculture species, or those otherwise lacking existing SNP-based genomic resources . In partAcross the range of RAD-Seq methods now available, each protocol contains its own subtle differences in methods undertaken throughout the sample-to-sequencing process, which have been reviewed in detail by Puritz , AndrewsIn an effort to further ensure consistent sequencing of the same genomic regions, a number of RAD-Seq methods have been paired with ‘targeted sequence capture’ protocols to enrich desired sequences (known as ‘RAD-tags’) in the sequencing libraries before final preparation for high-throughput next-generation sequencing . SpecifiBy following this two-stage library preparation approach, it is possible to further improve the consistency of genotype data compared to traditional RAD methods in two ways. Firstly, it is possible to obtain higher sequence read coverage of a refined set of loci which improves confidence in genotype calls . SecondlPenaeus monodon) are an aquaculture species of significant value , howev billion , the ind billion –21;). Wh billion –24), AFL billion , low-den billion ), these  billion , 26, 27. billion . To dateHere, the processes and rational underlying the development of a hybrid ‘RAD-Seq target-capture’ GBS assay for application within a black tiger shrimp industrial aquaculture setting are detailed. We demonstrate the versatility of this assay through traditional parentage assignment of animals reared under communal commercial conditions (i.e. pedigree unknown), as well as the utility of this assay in GRM calculations which set the foundations for accurate estimations of GEBVs. The ability to generate accurate GEBVs is integral for the establishment of genomic selection programs in black tiger shrimp.After processing raw sequencing data from a DArTseq GBS library of 650 individuals , a dataset of 24,683 RAD-tags containing 31,262 SNP markers (31 K SNP dataset), was returned. An average of 1.37 SNPs ±0.6 SD were present in each RAD-tag, with a maximum of six SNPs observed in a single RAD-tag . For this raw 31 K SNP dataset, the average genotype call rate was 0.86 ± 0.14 SD and the average MAF was 0.11 ± 0.15 SD.An average of ~ 2.5 million reads were allocated to each individual in library preparation and sequencing; however, after removal of low quality sequences, monomorphic loci, and SilicoDArT markers (presence/absence variants), an average of 459,987 ± 88,493 SD reads were associated with each individual . The average read depth over all non-missing genotype calls was 17.0 ± 18.3 SD, however, 27.0% of non-missing genotype calls had 5 or less reads associated.To ensure only the highest quality markers were available for baits probe selection and assay development, a series of SNP filtering thresholds were implemented to remove individual genotype calls with low confidence, remove less informative markers and remove erroneous data Fig. . FilteriIn total 23,720 SNPs were removed from the 31 K dataset, leaving 7542 high quality SNPs for further ranking and final SNP selection. After filtering, the average MAF increased from 0.11 ± 0.15 to 0.22 ± 0.15. While the average per genotype call rate decreased from 0.86 ± 0.14 to 0.84 ± 0.14, the average read depth per genotype call increased from 17.0 ± 18.2 SD to 30.7 ± 27.9 SD. Furthermore, the rate of average Mendelian inheritance errors detected across SNPs decreased from 3.2 to 1.4% after filtering.To determine the number of markers required for accurate genomic relationship calculations and selection of the density of the DArTcap panel, a number of marker subsets were modelled. An increasingly consistent GRM estimate was achieved by including progressively more random markers from 100 to 1000 markers Fig. . With 10r2 = 0.987) to the full 7542 marker panel  in the 7542 SNP dataset when considering all available individual genotypes, while only two of these marker pairs persisted within the selected 4236 SNPs . When assessing genotypes of the Northern Territory individuals only  from the 7542 SNP dataset, there were three marker pairs with r2 values greater than 0.8. Likewise, for East Australian Coast individuals only , there were 13 r2 values greater than 0.80, however, only one of these was also observed in the Northern Territory population. Using only Northern Territory individuals from the 4236 SNP dataset, there was one marker pair identified to be in LD (r2 > 0.80). Likewise, when considering genotypes of East Australian Coast individuals, there were 12 r2 values greater than 0.80, with one pair shared with those observed in the Northern Territory population.Limited evidence of substantial linkage disequilibrium was observed with the DArTseq datasets. A total of 4 marker pairs were found to be linked  of the selected 4236 panel were observed, with only 52 baits probes failing to capture the desired rad-tag sequences. For the 4194 DArTcap dataset, the average individual call rate was 93.0% ± 3.5% SD, and average MAF was 0.23 ± 0.15 SD. An average of 312,343 ± 83,235 reads were associated with each individual . An average of 46.8% ± 1.6% of the total DArTcap sequencing effort was successful in obtaining target sequences per individual. The average read depth per non-missing genotype call was 80.0 ± 88.7 SD, while 5.78% of non-missing genotype calls had 5 or less reads associated.The results of parentage assignment success using Colony are presented in Table Across all analyses, progeny containing higher missing data (> 10%) accounted for the majority of false parent exclusions. Furthermore, irrespective of the markers used in the analysis, there was a single individual where at least one of the two true parents could not be assigned consistently. For this individual, across analyses the unassigned parent was not consistent with either both parents unassigned, only the mother unassigned, or only the father unassigned. There were no untrue parent assignments  observed across any of the assignment analyses, with all putative parents correctly excluded where the true parent/s were absent from the dataset tested.r2 = 0.98; Fig. r2 = 0.98; Fig. Genomic relationship values calculated with the 4194 DArTcap markers showed high correlation to the 7542 DArTseq marker set (n = 272) that were produced from novel Northern Territory sourced broodstock (i.e. not genotyped with either DArTseq or DArTcap). Utilising the GRM values of progeny alone, in the absence of parental genotypes, it is possible to clearly obtain sib-ship information . By comparing the first two principle components (with PC1 explaining 52.8% and PC2 explaining 44.3% of the variation), three distinct clusters of individuals are clearly identified  genotyping of 650 individuals collected from across the natural range of Australian P. monodon , the lois study . Effectiis study –50. In fWhile a small number of marker pairs were observed to have linkage disequilibrium (LD) greater than 0.8, only one of these pairs was regarded as highly linked in both Northern Territory and East Australian Coast regions. Given the genetic distinction between regions Fig. , it is eP. monodon cultured in Australia are not yet distinctly separated from wild stock , the majority of commercial P. monodon genome has since been made available , it is P. monodon industry it would be of interest to further assess the utility of this assay across potentially divergent international populations and commercial stocks.A secondary SNP  was obtained for a number of the probes and for routine application should be removed (as these are in complete linkage with target SNP); however they may represent additional valuable information if combined into RAD-tag haplotypes , or whenThe sequencing effort applied to each sample was markedly reduced for each individual  in DArTcap over DArTseq; however the average read depth obtained for each genotype call was higher (80.03 reads in DArTcap vs 17.01 reads DArTSeq), substantially increasing the confidence and accuracy of genotype calls. Furthermore, given sequencing costs are the most substantial cost associated with GBS, being able to reduce the total allocation required to obtain robust data is critical for routine applications involving the genotyping of thousands of individuals annually as required by breeding programs or when undertaking genome-wide association studies. Through utilising DArTcap genotyping over DArTseq genotyping, three times the total number of individuals can be assessed for an equivalent fixed financial investment .P. monodon industry, progeny are produced from mass spawning events of multiple broodstock, with progeny of many families being reared communally from fertilisation onwards. Quite often it is not possible to manually tag or separate family lines. As a result, genotyping progeny and broodstock, and assigning parentage through molecular means is essential as it enables the recovery of pedigree information and ensures breeding programs are managed effectively . In adAs such we placed the highest emphasis on marker minor allele frequency and call rate to ensure the allele frequencies remain high enough for use across multiple generations of selective breeding, and the markers were present in as many genotyped individuals as possible (avoiding population specific markers and those that were difficult to sequence). With reduced error in mind, markers showing on average higher read depths and higher repeatability of genotype calls  were preferentially ranked well. Furthermore, SNPs with low levels of MI error and small deviations from HWE were expected to improve with increased sequencing depth of DArTcap genotyping, and comparatively were not ranked down aggressively, instead the SNPs representing the majority of MI errors (> 9%) and significant deviations from HWE in both populations were removed earlier.Markers with excessive read depth (e.g. > 200 reads/ genotype) should also be avoided, as this may indicate two markers erroneously being called together from paralogs (over-clustered), or located in highly repetitive regions , 77. FilA method to routinely genotype thousands of individuals is required to undertake advanced selective breeding in black tiger shrimp. This study described the development and validation of a crucial genotyping resource, which by utilising target capture genotype by sequencing (DArTcap) can for the first time be cost-effectively utilised in routinely commercial breeding (< $15 AUD per sample). This assay containing 4194 SNPs, simultaneously provides the ability reconstruct the pedigree of communally reared families, while also being able to accurately calculate the cryptic genomic relationships between individuals. Furthermore, this assay will facilitate genome-wide association studies, linkage mapping, and unlocks the ability to undertake genomic selection black tiger shrimp.P. monodon . Given the aims and design of this study, a priori sample size calculations were not appropriate. Farm stock were provided for sampling by Seafarms Group Ltd. as part of routine operational practises, while individuals from natural populations were obtained from CSIRO fishery surveys and were collected within the guidelines of appropriate permits for each location. All individuals were euthanized upon collection through immersion in an ice-slurry. Pleopod tissue or whole post larvae were collected and stored in ethanol or RNA-later at − 20 °C until extraction. Genomic DNA was extracted either following the CTAB extraction protocol or MagJET Genomic DNA Kit  approach, DArTseq, was employed for SNP discovery through a service provider, Diversity Arrays Technologies . To allow a measure of technical repeatability in genotype calls and library preparation, 147 random replicates (23%) were included within the library preparation process. Equimolar amounts of barcoded samples were pooled, with 94 samples forming a single pool, before sequencing cluster preparation on the Illumina c-Bot bridge PCR system. Libraries were sequenced on three flow cell lanes on an Illumina HiSeq2500 to provide an average of 2.5 million raw reads per individual.In the absence of existing published GBS datasets for es . High es  were excluded from the data set.Reads with low sequence quality scores (Q < 25) were eliminated and SNP calling was completed using the KDcompute pipeline . Follogithub.com/esteinig/dartQC) and undertake preliminary SNP filtering for quality. Briefly, genotype calls were silenced on the basis of low cumulative raw read depth , before SNPs with minor allele frequency (MAF) less than 0.02, call rate less than 50% or less than 90% repeatability were removed. Next, SNP redundancy filtering was undertaken, whereby sequence clusters (RAD-tag or clones) were identified using the CD-HIT clustering algorithm at 95% identity [To ensure only high quality markers were included in the final target capture genotyping assay, multiple steps of filtering were applied to the dataset before the remaining SNPs were ranked on quality and level of polymorphism prior to final selection. Custom python scripts were developed to efficiently handle the datasets provided by DArT  and Northern Territory stocks (n = 125) (Table p < 0.0001) from HWE in both populations were removed. Known parental trio relationships  were utilised to identify SNPs that displayed Mendelian Inheritance (MI) errors using PLINK [Hardy Weinberg Equilibrium (HWE) deviations were calculated within PLINK  utilizinng PLINK . MI errong PLINK . SNPs wiP. monodon, calculations were undertaken using a similar approach to Rolf [G). The programs R v3.4.1 and PLINK v1.9 [n = 100, 200, 500, 1000, 1500, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000), with replacement, from the pool of 7542 SNPs available. For each marker subset (n), 50 random replicates (i) were generated and a GRM (Gni) was estimated using GCTA v1.91.7b [Gni GRM and the corresponding relatedness values of G. Average pairwise relatedness values across the 50 replicates for each marker subset were calculated before correlation plots were produced using the R package ggplot2 [r2 correlation of 0.98 to the full 7542 array. Therefore, this defined the target size of the DArTcap sub-array to be developed.To determine the number of markers required to accurately obtain GRMs in  to Rolf . GRMs caINK v1.9  were usev1.91.7b . Correla ggplot2 . Such GRQC score) was developed to rank each SNP by a range of SNP quality metrics (Eq. CR) which ranged from 0 to 1 with a weighting of 1.4; minor allele frequency (MAF) ranging from 0 to 0.5 with a weighting of 2.4; average repeatability (Rep_Avg) ranging from 0 to 1; standardised read depth (RD) whereby the average read depth of the SNP was divided by the largest read depth observed to give a proportion between 0 and 1; Hardy Weinberg Equilibrium deviation (HWE) as a percentage; and Mendelian inheritance error rate (MI).To prioritise the selection of the highest quality SNPs in the final assay, a custom quality score  during probe synthesis. Furthermore, a GRM was calculated using the 4236 subset of markers and compared to the full 7542 markers to ensure concordance was maintained. Selected sequences were provided to DArT, and DArTcap probes synthesized  for testing.n = 125) and East Australian Coast (n = 132) regions independently. Pairwise LD values between loci greater than > 0.2 were reported, and then compared between Northern Territory and East Australian Coast regions to identify if any markers were consistently in complete (r2 = 1) or high LD (r2 > 0.8).To approximate the distribution and independence of markers across the genome, linkage disequilibrium (LD) was calculated across the complete datasets of 7542 SNPs and 4236 DArTseq SNPs with all available samples in PLINK . SimilarValidation of the DArTcap probes involved the re-genotyping of a subset of 251 individuals from the discovery population . A total of 46 progeny and 56 broodstock with known parent-progeny relationships were available for genotyping on both the DArTseq and DArTcap platforms. A number of additional broodstock with known parent-progeny relationships had insufficient DNA when undertaking DArTcap genotyping and were excluded. For these 72 family pairwise relationships, parentage assignment was undertaken in the program Colony . Repeaten = 195) in the two datasets were compared..To confirm the estimates of relatedness obtained from DArTcap genotyping (4129 SNPs) were concordant with those of the selected 4236 DArTseq markers, a GRM was calculated in GCTA v1.91.7b for both datasets and all common individuals  secondly retaining only wild sourced broodstock and wild samples and excluding all G2 individuals (n = 168) to ensure closely related individuals were not influencing the analysis, and lastly with only an independent set of G2 samples (n = 272) to assess the ability to discriminate between family lines. Concordance of sample placement within source populations was assessed.To confirm the DArTcap assay retained sufficient informativeness to distinguish between individuals from distinct populations, we completed discriminant analysis of principal components (DAPC) using the R package adegenet . We compAdditional file 1: Supplementary Figure 1.Penaeus monodon distribution across Australia (light grey), approximate locations of current pond based farming operations (dark grey), and location of samples included in DARTcap ‘discovery’ populations (1 – Joseph Bonaparte Bay (n = 34), 2 – Tiwi Islands (n = 56), 3 – Gulf of Carpentaria (n = 43), 4 – Bramston Beach (n = 60), 5 – Etty Bay (n = 50), 6 – Townsville (n = 22) and 7 – Commercial Farm site (n = 394). Supplementary Figure 2. Clustering of samples based upon genetic similarity shown through discriminant analysis of principle components (DAPC). PC1 and PC2 are shown on the x and y axis respectively. (A) Including all samples (n = 418). (B) Including only second generation (G2) individuals (n = 272) obtained from routine commercial spawning.


The homology and evolution of the archosaur ankle is a controversial topic that has been deeply studied using evidence from both extinct and extant taxa. In early stem archosaurs, the astragalus and calcaneum form the ancestral proximal tarsus and a single ossification composes the centrale series. In more recent stem archosaurs, the centrale is incorporated to the proximal row of tarsals laterally contacting the astragalus. This bone is subsequently lost as an independent ossification before the last common ancestor of birds and crocodilians, but the evolutionary fate of this element remains mostly unexplored. Here, we integrate embryological and palaeontological data with morphogeometric analyses to test the hypothesis of loss of the centrale or, alternatively, its incorporation into the archosaurian astragalus. Our results support the latter hypothesis, indicating that the astragalus developed ancestrally from two ossification centres in stem archosaurs and that the supposed tibiale of bird embryos represents a centrale. This conclusion agrees with previous embryological studies that concluded that the tibiale never develops in diapsids. In particular, the homology of the primordial cartilages and their subsequent ossifications that form the archosaur tarsus has been a widely discussed topic that integrates palaeontological and embryological studies12. The proximal tarsus of archosaurs is ancestrally composed of a medial astragalus that articulates proximally with the tibia and fibula and a lateral calcaneum that articulates proximally with the fibula13. Separate astragalar and calcaneal ossifications have been retained in Crocodylia, but both bones fuse into a single proximal tarsal in Aves7. During the embryological development of extant crocodilians, the chondrogenic proximal tarsus possesses three condensations – the intermedium, centrale and fibulare – that subsequently ossify into two elements – the astragalus and calcaneum –11.The tarsus of archosaurs – crocodilians, birds, non-avian dinosaurs and several other extinct clades – and their most immediate precursors has been one of the most deeply studied anatomical regions of this clade because of its strong phylogenetic signal and morphofunctional importanceMoradisaurus grandis14) is retained in stem archosaurs5. This single centrale is located immediately mediodistal to the astragalus in the earliest archosauromorphs (e.g. Protorosaurus speneri15) but is incorporated to the proximal row of tarsals, laterally contacting the astragalus, in more crownward stem archosaurs16. As a result, in the latter taxa, the centrale, together with the astragalus, forms part of the articular facet for the distal end of tibia. In archosauromorphs more closely related to Archosauria, the centrale is lost as an independent ossification16. The absence of a centrale has long been recognized as a synapomorphy within stem Archosauria16, but the evolutionary fate of this element has received relatively poor attention. Some authors have proposed that the centrale fuses with the astragalus to form a single medial proximal tarsal17. However, this hypothesis remains untested. Here, we integrate embryological and palaeontological data and quantitative methodologies to test the hypothesis of fusion between the centrale and astragalus, or the alternative hypothesis of a complete loss of this element. The potential recognition of the centrale as a component of the proximal tarsus of archosaurs has interesting evolutionary implications in the discussion of the homology of the primordial cartilages that form the astragalus in amniotes and the homology of the cartilaginous condensations of bird embryos.A single ossification originating from the four elements that are proposed to compose the centrale series of amniotes  there are two cartilaginous condensations that articulate proximally and proximolaterally with the distal end of tibia . We interpret these condensations as the intermedium laterally and the centrale medially. The proximal surface of the intermedium articulates with the lateral and medial halves of the distal end of tibia and fibula, respectively. The lateral surface of the intermedium articulates with the fibulare and there is a thin layer of cartilage that will participates in the future crurotarsal joint. This same layer extends distally to form the articulation between the fibulare and a large distal tarsal 4. The distal tarsal 3 is present medial to the distal tarsal 4 and proximal to the metatarsal III, and immediately proximal to metatarsal II there is a small, spherical condensation that we identify as distal tarsal 2. In the later embryonic stage (MLP-R.6490 CY-19) there is no differentiation between an intermedium and centrale, which creates a single centre of ossification, the astragalus, in more advanced embryos. In all sampled stages of both species we have not observed a segmentation or a condensation adjacent to the distal end of the tibia that may indicate the development of a tibiale, in agreement with the absence of this element in at least several diapsids – including turtles19 – (e.g. refs. 20).In the earliest-sampled embryonic growth stages of bia Fig. , resembl16 , Azendohsaurus madagaskarensis17 and Shringasaurus indicus21 present such fusion, but in the trilophosaurid allokotosaur Trilophosaurus buettneri its presence is polymorphic and apparently not related to the size of the individuals17. The astragalus and centrale are separated from each other in Early and Middle Triassic rhynchosaurs25, but the co-ossification between both bones occurs in large-sized individuals of at least some hyperodapedontine rhynchosaurs17. Among the Proterosuchidae, a small-sized and probably immature individual of Proterosuchus fergusi (SAM-PK-K14026) possesses an astragalus unfused to the centrale (the latter bone is not preserved in this specimen). By contrast, the astragalus is co-ossified with the centrale in larger specimens of the genus . Thus, at least in proterosuchids, it is possible that the fusion between the centrale and astragalus occurs later in post-hatching ontogeny. In those crocopods, in which the centrale is fused to the astragalus, a line of suture or ventral cleft persists showing the original separation between both elements , the centrale occupies a proximal position in the tarsus and is located medial to the astragalus, participating in the articulation with the tibia16 Fig. . In addints Fig. . In Erytnts Fig. .16. In the trees optimizing the astragalus + centrale, other changes in landmark and semilandmark positions are considerably lower and the transformations of the landmarks that describe the shape of the medial margin of the structure (i.e. Landmark 2 and semilandmark 2) are very minor. By contrast, in the other configuration, there is a distinct ventromedial displacement of Landmark 2 and a lower, but conspicuous, dorsolateral displacement of semilandmark 2. These changes in the latter optimization show that a medial expansion of the astragalus is the most parsimonious transformation in this branch. However, in the other configuration , the medial expansion of the astragalus is not necessary because this space is occupied by the centrale in more basal branches.The optimization of the two alternative morphogeometric configurations  in the phylogenetic supertrees  recovers the transformation of an astragalus + centrale into the astragalus as more parsimonious than the alternative hypothesis of loss of the centrale  Fig. ; Table 1In the Principal Component Analysis, the early archosauromorphs that retain a distinct centrale  occur at the bottom of the right side of the morphospace . The results recovered from the optimization of the morphogeometric configurations in the phylogenies and Principal Component Analyses are the first quantitative evidences that support the hypothesis that the centrale has actually fused to the astragalus to form a single ossification in the Erythrosuchidae + Eucrocopoda clade rather than that the centrale failed to ossify. The hypothesis of fusion between the astragalus and centrale agrees with the retention of a suture line in the co-ossified elements that form the medial proximal tarsal of allokotosaurs, some hyperodapedontine rhynchosaurs and medium- to large-sized proterosuchid specimens. The astragalus-centrale fusion also matches the topological relationship and subsequent fusion between the two chondrogenic condensations during the embryological development of the medial proximal tarsal in crocodilians  and birds . The fusion between the astragalus and centrale occurs during the post-hatching ontogeny in proterosuchid archosauriforms, but the merging of the structures that we consider as equivalent  takes place during the stage of cartilaginous condensation in the tarsus of crocodilian and bird embryos. This results in a single ossification centre that forms the definitive astragalus of extant archosaurs. As a consequence, we interpret that a peramorphic heterochronic event in the timing of fusion between the components of the medial proximal tarsal should have occurred in the branch leading to the Erythrosuchidae + Eucrocopoda clade.Some previous authors have interpreted that the medial proximal tarsal of Archosauria is a result of the fusion between the centrale and astragalus that occur as separate ossifications in post-hatching individuals ancestrally in Archosauromorpha  and another chondrogenic condensation forms the astragalar body during the embryological development of birds. These authors identified this latter condensation as a tibiale following previous embryological studies of birds, although they acknowledge that other authors have proposed the absence of a tibiale in reptiles and that this cartilage was alternatively interpreted as a proximal centrale11. If a tibiale homologous to the ossified tibiale of early tetrapods is present in bird embryos it would contradict the hypothesis supported here of the incorporation of the centrale to the astragalus –to form a single centre of ossification– in early archosaurs and their immediate precursors .Multiple embryological studies have shown that there are two chondrogenic condensations that form the single ossification centre that constitutes the astragalus of crocodilians and birds. In particular, Ossa-Fuentes 11 and embryological studies on reptile limb development have not observed such segmentation and, as a result, proposed that the tibiale never forms in diapsids20. Instead, the element that chondrifies in the equivalent position as the tibiale in diapsid embryos lacks a connection with the distal end of tibia, but it contacts the intermedium and has been therefore interpreted as a centrale10. As a consequence, the medialmost chondrification in the proximal tarsus of bird embryos should not be homologous to a tibiale. The evidence presented here for an archosaur astragalus composed of the ancestral centrale and astragalus of diapsids reinforces the interpretation that the two chondrogenic condensations that form the astragalus in birds should be identified as the intermedium and the centrale, as occurs in lepidosauromorph, turtle and crocodilian embryos (e.g. refs. 29).The tibiale develops from a transverse segmentation of the distal end of the tibia in non-amniote tetrapodsAmbystoma10). Thus, the absence of a tibiale in Diapsida implies the absence of such preaxial centrale. Previous authors have interpreted that the intermedium and centrale 4 are integrated to form the amniote astragalus (the three-centre model of Peabody8). An alternative hypothesis, the four-centre model, also includes the centrale 3 as part of the amniote astragalus14. As a consequence, following these evolutionary hypotheses that the centrale 4, and possibly the centrale 3 as well, form part of the astragalus, the single independent centrale ossification retained in archosauromorphs should be homologous to the centrale 2 (given the four-centre model) or centrale 2 + 3 (given the three-centre model) of non-amniote tetrapods. This interpretation contradicts the traditional hypothesis that the centrale that chondrifies in the tarsus of diapsid embryos is homologous to the centrale 4, a view often followed by embryological studies (e.g. refs. 29). In addition, the centrale of crocodilian embryos and early archosauromorphs shows a topological relationship that matches the position of centrale 2 in early tetrapods14.Our interpretations also have implications for the homology of the single centrale ossification retained in archosauromorphs. The separate chondrogenic condensation of the most preaxial centrale  originates from a segmentation of the tibiale in non-amniote tetrapods  under a collection number for each ontogenetic series, MLP-R.6490 for C. yacare and MLP-R.6491 for C. latirostris (Supplementary Table Caiman latirostris) and CY (Caiman yacare) followed by the number of the embryonic stage sensu Iungman et al.31. The material was collected during three field trips to the Chaco Province in 2012, 2015 and 2018. All the eggs belong to two nests per species removed from nature and were artificially incubated with constant conditions of relative humidity (95%) and temperature (30 °C ± 1). Embryos of both species were collected every day until hatching, fixed and kept in a 5% formaldehyde solution with calcium carbonate. Embryonic stages 20 to 27–28 and 17/18 to 25  were sampled in C. latirostris and C. yacare, respectively. Embryos were prepared for the observation of cartilage and bone according to the double staining and diaphanization technique of Taylor & Van Dyke32. Fixed embryos were immersed in baths of successively increasing alcohol concentration, embedded in Alcian Blue, submerged in 1% KOH, inserted in Alizarin red, and finally submerged again in 1% KOH. They were left in KOH solution until the material was totally cleared and the cartilages and bones were visible. Each specimen is conserved in glycerol and was studied using a Zeiss stereomicroscope and a NIKON Stereo Microscope SMZ745/SMZ745T magnifier with a NIKON NI-150 Illuminator illumination. Photographs were taken using a Nikon D40 camera.Pre-hatching ontogenetic series of 37 embryos of We complied with all relevant ethical regulations for animal testing and research. All experimental protocols concerning embryonic manipulation were developed during the PhD research of MVFB and approved by the Facultad de Ciencias Naturales y Museo-Universidad de La Plata, accordingly with the governmental regulations of the Chaco Province.33 and two semilandmarks  of Triassic archosauromorphs. The landmarks were placed on photographs of the astragali and centrale in anterior view taken at first hand by one of the authors (MDE), and some other photographs were taken from the literature. The sampled archosauromorph species are housed in different repositories worldwide and are listed in Supplementary Table 34 and tpsDig2 2.3135. A Generalized Procrustes analysis was conducted on both configurations, sliding the semilandmarks along their tangent directions using the Procrustes distance criterion, with the function gpagen of the package geomorph 3.0.136 in the software environment R37. A Principal Component Analysis was conducted on all the aligned coordinates and the Sum of Variances was calculated for both groups, respectively, as a descriptor of the morphospace. The aligned coordinates of each configuration were exported to TNT 1.538 and they were used as a single morphogeometric continuous character to test each hypothesis. The configurations were re-aligned in TNT applying the minimum distances criterion and optimized using maximum parsimony on two alternative phylogenetic topologies . Thus, the re-alignment of the data set in TNT was conducted using Pamelaria dolichotrachela as reference taxon because it is the earliest branching of the sampled allokotosaurs.The landmarks and semilandmarks were digitized using the programs tpsUtil 1.76The hypotheses of incorporation of the centrale to the astragalus or complete loss of the former bone in archosauromorphs were tested comparing the length of the branch in which the centrale is absent as a different ossification (i.e. the Erythrosuchidae + Eucrocopoda clade) between the different topologies. In this way, the configuration that requires the highest number of steps in that branch will be the least parsimonious and its associated hypothesis will be rejected over the other. We used a scale in millimetres for the scaling of each morphogeometric configuration and, as a result, the differences in branch lengths and the total number of steps for each tree are expected to be relatively low . However, this optimization is based on maximum parsimony and any difference, regardless of how small it is (contrasting with probabilistic methods), is enough to prefer one hypothesis over others. We did not conduct a probabilistic Templeton test to explore statistical differences between optimizations because this analysis evaluates differences between alternative topologies, but not differences in character optimizations using the same topology.Supplementary Information.