Saccharomyces cerevisiae. The mutation accumulation lines of the S. cerevisiae hybrid backgrounds - S288c/YJM789 (S/Y) and S288c/RM11-1a (S/R) were analyzed along with the homozygous diploids RM11, S288c, and YJM145. LOH was extensive in both S/Y and S/R hybrid backgrounds. The S/Y background also showed longer LOH tracts, gross chromosomal changes, and aneuploidy. Short copy number aberrations were observed in the S/R background. LOH data from the S/Y and S/R hybrids were used to construct a LOH map for S288c to identify hotspots. Further, we observe up to a sixfold difference in single nucleotide mutation rates among the S. cerevisiae S/Y and S/R genetic backgrounds. Our results demonstrate LOH is common during mitotic divisions in S. cerevisiae hybrids and also highlight genome-wide differences in LOH patterns and rates of single nucleotide mutations between commonly used S. cerevisiae hybrid genetic backgrounds.A growing body of evidence suggests that mutation rates exhibit intra-species specific variation. We estimated genome-wide loss of heterozygosity (LOH), gross chromosomal changes, and single nucleotide mutation rates to determine intra-species specific differences in hybrid and homozygous strains of  Another standard method involves the use of mutation accumulation lines (MA lines). Multiple independent lines are propagated from a common ancestor for a large number of generations to facilitate the accumulation of rare mutational events. The effect of natural selection is minimized (via bottlenecks) so that nearly all non-lethal mutations can be recovered. The sequences of the ancestor and the MA lines can be compared with high throughput sequencing to obtain precise and unbiased estimates of genome-wide mutation rates , gross chromosomal changes, and chromosomal aneuploidies. Mutations are among the primary sources of genetic variation but can also cause many genetic disorders . Since mon rates .S. cerevisiae, Drosophila, C. elegans, and other organisms to estimate spontaneous mutation rates (S. cerevisiae is particularly suited for mutation accumulation study as it is a eukaryote with short generation time (∼2.5 hr for mitosis), small genome size (12 Mb), and can be propagated in either haploid or diploid state. Most mutation rate estimates in S. cerevisiae have come from homozygous backgrounds  and LOH in a heterozygous S. cerevisiae genome generated by crossing two strains S288c and YJM789 (−10 per base per cell division), were similar to homozygous S. cerevisiae S288c strain (1.67 × 10−10 per base per cell division), surprisingly we observed more LOH tracts than expected in two of the five S288c/YJM789 hybrid lines, given known mitotic recombination rates in S. cerevisiae  relative to interspecies hybrids while the opposite is true for SNM rates  and S288c / RM11-1a (S/R) with similar heterozygosity and the homozygous diploids S288c (SS), RM11 (RR), and YJM145 (YY) respectively  uniformly distributed in the genome that facilitates analysis of recombination events (S. cerevisiae can show considerable variation in SNM rates and LOH signatures depending on their genetic background.To understand differences in LOH and SNM rates between S. cerevisiae strains used for this study are: S/Y hybrid (KTY83 x KTY84) (MATα ho::hisG lys2 cyh / MATa holys5); S/R hybrid (KTY188 x KTY277) (MATα his3Δ leu2Δ lys2Δ ura3Δ / MATa leu2Δ0 ura3Δ0 HO::KanMX); RR (YJM1293); YY (YJM145); SS (KTY186 x KTY188) (MATa his3Δ leu2Δ met15Δ ura3Δ / MATα his3Δ leu2Δ lys2Δ ura3Δ).The MA lines were grown on YPD  media at 30° , 10 replicate lines were initiated. Five replicate lines were initiated from each of the homozygous diploid parents . Each MA line was propagated by streaking a single colony onto a new plate of YPD agar every two days. Colony selection was randomized. In the 20et al. 2017. Culture samples were withdrawn at periodic intervals until 24 hr, and the cells visualized under the fluorescence microscope after DAPI staining. The sporulation efficiency was calculated as the fraction of cells in MI (2-3 nuclei stage) and MII (4 nuclei stage) to the total number of cells counted (∼200). For analysis of spore viability, the diploids were patched on sporulation media. After two days of incubation at 30°, tetrads were dissected on synthetic complete medium using a Zeiss dissection microscope.Sporulation efficiency of the parent strains was determined by monitoring Meiosis I (MI) and Meiosis II (MII) divisions, as described in Chakraborty th bottleneck only) were independently cultured overnight at 30° in YPD liquid medium. The DNA was isolated from each culture using the PrepEase DNA isolation kit from Affymetrix following the manufacturer’s protocol. The genomic DNA was sequenced using Illumina Hi- Seq 2500 platform at Fasteris, Switzerland.The parent strains and colonies from their respective MA lines  using bowtie2 (version 2.1.0) . UniquelFor analysis of LOH, heterozygous SNP positions were determined in the S/Y and S/R parent genomes. Fixed SNPs (not shared as homozygous in >50% of the MA lines) were identified at these positions using custom R scripts.LOH tracts were called in all the S/Y and S/R MA lines if supported by two or more fixed SNPs. LOH tracts supported by 10 or more fixed SNPs were also separately called  that are overlapping with these bins.The tract size was determined by taking the distance between the mid-points of the 1To analyze gross chromosomal changes, the genome was partitioned into 5 kb bins, and the read counts in each bin were plotted using custom R scripts. The median and 2x median read coverage were also plotted.et al. 2017. Only mutations that were commonly called from these methods were considered. We further filtered away mutations that are not supported by at least 40 reads. Out of 122 new mutations, 20 mutations representing the five lines were verified by Sanger sequencing.Whole-genome sequences of the 35 MA lines was compared with the parent sequences to detect new SNMs. Reads having mapping quality < 40 were removed prior to analysis. Mutations were called using muver  are deposited in the National Centre for Biotechnology Information Sequence Read Archive under accession number: SRP254814 (Bioproject accession number: PRJNA622400). For S/Y parent (S/Y_P), sequence data are available under accession number SRP098673. Supplementary figures include Figure S1: Analysis of LOH (supported by 10 or more SNPs) in S/Y and S/R hybrids; Figure S2: Frequency of heterozygous SNPs in S/Y and S/R hybrids; Figure S3: Analysis of read counts mapping to the chromosomes in the MA lines and parent strains; Figure S4: Verification of SNMs by Sanger sequencing; Figure S5: Sporulation kinetics of S/Y, S/R, RR, YY, SS parent strains and S/Y_8 line. Supplementary tables include Table S1: Summary of sequencing statistics for all 35 MA lines and the parent strains; Table S2: SNP counts in vegetative lines of S/Y hybrids; Table S3: SNP counts in vegetative lines of S/R hybrids; Table S4: Distribution of 316 LOH tracts (supported by two or more SNPs) in the S/Y hybrid; Table S5: Distribution of 942 LOH tracts (supported by two or more SNPs) in the S/R hybrid; Table S6: Distribution of 101 LOH tracts (supported by 10 or more SNPs) in the S/Y hybrid; Table S7: Distribution of 50 LOH tracts (supported by 10 or more SNPs) in the S/R hybrid; Table S8: Spore viability and aneuploidy data of the 35 MA lines and their parent strains; Table S9: Single nucleotide mutations in the 35 MA lines. Supplemental material available at figshare: Vegetative mutation accumulation lines of the S/Y hybrid, S/R hybrid, and the diploid homozygous strains, SS, YY, and RR were set up as described in Materials and Methods . A heat map showing the frequency of heterozygous SNPs also showed longer LOH tracts in S/Y compared to S/R hybrids . We separately analyzed interstitial LOH tracts (not extending to within 100 bp of the telomere), since terminal tracts are likely crossovers that can span much longer distances from a single mitotic DSB repair event. These interstitial tracts most likely reflect DSB repair by gene conversions. The median tract length for interstitial LOH events in S/Y (1.7 kb) was also significantly greater than S/R (242.25 bp) . These differences in LOH tract lengths between the S/Y and S/R hybrids are unlikely to result from differences in marker density since the median distance for markers in S/Y and S/R hybrids is similar (93 bp and 102 bp respectively). For the LOH tracts supported by a minimum of 10 SNPs, we observed contiguous LOH tracts where switching was observed between the S288c and YJM789 haplotypes in three S/Y lines  (Table S6). In the S/R lines, we observed contiguous LOH tracts that showed switching between the S288c and RM11 haplotypes in S/R_8 (Table S7). Since the LOH tracts were analyzed after 100 bottlenecks, it is uncertain whether the haplotype switching resulted from the same DSB repair event or from distinct DSB repair events during the mitotic divisions.We compared LOH tract sizes between the S/Y and S/R lines using a range from less than 100 bp to over 100 kb . We obsee.g., S/Y_8) and S/R lines, the LOH tracts were observed to span the centromere suggesting centromeres are not immune to LOH mediated by mitotic recombination  and S/R  backgrounds with no statistically significant difference . Among aS. cerevisiae S288c strain  in the S/Y and S/R hybrids. Segmental duplications involving chromosome IV and VII were observed in the S/Y_2 line . These sS. cerevisiae, and in laboratory conditions, the spontaneous rate of individual chromosome loss ranges from 10−6 to 10−7 / cell division  (−10) is comparable to the earlier estimate in the S/Y hybrid  is also similar to SNM rate estimates (1.67 × 10−10 per base per generation) from a large set of 145 diploid S. cerevisiae vegetative mutation accumulation lines . SNMs were detected in each of the lines after 100 bottlenecks with reference to their respective parent diploid genome. We observed 8 SNMs in S/R and 47 in S/Y. Among the isogenic lines, we observed 13 SNMs in RR; 26 in SS; 28 in YY. SNMs were called conservatively , and out of 122 new mutations, all 20 mutations tested by Sanger sequencing were validated . All the SNMs in the 35 MA lines were heterozygous, and many were in coding sequences . The 35 MA lines were propagated for a total of ∼2000 mitotic divisions (100 bottlenecks), and the 24.04 Mb genome was sequenced at 99.9% sequence coverage. The average SNM rates for each of the genetic backgrounds are   strain is more mutagenic compared to S288c , the S/Y hybrid showed approximately sixfold (5.76) higher SNM rate ( t-test) . OverallS. cerevisiae and usually involves intra-tetrad mating  that have similar heterozygosity. Our results suggest that the extent of LOH is similar between the two hybrids . IRC20 has a role in homologous recombination and the null mutant is known to have increased Rad52 foci formation  in outliers like the S/Y_8 line (40.26% fixed SNPs) is the occurrence of RTG (return to growth) events where cells transiently enter meiosis and undergo programmed DSBs, but complete the repair of the DSBs during return to mitotic growth (on state .We also observed LOH on a whole chromosome-scale-through chromosome loss followed by reduplication S/Y_7, . Such hyS. cerevisiae pan-genome can undergo LOH (S. cerevisiae hybrid genomes are dynamic and highlight the need for analysis of more heterozygous S. cerevisiae strains to understand the effects of genetic background on LOH and SNM rates.Since our experimental strategy involved the use of MA lines, LOH occurs independently of selection and environmental stress that are known to affect LOH rates and types . A speciergo LOH . Both kn


BADH2 gene. The trait is simply inherited, and its importance in variety development makes it a practical target for marker-assisted selection in applied breeding programs. The predominant functional mutation within BADH2, an 8-bp indel, can be reliably detected using a PCR-based assay, but the available assays and associated genotyping platforms are insufficient for large-scale applied molecular breeding applications and are not compatible with outsourcing genotyping services.Aroma is an important grain quality trait in rice, controlled by mutations within the BADH2 gene in a collection of 2932 rice varieties to determine the number of gene haplotypes in O. sativa. Using 297 gene-based SNPs, 11 haplotype groups were detected, and subsequently identified a minimal set of nine informative SNPs that uniquely identified the BADH2 haplotypes. These nine SNPs were developed into KASP assays and used to examine a panel of 369 U.S. rice accessions. The panel represented modern breeding germplasm and included all known aroma pedigree sources in U.S. rice. Six haplotypes were detected within the U.S. panel, of which two were found in majority (85%) of varieties. A representative set of 39 varieties from all haplotype groups was evaluated phenotypically to distinguish aromatic from non-aromatic lines.We first characterized SNP diversity across the One haplotype (Hap 6) was found to be perfectly associated with the aromatic phenotype. A single KASP SNP unique to Hap 6 was demonstrated to reliably differentiate aromatic from non-aromatic rice varieties across U.S. germplasm. Rice is a staple crop that provides the largest amount of daily caloric intake for human populations globally. Most rice is consumed within the country where it was produced, with only 7% of rice production entering the global export market Giraud, . AromatiOryza sativa) was domesticated roughly 10,000 years ago from pre-differentiated gene pools of the wild grass species Oryza rufipogon  was detected in multiple studies for 2AP content. This gene was reported on Chromosome 8 and explained up to 69% of the variation for 2AP content , and the functional mutation was demonstrated to be an 8-base pair (bp) deletion located in the seventh exon of the gene . Fragrant rice lacks a functioning BADH2 enzyme which causes an accumulation of AB-ald. Due to the inability of the enzyme to convert AB-ald into GABA, an increased synthesis of 2AP results from the accumulated AB-ald being acetylated  determine which BADH2 haplotypes/alleles are present in U.S. rice germplasm, and 3) develop and validate a SNP-based kompetitive allele specific polymerase chain reaction (KASP) assay -based DNA marker suitable for screening U.S. breeding material for fragrance and compatible with commonly used KASP genotyping platforms would be of immediate utility for applied breeding programs. The objectives of this research were to 1) characterize the haplotype diversity of the BADH2 (LOC_Os08g32870) gene was obtained from the International Rice Informatics Consortium (IRIC) SNP-Seek database, from the “3kAll” SNP dataset (http://snp-seek.irri.org/) , Arkansas (93), Texas (74), Mississippi (44), and California (8). The international diversity is comprised of germplasm from Italy, Japan, Brazil, Taiwan, and Uruguay. The panel includes short- (7), medium- (45), and long-grain (289) rice accessions as well as a collection of herbicide resistance classes.BADH2 haplotypes found in the 3 K genomes, the 255 SNPs found within the BADH2 gene were analyzed across the 11 haplotypes present in the 3 K accessions. The SNPs were sorted by minor allele frequency among the 11 haplotype groups. Each SNP was selected one at a time to distinguish the haplotype groups. Upon each SNP selection, the haplotype groups were further distinguished, and the subsequent SNP selection was based on the SNPs capacity to distinguish any haplotype groups that were yet to be differentiated by the minimum SNP set. A set of 8 SNPs (designated SNP2 – SNP9) was identified that could successfully distinguish all of the haplotypes, with the exception of Hap 7 and Hap 9, rare aus haplotypes which could not be distinguished. We next sought to identify a single SNP that would be diagnostic of Haplotype 6 (Hap 6), the haplotype carried by majority of lines known to be fragrant. None of the SNPs within the BADH2 gene met this criterion, so the 10-kb sequence on either side of the BADH2 gene were examined to identify a SNP (designated SNP 1) that would uniquely distinguish Hap 6.To identify a minimal subset of SNPs that could unambiguously differentiate the BADH2 haplotype groups (described above). An additional KASP assay was designed to characterize the previously reported functional 8-bp deletion in the BADH2 gene  with the primer sequences:The trait marker, SNP1, was additionally designed into an assay by 3CRbio  with a flame thermionic detector (FTD) following a modified method of the protocol described by Goufo et al., A subset of 39 varieties was selected from the U.S. germplasm panel and phenotyped for fragrance. These varieties represented all six of the haplotype classes observed in the U.S. germplasm, and 27 fragrant rice varieties that represented all known pedigree sources of aroma in U.S. germplasm. Seeds were obtained from panicle rows grown at the HRCRRS in 2017. Five grams of milled rice of each line were prepared by grinding them into a powder of less than 2.5 mm in diameter with a Cyclone Sample Mill (UDY Corporation). The 2AP concentration was determined using gas chromatographic separation on a Shimadzu GC-2010 BADH2 gene, we examined the SNP-Seek database containing re-sequencing information for a collection of 2932 (hereafter referred to as 3 K) diverse varieties of O. sativa (http://snp-seek.irri.org). A total of 297 SNPs was identified in the BADH2 (LOC_Os08g32870) gene. Filtering for heterozygous calls and missing data eliminated 42 SNPs, leaving 255 SNPs that were utilized for construction of BADH2 gene haplotypes. The number of SNPs observed at the BADH2 gene in the SNP-Seek database was similar to previously reported genes , and the second most frequent haplotype in the tropical japonica subpopulation  , present in 96% of temperate japonica and in 60% of tropical japonica accessions (Table Haplotype 1 (Hap 1) was the most common haplotype in the dataset, present in 892 (30%) accessions. It was the most frequent haplotype in indica or aus accessions, as summarized in Table indica and aus than in the tropical japonica and temperate japonica subpopulations of O. sativa  overall, but it was the most common haplotype within accessions belonging to the aromatic subpopulation, present in 51% (n = 38) of accessions in the subpopulation. Hap 6 was absent in aus and found at relatively low frequencies in the other subpopulations . The aromatic subpopulation only accounts for 2.6% (75 accessions) in the 3 K genomes dataset. Due to the relatively lower representation, there is a bias in the number of Hap 6 containing accessions across subpopulations in this study. For example, of the 171 accessions containing Hap 6, 22% were from the aromatic subpopulation, 38% were from the indica subpopulation, and 31% were from the tropical japonica subpopulation  was rare in the 3 K genomes , selected from the 255 SNPs segregating in the BADH2 gene, was selected that could unequivocally identify all major haplotypes, with the exception of Hap 9 which could not be distinguished from Hap 7  and Hap 2  cumulatively accounting for 85% of the panel  strongly associated with the es Table . Hap 3 wes Table   located at 20,374,951 MB is capable of differentiating the aromatic Hap 6 from all other non-aromatic haplotypes and hypothesized to be in linkage disequilibrium (LD) with the functional 8 bp deletion. The U.S. rice germplasm panel was screened with the previously reported gel-based marker that directly assays the 8 bp deletion in order to confirm the SNP is in LD with the indel . It is reasonable to assume that haplotype alleles present in a specific rice breeding program are represented within the 3 K genomes, even if the specific accessions are not. In this study, the 3 K genotype data was utilized to characterize, identify, and validate that the BADH2 allele that confers fragrance in U.S. rice germplasm, without any fragrant U.S. varieties being present in the 3 K genomes dataset. By characterizing the haplotype diversity found in the 3 K genomes using 255 genic SNPs, a subset of nine SNPs was identified that together could distinguish all 11 haplotypes, effectively obtaining the same resolution as with the full 255-SNP set. Hap 6 was determined to be the aromatic haplotype due to 128 lines within the haplotype containing the 8-bp deletion.The objective of this research was to leverage publicly available, high density, SNP data from BADH2 gene diversity in this panel. This set of nine SNPs could be applied in a similar fashion to other breeding programs’ target germplasm, assuming the haplotypes present in the target germplasm were represented in the 3 K genomes dataset. The panel of U.S. germplasm included all modern U.S. varieties and elite breeding lines, thereby representing the pool of potential parents of new breeding crosses. In addition, the panel included fragrant lines whose pedigrees traced back to all original sources of fragrance used in U.S. rice breeding. Thus, the aroma trait SNP (SNP1) validated across this panel can be used with confidence in any breeding crosses that are derived from materials in the panel.Then a panel of U.S. germplasm was genotyped using the nine SNPs to characterize indica germplasm or a different assay design could provide better results.Although this SNP has been validated across the target U.S. breeding germplasm, it should be noted that the nature of LD based markers is germplasm dependent. The aroma SNP (SNP1) identified in this work was in perfect LD across the target germplasm; however, it is very possible that this SNP will not be in LD with the 8 bp deletion across all germplasm globally. Thus, as with any LD based marker, validation in the target breeding germplasm is necessary prior to deployment for breeding applications. Similarly, attempts in this work to develop a KASP assay on the 8 bp deletion were not successful when test on U.S. japonica rice, thus it is possible that the KASP assay could be successfully utilized on The ultimate objective of this research was to identify a single SNP that could be used for selection of fragrant varieties within U.S. breeding programs. For deployment in an applied breeding program, a DNA marker must be accurate across the target germplasm, amenable to high-throughput genotyping, and effective on low cost genotyping platforms/chemistries .


Staphylococcus aureus (MRSA) isolates, in which we sought to define key quality metrics to support MRSA sequencing in clinical practice. We evaluated the accuracy of mapping to a generic reference versus clonal complex (CC)-specific mapping, which is more computationally challenging. Focusing on isolates that were genetically related (<50 single nucleotide polymorphisms (SNPs)) and belonged to prevalent sequence types, concordance between these methods was 99.5 %. We use MRSA MPROS0386 to control for base calling accuracy by the sequencer, and used multiple repeat sequences of the control to define a permitted range of SNPs different to the mapping reference for this control (equating to 3 standard deviations from the mean). Repeat sequences of the control were also used to demonstrate that SNP calling was most accurate across differing coverage depths  when the depth required to call a SNP as present was at least 4−8×. Using 786 MRSA sequences, we defined a robust measure for mec gene detection to reduce false-positives arising from contamination, which was no greater than 2 standard deviations below the average depth of coverage across the genome. Sequencing from bacteria harvested from clinical plates runs an increased risk of contamination with the same or different species, and we defined a cut-off of 30 heterozygous sites >50 bp apart to identify same-species contamination for MRSA. These metrics were combined into a quality-control (QC) flowchart to determine whether sequence runs and individual clinical isolates passed QC, which could be adapted by future automated analysis systems to enable rapid hands-off sequence analysis by clinical laboratories.Bacterial sequencing will become increasingly adopted in routine microbiology laboratories. Here, we report the findings of a technical evaluation of almost 800 clinical methicillin-resistant  Whole-genome sequencing of MRSA can now be performed within 24 h, and tools are under development to rapidly analyse the resulting data without the need for expert input, but quality-control (QC) metrics are required for this tool to be used in clinical practice. In this study we defined a number of QC metrics to support MRSA sequencing in clinical practice, culminating in a QC flowchart that could be adopted or adapted by future users.Bacterial whole-genome sequencing is a highly discriminatory technique that could be a powerful adjunct for infection control teams. This allows isolates (patients) to be identified as being part of an outbreak, enabling early action to prevent further dissemination. As importantly, isolates (patients) can be shown not to be linked with a suspected outbreak, preventing unnecessary infection control actions. Methicillin-resistant Research evidence for the practical utility of MRSA sequencing as an adjunct to infection control practice supports its introduction into routine use. This includes the demonstration that sequencing is superior to other typing methods in discriminating between isolates of the same lineage , and thaThere has been considerable debate about whether pathogen sequencing should be performed in a centralized facility or distributed throughout the network of hospital-based diagnostic laboratories. However, the pace at which sequencing instruments are being developed for clinical use means that sequencing will become increasingly feasible in any setting provided that robust methods are available to support high-quality real-time sequencing, combined with the availability of fully automated tools that undertake relatedness comparisons and provide interpretation guidelines for use of this information to healthcare workers without informatics expertise. This would support the rapid use of data to direct and optimize MRSA outbreak investigations. Progress has been made to describe the sequencing of MRSA within a 24 h time window (from the start of DNA extraction to sequence data generation) , which imec genes and same-species contamination. These culminated in a quality-control (QC) metric flowchart to guide future adopters.Here, we report the findings of a technical evaluation of almost 800 MRSA isolates that set out to define several key metrics that could support the analysis of MRSA sequence data in clinical practice. This includes the selection of mapping reference for relatedness determination using core-genome SNPs, the sequencing depth required to call a SNP, and defining cut-offs for the detection of The study was conducted at the Clinical Microbiology and Public Health Laboratory at the Cambridge University Hospitals NHS Foundation Trust (CUH), UK, under ethical approval from the National Research Ethics Service (ref: 11/EE/0499) and the Cambridge University Hospitals NHS Foundation Trust Research and Development Department (ref: A092428). Consecutive patients with samples that were MRSA positive were identified between 24 January and 1 November 2018 using the hospital IT system [EPIC EMR ]. One MRSA isolate from each patient  was sought for sequencing, together with all isolates cultured from blood cultures. In the first 3 months of the study (24 January to 3 April), this was achieved by isolate retrieval from the frozen archive to provide look-back data for the subsequent 6-month prospective collection. Of the 147 cases identified with MRSA-positive samples in the first 3 months, 124 (84 %) patients had a stored MRSA isolate. Thereafter through to the end of the study, MRSA-positive cultures were retrieved in real-time from the clinical laboratory by the research team. Samples were flagged by laboratory staff and the culture plate retrieved on the same day, where possible. A total of 772 patients were identified as having MRSA-positive samples during this second period, of which 673 patients (87 %) had a clinical culture retrieved from which MRSA was obtained and sequenced. After de-duplication of cases presenting in the first and second periods, a total of 789 isolates from 784 patients were processed for sequencing. Samples were renumbered with an anonymous study code upon entry into the study.S. aureus was confirmed using the Staph Latex kit (Pro-lab Diagnostics). A single 2–3 mm colony was picked from clinical culture plates using a 1 µl loop and inoculated onto CBA and incubated overnight at 37 °C in air. Where colonies were smaller than 2 mm, several colonies were picked. Where bacterial growth was confluent , a 1 µl loopful was taken. If there were several positive plates for one clinical sample, the plate with the least visible background contamination was selected. As with the freezer archive, a single colony was selected for sequencing, and a 10 µl loopful was stored at −80 °C in Microbank vials.Freezer archive samples were plated onto Columbia Blood Agar (CBA) using a 1 µl loop and incubated overnight at 37 °C in air. A single colony was selected for sequencing, and a 10 µl loopful was stored at −80 °C in Microbank vials (Pro-lab Diagnostics). Plates that appeared contaminated were re-subbed onto CBA and incubated at 37 °C overnight prior to sequencing and re-storage. For prospectively obtained clinical samples, putative https://ccb.jhu.edu/software/kraken/) with the miniKraken database (https://ccb.jhu.edu/software/kraken/dl/minikraken_20171019_8GB.tgz). Multilocus sequence type (ST) was identified using Ariba . Clonal complexes were determined based on assignments reported previously  with the aim of producing 50× of data for each isolate ; contamination with a different species ; no mec gene (n=4);<20× depth (n=10); or <80 % of the reference covered at 20× depth (n=5). After re-purification of the original culture to re-isolate MRSA, 22 of 25 isolates were successfully re-sequenced, leaving a total of 786 isolates that were included in the analysis (Table S2). Of the 25 isolates failing the initial QC metrics, 24 were from the prospective collection and one was from the retrospective archives. Of the five isolates failing QC due to heterozygous sites, all five were from prospective collection.All clinical isolates had to pass the following QC metrics: have their best match to  species , and havmec gene detection) and were excluded, leaving 423 isolates for further analysis. The second collection consisted of 742 clinical isolates from two NHS hospital groups and a general district hospital in South-East London between 2011 and 2012. A total of 152 isolates failed our QC metrics for the following reasons: not being best matched to S. aureus in Kraken (n=8), >4 % of reads in Kraken matching to another species (n=80), no mec gene detected (n=54), or <20× depth of coverage (n=10) leaving a total of 590 isolates for further analysis. These were mapped to the CC22 reference and CC-specific mapping references as described above, with AUS0325 (accession number LT615218) used as the mapping reference for CC88.To determine the applicability of CC22 mapping to alternative clinical settings, raw fastq files were downloaded from the European Nucleotide Archive that were submitted by two studies that have been described previously . The firn=485), the remainder being from patients at two other hospitals in the locality   and GP surgeries (n=141). The majority of isolates were from multisite screens , with the remainder from clinical samples including seven isolates (1 %) grown from blood cultures taken from six patients. In silico MLST from the sequence data demonstrated that the most common STs were ST22 , followed by ST45  and ST59  with a total of 66 STs including 15 novel STs identified (Table S2). A total of 781 isolates were mecA (99 %) with the remaining five isolates containing mecC.We sequenced 786 MRSA isolates from samples submitted to the clinical laboratory from 782 patients. The majority of patients had isolates submitted from Cambridge University Hospitals . The single CC22 reference used represented the dominant clonal complex in the collection and was ~46 000 and 53 000 SNPs from the next most common STs in the collection (ST59 and ST45). n=399 isolates). There was a positive association between CC-specific versus CC22 mapping, with a median of 0 SNPs different (range 0–19) between the two approaches across all 399 isolates (9597 pairs), and a median of three SNPs different (range 0–19) for the 602 non-CC22 pairs . Based on binary categories of related (0–25 SNPs) or unrelated (>26 SNPs) for CC22 mapping, there were 44 pairwise discords . A single ST59 pair was 25 SNPs apart based on CC22 mapping but 27 SNPs apart based on CC-specific mapping, and 43 isolate pairs were >25 SNPs (26–38 SNPs) apart based on CC22 mapping but <25 SNPs (14–24 SNPs) apart based on CC-specific mapping . This provides a concordance of 93.6 % for non-CC22 isolate pairs and 99.5 % for all isolate pairs.We evaluated whether a single reference could be used for mapping, rather than the common practice of using CC-specific references . We base22 pairs . We haveWe repeated the analysis based on CC22 and CC-specific mapping in which we compared only a single randomly chosen genome that was <50 SNPs different for each isolate. This resulted in a median of 0 SNPs different (range 0–19 SNPs) with 65 % of the comparisons  comprising CC22 isolates. There were 9 discrepancies (2 %), with higher numbers of SNPs called using the CC22 mapping method. This provides a concordance of 93.6 % for non-CC22 isolate pairs and 98 % for all isolate pairs.To determine whether the results were comparable with a different mapping reference we repeated the analysis using a generic CC30 reference. A total of 17 isolates (2 %) in the collection belonged to ST30. This demonstrated a median of 3 SNPs (range 0–24 SNPs) different between CC30 and CC-specific mapping across the isolate pairs . Based on a 25 SNP cut-off, 1046 isolate pairs (10.8 %) were discrepant, providing an overall concordance of 89.2 %.n=362, 86 %), and >10 isolates for the London collection , and focused on isolate genomes that were <50 SNPs different from another genome. When comparing CC22 and CC-specific mapping, there was a median of 0 SNPs (range 0–9 SNPs) and 0 SNPs (range 0–25 SNPs) different for all isolates and a median of 2 SNPs (range 0–9 SNPs) and 4 SNPs (range 0–25 SNPs) different for non-CC22 isolates, based on the UK and London collections, respectively. Based on binary categories of related (0–25 SNPs) or unrelated (>26 SNPs), there were 37 pairs of related isolates in the national collection (of which 27 were ST22 pairs), with 100 % concordance between CC22 and CC-specific mapping, and 1196 pairs of related isolates in the regional collection (of which 1118 were ST22), with 19 discrepancies between CC22 and CC-specific mapping. All 19 discrepancies in the regional collection belonged to ST8 (n=3) or ST36 (n=16), accounting for 21 and 36 % of related pairs and 0.6 and 1.1 % of all pairs in these STs, respectively. For the national and regional collections, respectively, this provided an overall concordance rate of 100 and 75.6 % for non-CC22 isolate pairs and 100 and 98.4 % for all isolate pairs.To investigate whether these findings are applicable to other clinical settings, we repeated the analysis using two other MRSA collections that have been described previously , drawn fWe explored the extent to which the number of called SNPs varied at different thresholds for the minimum depth of coverage required to call a base as present . This was evaluated over incremental 2× steps between 4× (which has been used previously ) and 20×The number of SNPs identified in the sequences with the highest mean depth of coverage across the genome would be predicted to be the true number of SNPs, on the basis that these have the most data to support base-calling. This is supported by findings that the SNP range was smallest at the highest mean depth of coverage across the different base depth thresholds evaluated . Based on this, thresholds of 4–8× depth at a base provided SNP calls across the different genome coverage depths that were closest to the predicted true number of SNPs . At threTo determine how SNP calling varied depending on the mapping reference used, we repeated the mapping of the 43 positive MRSA control runs, together with three study isolates that were ~100 SNPs from the MRSA control, to a CC30 reference . These three study isolates were used to assess a similar SNP difference to the CC22 reference method (~100 SNPs), since the MRSA control is too distant (thousands of SNPs) from the CC30 reference to perform an accurate comparison of the methods. A similar pattern was observed, with the number of SNPs decreasing as the depth required to call a SNP increased . As with the CC22 reference, the lowest range of SNPs called occurred between 4–8× .Our methodology for sequencing does not include PhiX , and insmec genes (mecA and mecC) in the 786 isolates was 53× (range 8×–126×). Comparison between the depth of coverage across the entire mapping reference and the depth of coverage of the mec genes revealed a positive association, with one clear outlier . In addition, the gene was split into two sections based on Ariba, one of 1192 bp and one of 849 bp. The additional 20 isolates failing QC at 1 standard deviation had mec gene depths 70–76 % of the depth of the mapping reference depth . Based on these data we suggest that mec genes with a depth that is at most 2 standard deviations below the mean depth across the mapping reference should be called as present, and those with a greater deviation should be failed and investigated further in the laboratory. This requires calculating the depth of coverage across the mapping reference, the standard deviation of depth, and the depth of coverage across the mec gene for each new isolate and performing a simple calculation.The mean depth of coverage across the  outlier . Low depe genome . Based oe genome . Based o0× depth , this opmecA gene depth, described above, further supporting that this sample was contaminated. Based on these findings, we propose that all isolate genomes should contain less than 30 hetSNPs each of which are >50 bp apart (We determined a cut-off that could be used to identify same-species contamination based on the number of heterozygous sites (hetSNPs) detected when mapped to the CC22 reference genome. The number of heterozygous sites identified in the 786 clinical MRSA isolates ranged from 0 to 6046  the number of SNPs called fell. Our findings that the same number of SNPs were identified at the highest depth of coverage across the genome (92×) and at ~40× depth, for both the 4× and 6× thresholds, supports targeting 50× coverage across the genome and suggests that the requirement for at least 60× reported previously [In our current pipeline, we aim for a median depth greater than 50× based on 21 isolates and three controls , which weviously  may be tOur clinical MRSA sequencing methodology includes a positive MRSA control to control for the accuracy of base calling by the sequencer rather than PhiX, which led us to define the limit of variation in SNPs allowed for the positive control. We allowed 3 standard deviations from the mean, which resulted in a SNP range of 111–120 SNPs when using the 4× cut-off for defining a SNP, up to 107–121 SNPs when using the 8× cut-off. The exact range of SNPs may vary if a different mapping or SNP calling methodology is utilized by future automated analysis pipelines, but the data provided here could be utilized to reproduce the expected SNPs for these.mec genes based on depth of coverage. Low coverage of the mec gene in comparison to the remainder of the genome could indicate low level contamination of an MSSA with MRSA, thereby calling a false-positive. We demonstrated that a cut-off of 2 standard deviations below the average coverage depth across the mapping reference could be used to define mec genes as either present or potentially absent. Our findings indicate that 1 standard deviation may be too stringent, whilst 3 standard deviations could allow false-positives. We suggest that those with mec genes at coverages lower than the cut-off are tested further in the laboratory to confirm absence of mecA and exclude a mixed population.Looking ahead to future practice when sequencing could replace phenotypic antimicrobial susceptibility testing, we aimed to define a robust measure for the detection of S. aureus strain is inadvertently sequenced, although the likelihood of mixed carriage is low [S. aureus strain, low-level contamination and contamination with a genetically similar isolate may be missed. Parameters for detection of contamination with a different species have been described by us [S. aureus for clinical use has not been reported.Compared with sequencing performed for research purposes using highly purified bacterial stock, sequencing from clinical plates runs an increased risk of contamination in which more than one e is low . In addied by us  and otheed by us , but to The final metrics required for the controls and clinical isolates to pass QC were combined into a flowchart for ease of use. This study was performed by manually inputting command lines to run each separate bioinformatic tool, and the next step in implementing real-time whole-genome sequencing into clinical practice will be the generation of a fully automated analysis-pipeline. Work on this is currently underway, and we have recently performed a pilot study into an automated pipeline developed by Next Gen Diagnostics . The parEuropean Nucleotide Archive, ERP005128 (2019).1. Toleman, M.S., Reuter, S., Jamrozy, D., Wilson, H.J., Blane, B., Harrison, E.M., Coll, F., Hope, R.J., Kearns, A., Parkhill, J., Peacock, S.J., Torok, M.E. European Nucleotide Archive, PRJEB11177 (2016).2. Tosas Auguet, O., Stabler, R.A., Betley, J., Preston, M.D., Dhaliwal, M., Gaunt, M., Ioannou, A., Desai, N., Karadag, T., Batra, R., Otter, J.A., Marbach, H., Clark, T.G., Edgeworth, J.D. Click here for additional data file.Click here for additional data file.


Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection.Wheat . No high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome was found to have the highest LD compared with the two other genomes A and B. The ratio between the number of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D.To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes contained three different subpopulations based on population structure, principle coordinate, and kinship analyses. A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1 was found to be the more diverse subpopulation based on the different allelic patterns (The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations. Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful information on the genomic regions that includes interesting genes. Triticum aestivum L.) is one of the most important cereal crops globally. It feeds more than a third of the human population around the world. The genome of bread wheat is an allohexaploid which contains three different genomes A, B, and D [Arabidopsis thaliana (~ 120 times), and Oryza sativa L. (~ 40 times) [Wheat  . To wellPstI/MspI) to reduce the complexity of large genomes such as wheat [Generally, there are many types of molecular markers which could be used in various genetic analysis such as genetic diversity, genome-wide association studies, fingerprinting, evolutionary origin, and breeding applications. The most common type of markers is single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) . Howeveras wheat , 14. Usias wheat .Understanding the linkage disequilibrium (LD) between marker pairs is very important in association mapping studies as it determines the resolution of the association . For exaAs wheat is one of the most important crops globally, it is very important to study the global genetic variation. This requires the collection of cultivars from different countries. The USDA-ARS national plant germplasm system is a good resource for plant breeders worldwide as it contains a large number of accessions of wheat  which have been collected starting from 1897. In 1995, the number of NSGC core accessions has been reduced to only 10% of the total number of the collected accessions following Brown 1989  outline The objectives from this study were to (1) understand the genetic diversity and population structure in spring wheat using 103-accessions representing different countries worldwide, (2) compare the genetic properties among subpopulations, and (3) determine the patterns of linkage disequilibrium (LD).The total number of GBS derived SNPs from the tested genotypes was 287,798 SNPs. After quality filtering, the total number of high-quality SNPs was 36,720 which were well distributed across the genome Fig. . The higThe PIC value across chromosomes ranged from 0.1 (1598 SNPs) to 0.4 (6836 SNPs) with an average of 0.24  was plotted against ΔK to identify the suitable number of subpopulations. The largest ΔK value was observed at K = 3 suggesting the presence of three subpopulations in the tested genotypes  among genotypes in each subpopulation Table . SubpopuThe three subpopulations identified based on STRUCTURE analysis were used to calculate the AMOVA and genetic diversity indices in GenAlex 6.41 software. A significant variation within and among the subpopulations was found based on the AMOVA results. The total variation between the tested genotypes could be classified into two parts; variation among subpopulations with a percentage of 15%, and variation within subpopulations with a percentage of 85% Table . The hapI), the diversity index (h), and the unbiased diversity index (uh) had average values of 0.636, 0.384, and 0.403 based on the average of the three subpopulations  and effective alleles (Ne) were 2.528 and 1.781, respectively Table . The ShaR2 revealed that there is no high LD among the 36,720 SNP pairs in the tested genotypes with an average value of 0.138 , while the LD average of non-significant markers was approximately the same in genome A and B with an average of ~ 0.084.The analysis of linkage disequilibrium showed that the LD decayed with the genetic distance . The LD decay in each genome is illustrated in Fig. The ratio between the number of significant LD and the number of nonsignificant LD could be arranged from higher to lower as follows; 0.06, 0.05, and 0.04 for genome D, genome A, and genome B respectively. At the chromosome level, chromosomes 2D, 5A, and 7B had the highest ratios between the number of significant and non-significant LD with values of 0.08, 0.07, and 0.05, respectively. The RTriticum aestivum L. resulted in the polyploid nature of its tetraploid ancestor with AABB. This conclusion could be a good explanation of the high level of diversity among hexaploid wheat genotypes and the high number of SNPs in the A and B genomes.The studied wheat genotypes were collected from different countries representing five of the world continents  which enable us to estimate wheat genetic diversity in the studied countries. The study was conducted using 36,720 SNPs which were well distributed across the three hexaploid wheat genomes . The highest number of SNPs were found on genome B 41%), while the lowest number of SNPs were found on genome D (19%) indicating that genome D is the least diverse wheat genome  as it could result in a superior association between the studied trait and the GBS-derived SNPs .The result of AMOVA indicated the presence of highly significant genetic diversity among the three subpopulations Table . The higNa, Ne, I, h, uh, and PPL) among the three subpopulations, subpopulation 1 is the most diverse subpopulation as it shows the highest values of all the indices. This result is expected as this subpopulation contains genotypes from 11 different countries compared with the other two subpopulations which contain genotypes from ten and two different countries, respectively  and A 5%) than in genome B (4%) which means that genome B had the highest number of markers in non-significant LD . The extracted DNA was digested for GBS purpose using two different restriction enzymes, ify SNPs . Chineseify SNPs . GeneratGenetic diversity statistics of all the 36,720 SNP markers such as polymorphic information content (PIC), gene diversity, percentage of heterozygosity, and minor allele frequency (MAF) were calculated using PowerMarker software V 3.25 . The folij and Pik are the frequencies of jth and kth alleles for marker i, respectively.Where PTo estimate the number of subpopulations in the current tested genotypes, a model-based (Bayesian) method with the filtered SNPs  was used. STRUCTURE 3.4.0 software was used to analyze population structure . Burn-inst), different alleles (Na), number of effective alleles (Ne), Shannon’s index (I), the diversity index (h), the unbiased diversity index (uh), and percentage of polymorphic loci (PPL) were calculated. The AMOVA and estimation of genetic indices were performed using GenAlex 6.41 [For this analysis, 14,400 SNPs based on the highest PIC values (from 0.282 to 0.375) were used. The number of subpopulations based on the STRUCTURE analysis was considered in the AMOVA. The genetic indices such as fixation index (Flex 6.41 .R2) using TASSEL v.5.2.5 software [The LD between each pair of the 36,720 SNPs was calculated as the squared allele frequency correlation coefficient  decay of the 103-spring wheat based on the 36,720 SNP markers.Additional file 3: Figure S2. Haplotype block analysis represents the number of haplotype blocks on: (a) chromosome 2D, (b) chromosome 5A, and chromosome 7B.


Illumina Goat 50K single-nucleotide polymorphisms (SNP) BeadChip. The analysis used a polygenic model where the dropping criterion was a call rate ≥ 0.95. The initial dataset was composed of ~60,000 rows of SNPs and 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50K BeadChip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield , test day protein yield , test day fat yield , percentage protein , percentage fat , and percentage lactose content . The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provides precision breeding methods will thereby increase the breeding value.This paper reports an exploratory study based on quantitative genomic analysis in dairy traits of American Alpine goats. The dairy traits are quality-determining components in goat milk, cheese, ice cream, etc. Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the  The use of goats to convert poorly digestible fiber into high-quality meat and milk has been a mainstay since 9,000 YBP . Goats pcaprines [Global demand for milk consumption per capita is increasing due to both population growth and consumer preferences . Genome-caprines .The secretory mechanism of lactation , 9 is weGoat 50K SNP BeadChip [PLINK [Qlick [SNP & Variation Suite v8.8.1 [GWAS have benefitted from genotyping technologies like that of the high-density BeadChip . HoweverBeadChip . Whole gBeadChip . To imprp [PLINK  in combip [PLINK , Qlick [K [Qlick , and anae v8.8.1 .Current genotyping arrays can capture small fractions of low frequency variants for individual goats. New or custom genotyping array developments provide avenues to utilize high priority variants with GWAS . Custom GoatSNP50 BeadChip was used to determine genetic diversity of Boer, Cashmere, and Rangeland goats [The nd goats . The sucnd goats .High-density SNP data has effectively identified regions of the genome that are important predictors of production traits . TheSNP BeadChip for use in research and animal management that would not be cost prohibitive for researchers and smaller scale producers.This study explored the use of genome-wide associations to identify heritable milking traits in the American Alpine goat. We hypothesize that associations would exist and could be elucidated for future development of a low-panel improve contributions to the genotype-phenotype repository of the Council on Dairy Cattle Breeding (CDCB) for milk quality traits that are economically important for goat productionGoat50KSNP BeadChipdevelop genomic prediction and provide data for better tools, for predicting phenotypic traits, and for the selection of specific SNPs associated with select signatures (genes) that determine specific phenotypic traits in American Alpine goats by the initial use of the high-density p < 0.05) predictive capabilities for desired breeding traits in American Alpine dairy goatsestablish whether a low number of goat subjects (<300 goats) will provide statistically significant . Milk samples were collected from local, state, and national farms where goat milk samples were obtained. Sire ID, breed, somatic cell count and milk weight by test days, lactations, history of milk yield, protein percentage, and fat percentage are located in the CDCB repository.Capra hircus) were obtained from national breeding programs across the United States, including those at the Langston University-American Institute for Goat Research (LU-AIGR). Breeding values for fat, protein, lactose, solids, solids-not-fats (SNF), urea, freezing-point-depression (FPD), somatic cell count (SCC), test day milk yield (TDMilk), test day fat (TDFat), test day protein (TDProtein), test day somatic cell counts (TDSCCS), and Call_Rate were recorded  for a particular lactation Li of a doe of birthing age Ak at parity Pl within Ak, during postpartum week Wj, and with m number goats weaned (Nm) [Does from throughout the United States were utilized in this study with varying phenotypes and environmental stimuli. Neither handling and feeding conditions throughout the experiment nor birth month nor season factors were included in the statistical analysis. The statistical study was carried out following two generally applied mathematical models, a mixed model and a fixed model. The mixed model is described inned (Nm) . Lactatined (Nm)  for estibronopol preservation tablets that preserved DNA in milk at ambient temperature [μl) and the quality (A260/A280) of DNA samples were assessed by spectrophotometric techniques on a 96-well automated spectrophotometer . Genotyping assays utilizing the GoatSNP50 BeadChip were completed at 30x depth through the Core Facility at the Oklahoma Medical Research Foundation (OMRF). High-quality genotype assays were obtained following QC/QA determinations from 276 DNA samples derived from somatic cells contained in goat milk for use in this study. The final number of samples was reduced for SNP analyses to 72 different goats.Milk producers delivered milk samples in 50 ml plastic vials containing perature . Milk saGoatSNP50 BeadChip was used to identify the strongest associations of SNPs to phenotypic data. The platform used is GPL28152 Illumina Goat IGGC_conf_60K Genotyping BeadChip (GoatIGGC_conf_60K). Isolated DNA samples were hybridized on individual high-density GoatSNP50 BeadChips. The original corresponding goat milk samples with defined phenotypic traits were used with the reference GoatSNP50 BeadChip to conduct a GWAS using PLINK [SNP & Variation Suite v8.8.1 [The ng PLINK  to analyng PLINK  and withe v8.8.1 .p < 0.05. The identification of genomic coordinates on Manhattan plots  = 4.0 is shown in the figures. Association with milk yield (TDMilk), protein yield (TDProtein), fat yield (TDFat), protein, fat, and lactose composition was completed, respectively. Phenotypic/genotypic associations were observed for variance, and novel SNPs with strong association for particular traits were identified that segregated for inclusion on a low-density SNP Chip.The number of SNPs was identified after basic association tests correlating specific phenotypic traits of each goat. The traits were selected according to the strongest associations having  Figures –6 with gp < 0.05, where their negative logarithms would be the greatest. Analysis was conducted using SNP & Variation Suite v8.8.1 [p < 0.05.After these basic association tests correlating specific phenotypic traits, the number of SNPs for each goat was selected based on the strongest associations having e v8.8.1 . SpreadsThe quantitative values for phenotypic traits are depicted for each sample based on feature identification (FID) numbers in Figures 750KGoatSNP BeadChip in relation to the phenotypic traits in relation to their FID number (1–276) are shown as the numbers of SNPs .The distributions of SNPs and the yield values for phenotypic traits  with the  Figures –18 in ean = 13,469), TDProtein , TDFat , protein , fat , and lactose  in the different data sets are presented below.The milk phenotypes and their associated genes TDMilk (50KSNP BeadChip are shown (p ≤ 0.05) SNPs at a call rate ≥ 0.95 was selected for approximate numbers of genotype/phenotype association  . This GW10(p) = 4.0) was completed for test day milk, test day protein, test day fat, protein, fat, and lactose, respectively. Phenotypic/genotypic associations were identified for variance, and novel SNPs with strong association for particular traits were segregated for inclusion on a low-density SNP chip. Manhattan plots for these six analyses indicated several significant regions at the genome threshold indicating outliers and chromosome level, niche association with specific, phenotypic traits.The identification of genomic coordinates on Manhattan plots Figures -6 with gp values (∗p < 0.05), large estimated effect sizes (odds ratios), and previous reports of association. They also include biologically relevant pathways, plausible function of variations, or a combination of all these factors.Custom-designed genotyping tools that are smaller and are less costly than the original, high-density tools have similar predesigned amounts of computational power. The selection criteria include, but are not limited to, small This is an exploratory study and did not analyze high probability rare variants. Previous studies show that 460 to 4,600 individuals are needed to assume certainty for detections . Also, LThe goals of this exploratory study were to identify regions of the Alpine goat genomes that are important for predicting genetic merit in female dairy goats. This is useful to develop genomic prediction equations for the genetic evaluation of economically important milk production traits . This would further identify methods for high-yield and high-quality milk towards the production of goat milk, cheese, ice cream, and many other dairy goat products for the consumer through the producer. The selection of goat phenotypes could be predicted while it is young, before reaching the age of gestation, thereby facilitating appropriate use of resources and positively leading to selecting genetic change in goat herds.5K BeadChip) available for goat producers at a reduced cost, compared to the current cost of a 50K BeadChip. This would facilitate both genetic selection and integration of genomic technologies into production systems, contributing to precision agriculture.These approaches comprise the first step in identifying SNPs for future production of a low-density SNP panel , respectively. These results imply that the possibility of a marker-based selection of desired traits is possible.The amount of genotypic data in this study was relatively high, considering the low number of subjects. It enabled the discovery of multiple regions of the genome that were associated at chromosome-wide significance levels with desired traits from American Alpine dairy goats. A combined approximation of 30,594 SNPs was detected for six phenotypic traits at levels of significance (50K SNP BeadChip that is available to identify those goats that contain the genes for desirable traits examined here. The creation of a 5K or less SNP chip for the traits described in this study can provide similar information about each goat and would be a better way of precision breeding while preserving resources for better use in a goat-producing farm or facility. A larger scale study is cleared indicated to more conclusively identify DNA fragments for inclusion on a low-panel SNP chip. Finally, additional insight as to the relationship of phenotypic/genotypic effects on the variability in yields should be examined.Currently, there is a


Bemisia tabaci Gennadius, is a serious pest of many agricultural crops worldwide. Numerous studies have examined the genetic structure of whitefly populations separated by geographical barriers; however, very few have assessed the population structure of B. tabaci at a farmscape level. A farmscape in this study is defined as heterogenous habitat with crop and non-crop areas spanning approximately 8 square kilometers. To assess the roles of farmscapes as drivers of B. tabaci genetic variation, thirty-five populations of the sweetpotato whitefly were collected from crop and non-crop plant species from fifteen farmscapes. Using mitochondrial COI gene sequences (mtCOI) and six nuclear microsatellite markers, the genetic diversity and genetic differentiation among collected B. tabaci MEAM1 populations were examined. Haplotype analysis using mtCOI sequences revealed the presence of a single B. tabaci MEAM1 haplotype across farmscapes of Georgia. Results from microsatellite markers further showed no significant genetic structuring among populations that corresponded to plant species or farmscapes from which they were collected. Annual whitefly population explosions and subsequent dispersal might have facilitated the persistence of a single panmictic B. tabaci population over all sampled farmscapes in this region. Sweetpotato whitefly, Bemisia tabaci is a whitefly species complex comprising important phloem feeding insect pests and plant virus vectors of many agricultural crops. Middle East–Asia Minor 1 (MEAM1) and Mediterranean (MED) are the two most invasive members of the B. tabaci species complex worldwide. The diversity of agroecosystems invaded by B. tabaci could potentially influence their population structure, but this has not been assessed at a farmscape level. A farmscape in this study is defined as heterogenous habitat with crop and non-crop areas spanning ~8 square kilometers. In this study, mitochondrial COI gene (mtCOI) sequences and six microsatellite markers were used to examine the population structure of B. tabaci MEAM1 colonizing different plant species at a farmscape level in Georgia, United States. Thirty-five populations of adult whiteflies on row and vegetable crops and weeds across major agricultural regions of Georgia were collected from fifteen farmscapes. Based on morphological features and mtCOI sequences, five species/cryptic species of whiteflies  were found. Analysis of 102 mtCOI sequences revealed the presence of a single B. tabaci MEAM1 haplotype across farmscapes in Georgia. Population genetics analyses  of B. tabaci MEAM1 (microsatellite data) revealed only minimal genetic differences among collected populations within and among farmscapes. Overall, our results suggest that there is a high level of gene flow among B. tabaci MEAM1 populations among farmscapes in Georgia. Frequent whitefly population explosions driven by a single or a few major whitefly-suitable hosts planted on a wide spatial scale may be the key factor behind the persistence of a single panmictic population over Georgia’s farmscapes. These population structuring effects are useful for delineating the spatial scale at which whiteflies must be managed and predicting the speed at which alleles associated with insecticide resistance might spread.  Insect herbivores rely on living plants for food and habitat. Accordingly, host plants are among the most important ecological factors that drive genetic diversity within and among insect herbivore populations . Insect Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) is a serious pest of open-field crop production systems throughout the world. Nymphs and adults of B. tabaci are phloem feeders and are typically found on the abaxial leaf surfaces of their hosts [Cucurbita pepo L.) and irregular fruit ripening in tomato (Solanum lycopersicum L.) [B. tabaci transmits multiple plant-pathogenic viruses to important crops [Bemisia tabaci is a species complex that encompasses more than 40 cryptic species [B. tabaci worldwide [B. tabaci was first reported in the United States in the mid-1980s, and has since become the predominant cryptic species in the country [Citrullus lanatus L.), cantaloupe (Cucumis melo L.), tomato, snap bean (Phaseolus vulgaris L.), and other vegetable crops, while transmitting a wide range of plant viruses in the southwestern and southeastern United States [B. tabaci MED was first documented on poinsettia (Euphorbia pulcherrima Willd. ex Klotsch) in Arizona [B. tabaci MED has rapidly spread in the United states, but is restricted primarily to greenhouse-grown ornamentals [Sweetpotato whitefly, ir hosts ,9. Theiricum L.) ,11,12. Ant crops ,14,15. B species ,17,18. Morldwide ,20. MEAM country ,22,23. Id States . In 2004 Arizona . Since tamentals . B. tabaci at broad spatial scales [B. tabaci populations vary at a farmscape level, i.e., between spatially adjacent crop and non-crop habitats. Variability in host-plant resistance, cropping patterns, landscape composition and configuration, and insecticide application patterns all might alter B. tabaci genetic population structure [B. tabaci evolutionary patterns and phylogenetic relationships [Bemisia because of the similarities in the morphology of whitefly adults and pupae of several members within this species complex [B. tabaci at a broad scale [Numerous studies have examined the population genetics of l scales ,30,31,32tructure ,33,34,35ionships ,37,38,39 complex ,40. Howe complex . Many miad scale . B. tabaci in Georgia, we collected whiteflies from 35 populations, 14 plant species, and 15 farmscapes (defined here as an area ~8 square kilometers and at least 16 km apart). Whiteflies were then identified to the species level using a combination of morphology and mtCOI sequencing, and B. tabaci populations were grouped into MED and MEAM1 cryptic species [B. tabaci MEAM1 populations in order to determine whether B. tabaci MEAM1 populations exhibited genetic differentiation by (1) host plant and/or (2) farmscape. To assess the roles of host plants and farmscapes as drivers of the population structure of  species . We thenA total of 35 different populations of whiteflies were collected from 15 farmscapes located in 15 different counties of Georgia, USA . Here, “Total DNA was extracted from individual whiteflies using InstaGene Matrix containing six percent Chelex resin . Individual whiteflies were homogenized in 1 mL of autoclaved distilled water in a 1.5 mL microcentrifuge tube and centrifuged for 1 min at 12,000 rpm. The supernatants were discarded and 50 μL of InstaGene matrix was added to the pellet. Microcentrifuge tubes were then incubated at 56 °C for 20 min and vortexed for 10 s. The tubes were again incubated for 8 min at 100 °C, vortexed for 10 s, and centrifuged at 12,000 rpm for 3 min. Extracted DNA was stored at −20 °C until used. ® Green Master Mix  in an Eppendorf Mastercycler® pro thermocycler . The 50 μL PCR mixture contained 25 μL of Master Mix, 0.5μM of forward and reverse primers, 20 ng DNA, and nuclease-free water. PCR conditions were 5 min of initial denaturation followed by five cycles of 40 s at 94 °C, 40 s at 45 °C, and 60 s at 72 °C; and then 35 cycles of 40 s at 94 °C, 40 s at 51 °C, and 60 s at 72 °C, and a final extension period of 72 °C for 10 min [Collected whitefly populations were placed in individual 9 cm Petri dishes, and under a dissecting microscope at 20× magnification, individuals were grouped into species using a whitefly identification guide . Speciesr 10 min . SuccessB. tabaci cryptic species. Therefore, B. tabaci individuals (three per population: 35 × 3 = 105) were further identified to the cryptic species level by amplifying and sequencing 867 bp of the 3′end of mtCOI gene using the primers and conditions described by Mugerwa et al. 2018 [® pro thermocycler with an initial denaturation at 94 °C for 5 min followed by 40 cycles of 40 s at 94 °C, 40 s at 52 °C, and 60 s at 72 °C, and a final extension period of 72 °C for 10 min. PCR products were purified and sequenced as described above. Bemisia tabaci cryptic species determinations were based on direct sequence comparisons using the web based NCBI BLAST sequence comparison application. Whitefly species delimitation was based on 3.50% partial mtCOI gene sequence divergence [Bemisia tabaci individuals were designated as MEAM1 or MED based on ≥96.50% mtCOI sequence similarity with the MEAM1 (GenBank accession number KR559508) and MED (GenBank accession numbers MH205753) mtCOI reference sequences.The mtCOI region amplified by primer pairs LCO1490 and HCO2198 was not effective in differentiating B. tabaci (102 MEAM1 and three MED) sequences obtained through 2195Bt and C012/Bt-sh2 primers . Sequences were aligned using MUSCLE in MEGA X [B. tabaci MEAM1 and B. tabaci MED haplotypes was constructed using PopART software [Haplotype analysis was carried out using 105 n MEGA X , and thesoftware . B. tabaci were genotyped at six loci using the following primers: BEM6, BEM11, BEM15, BEM23, BEM25, and BEM31 [B. tabaci [https://doi:10.5061/dryad.xgxd254f7).For each population, twelve females of nd BEM31 . PCR amp. tabaci . All genISF), and fixation index (STF), using the ‘poppr’ package in R version 3.6.0 [STF using GenAlEx6.5 [Genetic diversity across loci was estimated with several descriptive statistics: number of alleles, evenness of allele frequency, observed heterozygosity, expected heterozygosity, inbreeding coefficient  according to three models: infinite alleles model (IAM), two-phase model (TPM), and stepwise mutation model (SMM) . The probability of a bottleneck was estimated using the Wilcoxon sign-rank test [Mutation-drift equilibrium among populations was tested using BOTTLENECK v.1.2.02 . The proank test . Signatuank test . STF, analysis of molecular variance (AMOVA), genetic isolation by distance (Mantel test), STRUCTURE, and principal components analysis (PCA) were used to characterize the population structure and genetic differentiation among collected B. tabaci MEAM1 populations. First, pairwise STF was calculated using 10,000 bootstrap pseudoreplicates over loci, while accounting for null alleles, using the ‘poppr’ R package. Significance of pairwise STF values was estimated with the ‘hierfstat’ R package with 999 permutations [STF < 0.05 were taken as evidence of low differentiation among the populations, while values of STF > 0.15 were taken as evidence of high genetic differentiation [randtest function in the ‘ade4’ R package [STF/(1 − STF)) and pairwise geographic distance (Ln km) between all pairs of populations was analyzed by Mantel test (9999 permutations) using GenAlEx6.5 [B. tabaci MEAM1 populations collected over the farmscape was estimated using log-likelihood values of each K and ΔK in STRUCTURE HARVESTER [dudi.pca function and the first three principal components (PC) encompassing the majority of genetic variability among host plants  and farmscapes  were retained. Pairwise utations . Values ntiation . Second,ntiation , and sig package . Third, nAlEx6.5 . Fourth,nAlEx6.5 . The admARVESTER . Fifth, ARVESTER ,61. PCA B. tabaci cryptic species were observed among the collected populations. The bandedwinged whitefly, Trialeurodes abutilonea Haldeman (GenBank accession number MT976143 −99.83% nucleotide similarity with GenBank reference sequence MG817067) and B. tabaci MEAM1 (GenBank accession number KR559508 −98.78% nucleotide similarity with GenBank reference sequence LN614546) were present at all collection sites. The greenhouse whitefly, Trialeurodes vaporariorum Westwood (GenBank accession number MT976141 −100.00% nucleotide similarity with GenBank reference sequence MK490855) was found on field squash in Spalding county. The citrus whitefly, Dialeurodes citri Ashmead (GenBank accession number MT976142 −96.81% nucleotide similarity with GenBank reference sequence JQ340192) was found on horseweed growing in the non-crop vegetation located next to cotton in Mitchell and Sumter counties. Bemisia tabaci MED was detected from snap bean, lantana, and eggplant in Clarke, Oconee, and Spalding counties, respectively (GenBank accession number MT976144 −99.75% nucleotide similarity with GenBank reference sequence MH205753). In farms from all three above-stated counties/farmscapes, mixed populations of B. tabaci MEAM1 and B. tabaci MED were present in the same field . Bemisia tabaci MED mtCOI sequences had a 99.50% to 99.75% nucleotide identity to the reference sequence (GenBank accession number MH205753). The mtCOI amino acid sequences of B. tabaci MEAM1 and MED were 100.00% identical to their respective reference sequences . Examination of aligned B. tabaci mtCOI sequences (690 bp) revealed the presence of one MEAM1 (MEAM1H1) and one MED (MEDH1) haplotype  (0.12–0.73), confirming the utility of these microsatellite markers for detecting variability among populations (expH) heterozygosity ranged from 0.12 (BEM23) to 0.73 (BEM15) and observed heterozygosity (Ho) ranged from 0.05 (BEM23) to 0.63 (BEM25) , ranged from −0.16 to 0.51 across the loci. Loci BEM6 and BEM23 had a significantly positive ISF value, suggesting heterozygote deficits at these loci. STF across loci ranged from 0.04 to 0.19  and expected heterozygosity (ulations . The aveulations . The exp (BEM25) . FIS, wh to 0.19 . The higB. tabaci populations collected from different host plants and farmscapes are shown in expH) and observed heterozygosity (Ho) for populations from different host plants ranged from 0.30 to 0.42 and 0.29 to 0.44, respectively. For populations from different farmscapes, expected (exp)H and observed heterozygosity (Ho) ranged from 0.32 to 0.49 and 0.14 to 0.47, respectively  significant heterozygote excess was observed among whiteflies collected from okra  and horseweed  . HoweverSTF values among B. tabaci MEAM1 populations collected from different host plants ranged from 0.01 to 0.05 and pairwise STF values among populations collected from different farmscapes ranged from 0.01 to 0.07  for different hosts was found between tobacco and okra (STF (0.07) was observed between farmscape 4 and farmscape 15 .The analysis of molecular variance (AMOVA) revealed that most of the genetic variance was partitioned within populations  . The varas 2.00% . OverallK = 3 as the optimal number of genetic clusters according to log-likehood values of each K and ΔK . Both locations were in close proximity to urban landscapes; therefore, there is a high likelihood that these isolated MED individuals may have dispersed into these crops from nearby greenhouses or ornamentals. Bemisia tabaci MED has a high propensity to develop resistance to insecticides, and its presence in field-grown vegetables can have profound impacts on whitefly management programs [Bemisia tabaci MED has replaced B. tabaci MEAM1 as the dominant whitefly in certain regions of China [B. tabaci MED has been primarily restricted to ornamentals in greenhouses [B. tabaci MED outbreak in field crops and vegetables in the United States as in other places is not obvious. As of now, B. tabaci MEAM1 seems to be better adapted to the farmscapes in the southeastern United States than B. tabaci MED. In a recent study, McKenzie et al., also documented the reoccurrence of New World cryptic species  of B. tabaci in the United States following its disappearance in the late 1980s [The citrus whitefly,  Florida . Howeverronments ,65. Howes vector . Both T.oviridae . Bemisiaprograms . Bemisiaof China ,69,70. Ienhouses . Recentlenhouses . Predictte 1980s ,71. Our STF) between B. tabaci MEAM1 populations collected from different host plants or farmscapes was very low. Likewise, results from population structure analysis  did not suggest evidence of host- or farmscape-specific genetic clustering. Furthermore, a test of isolation by distance (Mantel test) indicated no correlation between genetic differentiation and geographic distance among all populations. Taken together, results suggest that there could be extensive gene flow among whitefly populations inhabiting various crops and farmscapes aided by frequent wind-aided dispersal and high spatial synchrony among B. tabaci populations across farmscapes [B. tabaci MEAM1 populations observed in the current study could also be influenced by population bottlenecks, founder effects, or high mortality caused by insecticides [B. tabaci MEAM1 often experience genetic bottlenecks that can lead to low genetic diversity [B. tabaci MEAM1 populations in Georgia. Nevertheless, the heterozygote excess associated with population bottlenecks is not expected to last more than few generations [B. tabaci, which have high reproductive potential and can complete multiple generations  within a single calendar year [B. tabaci MEAM1 populations since its introduction to the United States in the 1980s might not have been captured in this study. Genetic differentiation  [Dispersal is a vital component of ulations . In the d States ,85,86,87d States ,88,89,90 action) . TherefoB. tabaci MEAM1 genetic cluster to prevail and circulate with changing cropping patterns in cooler seasons. However, it remains to be empirically examined.Whitefly population genetics at broad spatial scales and with respect to invasion routes have been well studied ,32. Howe