Informative Single Nucleotide Polymorphisms Research Articles

Comparison of commercial targeted amplicon sequencing assays for human remains identification casework.

Targeted amplicon sequencing (TAS) facilitates the genotyping of forensically informative single nucleotide polymorphisms (SNPs) using massively parallel sequencing (MPS). For human remains identification, where any extracted DNA is likely to be degraded, TAS may succeed when short tandem repeat (STR) profiling using capillary electrophoresis fails. Further, as well as yielding identity information, SNPs can provide information about ancestry, phenotype, kinship and paternal lineage (Y chromosome haplotypes). Two TAS platforms were compared in this study: Ion AmpliSeq™ panels coupled with Ion Torrent sequencing on an Ion GeneStudio™ S5 Plus System, manufactured by Thermo Fisher Scientific, and the ForenSeq® Kintelligence Kit coupled with Illumina sequencing on the MiSeq FGx® Sequencing System, manufactured by QIAGEN. Four Ion AmpliSeq™ panels (Precision ID Identity, Precision ID Ancestry, DNA Phenotyping and HID Y-SNP) share 177 SNPs with the ForenSeq® Kintelligence Kit and all five were used to profile the DNA extracted from the petrous part of the temporal bone from six skeletonised cadavers. Of the 6 × 177 = 1,062 SNP genotype comparisons, 1,055 (99%) were concordant between the Ion AmpliSeq™ panels and Kintelligence Kit. Of the seven (< 1%) non-concordant SNPs, only three of them (0.3%) would have resulted in erroneous genotypes being reported as a result of allele dropout by either assay, using our optimised relative variant frequency windows for allele calling. We conclude that both the Ion AmpliSeq™ panels and the ForenSeq® Kintelligence Kit were suitable for TAS applied to the human remains in this study.

Guadeloupe and Haiti's coffee genetic resources reflect the crop's regional and global history

Societal Impact StatementDespite strong historical declines, Guadeloupe and Haiti's coffee sectors remain important to rural communities' livelihood and resilience. Coffee also holds value as part of the islands' historical legacy and cultural identities. Furthermore, it is often grown in agroforestry systems providing important ecosystem services, which will become more important as these vulnerable islands work to adapt to a changing climate. Current efforts to revitalize coffee farms and target strategically important specialty markets would benefit from understanding existing genetic resources and the historical factors that shaped them. Our study reveals the rich history reflected in current coffee stands on the islands.Summary The West Indies, particularly former French colonies like Haiti and Guadeloupe, were central to the spread of coffee in the Americas. The histories of these Islands are shared until the 19th century, where they diverged significantly. Still, both Islands experienced a strong decline in their coffee sector. Characterizing the genetic and varietal diversity of their coffee resources and understanding historical factors shaping them can help support revitalization efforts. To that end, we performed Kompetitve Allele‐Specific PCR (KASP) genotyping of 80 informative single nucleotide polymorphism (SNP) markers on field samples from across main coffee‐growing region of Guadeloupe, and two historically important ones in Haiti, as well as 146 reference accessions from international collections. We also compared bioclimatic variables from sampled geographic areas and searched for historical determinants of present coffee resources. At least five Coffea arabica varietal groups were found in Haiti, versus two in Guadeloupe, with admixed individuals in both. The traditional Typica variety is still present in both islands, growing across a variety of climatic environments. We also found Coffea canephora on both islands, with multiple likely origins, and identified C. liberica var. liberica in Guadeloupe. These differences are explained by the Islands' respective histories. Overall, Guadeloupe experienced fewer, but older introductions of non‐Typica coffee. By contrast, several recent introductions have taken place in Haiti, driven by local and global factors and reflecting the history of Arabica varietal development and spread. Diversity on these islands is dynamic, and our results reveal opportunities and limits to the future of Guadeloupean and Haitian coffee.

The Jan Sjödin faba bean mutant collection: morphological and molecular characterization.

Plant mutagenesis creates novel alleles, thereby increasing genetic and phenotypic diversity. The availability of the faba bean (Vicia faba L.) reference genome and a growing set of additional genomic resources has increased the scientific and practical value of mutant collections. We aimed to genotype and morphologically phenotype a historical faba bean mutant collection developed and characterized by Jan Sjödin (1934-2023) over half a century ago in order to increase its value to researchers. The collection was genotyped using high-throughput single-primer enrichment technology (SPET) assays. We used 11,073 informative single nucleotide polymorphism (SNP) markers spanning the faba bean genome to genotype 52 mutant lines along with the background line, cv. Primus. A range of flower, seed, leaf, and stipule mutations were observed. The analysis of population structure revealed a shallow structure with no major subpopulations. Principal component and cluster analyses revealed, to a minor extent, that the mutants clustered by their phenotype. The mutants' phenotypic variation and shallow structure indicate that the Sjödin faba bean collection has the potential to play a significant role in faba bean breeding and in genetic and functional studies.

Genetic Re-assessment of Population Subdivision in Yellowstone National Park Bison.

Yellowstone National Park is home to the only plains bison population that has continually existed as wildlife, on the same landscape, through the population bottleneck of the late 19th century. Nevertheless, by the early 1900s, only 23 wild bison were known to have survived poaching. Salvation efforts included the addition of 18 females from Montana and 3 bulls from Texas to augment this population. A century later, nuclear microsatellite-based population level assessment revealed two genetically distinct bison sub-populations. However, in 2016 an analysis of mitochondrial haplotypes showed the two founding lineages were distributed throughout the park. This study is designed to delineate any current sub-structure in the Yellowstone bison population by strategically sampling the two major summer breeding herds and the two major winter ranges. Population level metrics were derived using the same microsatellite loci as the original study along with a newly developed set of highly informative bison specific Single Nucleotide Polymorphisms (SNPs). Our analyses reveal that the modern bison in Yellowstone National Park currently consist of one interbreeding population, comprised of two subunits.

CattleAssigner: A framework for accurate assignment of individuals to cattle lineages and populations using minimum informative markers

Assigning individual animals to their respective breeds, populations or lineages has immense significance in the evolutionary analyses of global cattle populations besides detecting the underlying genetic variation that may likely have facilitated the adaptation of these breeds to diverse environmental conditions. It is also important in discovering the geographic patterns of genetic variation in cattle populations as well as tracing the geographical origin of breeds, food products, and diseases. Given this, the present study was undertaken to elucidate the minimum number of informative single nucleotide polymorphism (SNP) markers, originally generated using medium-density BovineSNP50 BeadChip across 1823 individuals represnting 73 populations, to assign individual animals to the corresponding lineage/group (African or European or Indicine or admixed) and respective populations within that lineage/group using two well-known supervised machine learning (ML) algorithms namely Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Each of the two ML models were trained with the most informative SNP panels (with sizes of 48, 96, and 192) that were elucidated using two statistical methods i.e., principal component analysis (PCA) and Wright's fixation index (FST), and two ML methods (RF with Gini, and RF with MDA). Three panels with the topmost discriminant SNPs (at 192, 96, and 48 densities) were created for each of the marker preselection approaches. These panels were evaluated, based on their performance vis-à-vis animals’ assignment to respective lineage, population group or population. The results showed that XGBoost achieved the best accuracy of 95% with 192-SNP panel (selected via RF with MDA), followed by RF (93% accuracy) with 192-SNP panel (selected via RF with either Gini or MDA), for animal to lineage assignment. Similarly, RF trained with 48-SNP panel (selected via RF with Gini algorithm) achieved the best accuracy of 97% for assigning animals to African lineage, while it achieved the best accuracy of 89% for assigning animals to admixed populations using 96-SNP panel (selected via PCA). On the other hand, XGBoost achieved the best accuracy of 88% for assigning animals to European breeds using 192-SNP panel (selected via FST method). Furthermore, the results with both RF and XGBoost achieved a poor performance of assigning animals of Indicine lineage to the correct group as the best accuracy for such assignment was 66%, achieved using RF with 192-SNP panel (selected via FST method). In conclusion, the study reports the applicability of statistical and ML approaches for identification of discriminatory SNPs, useful the assignment of individuals to corresponding lineages and to respective populations within lineages besides revealing the efficiency of XGBoost and RF-based ML models in performing such assignments. Both the ML models achieved better performance as compared to statistical ones in assigning the animals to specific lineages while they faired comparably similar to each other for the assignment of individuals to respective populations within respective lineages or population groups.

Development of a microfluidic SNP assay for lineage discrimination in the endangered hazel dormouse

AbstractThe application of Genotyping-by-Sequencing (GBS) approaches is often restricted in wildlife monitoring and conservation genetics, as those fields often rely on noninvasively collected samples with low DNA content. Here we selected a subset of informative single-nucleotide polymorphisms (SNPs) from genome-wide data for lineage discrimination of a locally endangered Eurasian rodent, the hazel dormouse (Muscardinus avellanarius), and designed a microfluidic 96 SNP genotyping assay suitable for noninvasively collected samples. Analyses of 43 samples from different European countries confirmed successful discrimination of the Eastern and Western lineage and local substructure within those lineages, proving the suitability of the developed panel for identifying evolutionary significant units and conservation units. Application with 94 hair and scat samples collected in a recent monitoring study on the hazel dormouse in Southern Germany resulted in &gt; 99.5% amplification success showing the applicability of the new tool in genetic wildlife monitoring and conservation studies.

Association of the dopamine D2 receptor gene SNP rs1800497 with postoperative nausea and vomiting

BACKGROUND Postoperative nausea and vomiting (PONV) are the most frequent complications in the context of anaesthesia. Several studies suggest a contribution of genetic traits to PONV disposition. Single nucleotide polymorphisms (SNPs) located in the cholinergic receptor muscarinic 3 gene CHRM3 (rs2165870) and the potassium voltage-gated channel subfamily B member 2 KCNB2 (rs349358) have been described as independent risk factors for the occurrence of PONV. In addition, further SNPs might be associated with an increased PONV risk, for example a dopamine D2 receptor (DRD2) SNP (rs1800497). OBJECTIVE The primary aim of our study was the development of a new PONV prediction score which includes genetic information of SNPs in the genes CHRM3 and KCNB2, which have been already associated with PONV. The secondary aim of our study was to investigate the association of five additional SNPs with PONV. DESIGN Prospective cohort study. SETTING Single centre study in Germany. RESULTS We could not establish a new PONV prediction score that includes genetic information, due to limited association of the KCNB2 SNP and CHRM3 SNP with PONV. Interestingly, the GA and AA genotypes of the DRD2 rs1800497 in the dopamine D2 receptor gene were associated with PONV 24 h postoperatively, with a relative risk (RR) of GA/AA genotype vs. GG genotype of 1.5 [95% confidence interval (CI) 1.06 to 2.01, P = 0.02]. This association was independent from the Apfel score in a multivariate logistic regression analysis (RR 1.4, 95% CI 1.03 to 1.90, P = 0.03). CONCLUSION The construction of a new PONV prediction score including genetic information was not possible due to limited association of the CHRM3 and KCNB2 SNPs. However, the DRD2 GA and AA genotypes (rs1800497) were associated with PONV and this SNP might be a future candidate for further validation studies aiming for molecular-derived PONV prediction models. TRIAL REGISTRATION German Clinical Study Register – DRKS00021051.

Genetic diversity and population structure in banana (Musa spp.) breeding germplasm.

Bananas (Musa spp.) are one of the most highly consumed fruits globally, grown in the tropical and sub-tropical regions. We evaluated 856 Musa accessions from the breeding programs of the International Institute of Tropical Agriculture of Nigeria, Tanzania, and Uganda; the National Agricultural Research Organization of Uganda; the Brazilian Agricultural Research Corporation (Embrapa); and the National Research Centre for Banana of India. Accessions from the in vitro gene bank at the International Transit Centre in Belgium were included to provide a baseline of available global diversity. A total of 16,903 informative single nucleotide polymorphism markers were used to estimate and characterize the genetic diversity and population structure and identify overlaps and unique material among the breeding programs. Analysis of molecular variance displayed low genetic variation among accessions and diploids and a higher variation among tetraploids (p<0.001). Structure analysis revealed two major clusters corresponding to genomic composition. The results indicate that there is potential for the banana breeding programs to increase the diversity in their breeding materials and should exploit this potential for parental improvement and to enhance genetic gains in future breeding efforts.

Asian Screening Array and Next-Generation Sequencing Based Panels Applied to Preimplantation Genetic Testing for Monogenic Disorders Preclinical Workup in 294 Families: A Retrospective Analysis.

Currently, the most commonly used methods for linkage analysis of pre-implantation genetic testing for monogenic disorders (PGT-M) are next generation sequencing (NGS) and SNP array. We aim to investigate whether the application efficacy of Asian screening array (ASA) in PGT-M preclinical workup for the Chinese population is superior to NGS based single nucleotide polymorphism (SNP) panels. We conducted a retrospective analysis by reviewing 294 couples from a single center over the past 4years and compared the detection results between NGS-based SNP panels and ASA. Using the numbers of informative SNPs upstream and downstream flanking of variants, we assessed the detection efficiency of both methods in monogenic diseases, chromosomal microdeletion syndrome and males with de novo variants, among other scenarios. Results indicate that ASA offers a greater number of informative SNPs compared with NGS-based SNP panels. Additionally, data analysis for ASA is generally more straightforward and may require less computational resources. While ASA can address most PGT-M challenges, we have also identified certain genes in previous tests that are not suitable for PGT-M using ASA. The application of ASA in PGT-M preclinical workup for Chinese populations has good practical value as it can perform linkage analysis for most genetic variants. However, for certain variants, NGS or other testing methods, such as mutated allele revealed by sequencing with aneuploidy and linkage analysis (MARSALA), may still be necessary for completion.

Genome-wide association mapping of quantitative trait loci for chalkiness-related traits in rice (Oryza sativa L.).

Grain chalkiness directly affects the commercial value of rice. Genes related to chalkiness reported thus far have been discovered in mutants, but it has not been identified whether these genes can be used to improve rice quality by breeding. Therefore, discovering more quantitative trait loci (QTLs) or genes related to chalkiness in the rice germplasm is necessary. This study entails a genome-wide association study on the degree of endosperm chalkiness (DEC) and percentage of grains with chalkiness (PGWC) by combining 1.2 million single-nucleotide polymorphisms (SNPs) with the phenotypic data of 173 rice accessions. Thirteen QTLs for DEC and nine for PGWC were identified, of which four were detected simultaneously for both DEC and PGWC; further, qDEC11/qPGWC11 was identified as the major QTL. By combining linkage disequilibrium analysis and SNP information, LOC_Os11g10170 was identified as the candidate gene for DEC. There were significant differences among the haplotypes of LOC_Os11g10170, and the Hap 1 of LOC_Os11g10170 was observed to reduce the DEC by 6.19%. The qRT-PCR results showed that the gene expression levels in accessions with high DEC values were significantly higher than those in accessions with low DEC values during days 21-42after flowering, with a maximum at 28days. These results provide molecular markers and germplasm resources for genetic improvement of the chalkiness-related traits in rice.

Tag SNP selection for prediction of adaptation traits in Braford and Hereford cattle using Bayesian methods

AbstractThis study utilized Bayesian inference in a genome‐wide association study (GWAS) to identify genetic markers associated with traits relevant to the adaptation of Hereford and Braford cattle breeds. We focused on eye pigmentation (EP), weaning hair coat (WHC), yearling hair coat (YHC), and breeding standard (BS). Our dataset comprised 126,290 animals in the pedigree. Out of these, 233 sires were genotyped using high‐density (HD) chips, and 3750 animals with medium‐density (50 K) single‐nucleotide polymorphism (SNP) chips. Employing the Bayes B method with a prior probability of π = 0.99, we identified and tagged single nucleotide polymorphisms (Tag SNPs), ranging from 18 to 117 SNPs depending on the trait. These Tag SNPs facilitated the construction of reduced SNP panels. We then evaluated the predictive accuracy of these panels in comparison to traditional medium‐density SNP chips. The accuracy of genomic predictions using these reduced panels varied significantly depending on the clustering method, ranging from 0.13 to 0.65. Additionally, we conducted functional enrichment analysis that found genes associated with the most informative SNP markers in the current study, thereby providing biological insights into the genomic basis of these traits.

A genome-wide association study on growth traits of Korean commercial pig breeds using Bayesian methods.

This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations. A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance. A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits. The identified SNPs within these regions hold potential value for future markerassisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.

The use of SNP markers for cattle breed identification.

A potential application of single nucleotide polymorphisms (SNPs) in animal husbandry and production is identification of the animal breed. In this study, using chosen marker selection methods and genotypic data obtained with the use of Illumina Bovine SNP50 BeadChip for individuals belonging to ten cattle breeds, the reduced panels containing the most informative SNP markers were developed. The suitability of selected SNP panels for the effective and reliable assignment of the studied individuals to the breed of origin was checked by three allocation algorithms implemented in GeneClass 2. The studied breeds set included both Polish-native breeds under the genetic resources conservation programs and highly productive breeds with a global range. For all of the tested marker selection methods ("delta" and two FST-based variants), two separate methodological approaches of marker assortment were used and three marker panels were created with 96, 192, and 288 SNPs respectively, to determine the minimum number of markers required for effective differentiation of the studied breeds. Moreover, the usefulness of the most effective panels of markers to assess the population structure and genetic diversity of the analyzed breeds was examined. The conducted analyses showed the possibility of using SNP subsets from medium-density genotypic microarrays to distinguish breeds of cattle kept in Poland and to analyze their genetic structure.

Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT).

In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession's year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.

A high-density genetic linkage map and QTL identification for growth traits in dusky kob (Argyrosomus japonicus)

High-density genetic linkage maps provide a foundational genomic resource for quantitative trait locus (QTL) mapping, candidate gene localisation, and comparative genomic analysis. Data from such studies, in turn, can be incorporated into marker or genomics assisted selective breeding strategies for increased efficiency of aquaculture production. This study aimed to construct the first high-density genetic linkage map for dusky kob (Argyrosomus japonicus) a commercially important marine finfish in Asia, Australia, and South Africa. Using 2b-restriction site-associated DNA (2b-RAD) sequencing for genome-wide single nucleotide polymorphism (SNP) genotyping, in three full-sib F1 families (Family 1, n = 69 offspring; Family 2, n = 73 offspring; and Family 3, n = 70 offspring), led to the discovery and genotyping of 22,789 SNPs. Among these, 3992 quality filtered and informative SNPs were mapped to 24 linkage groups (LGs), aligning with the species' proposed haploid chromosome number. The total integrated genetic map spanned a length of 2550 cM with an average marker spacing of 0.68 cM, achieving a genome coverage of 98.61%. Male and female specific maps highlighted differential map lengths and recombination rates between the sexes, with the female map slightly longer and a lower recombination frequency than for the male. QTL analysis for growth-related traits, weight (W), standard length (sL), and Fulton's condition factor (K), identified a total of 25 QTLs of which five QTLs were found to be consistently shared across the traits. Collectively, these shared QTLs explained a substantial proportion of the phenotypic variance, accounting for between 9.30 and 19.30% of the observed variation, suggesting potential major genes for growth. These QTL regions led to the discovery of 65 potential candidate genes, including 11 genes that were specifically located within the shared QTLs. These genes were linked to essential biological processes, including metabolism, immunity, stress response, development, and vascular function. The establishment of this high-density genetic linkage map represents an important genomic resource for comprehending the genetic determinants of economically important traits in A. japonicus, with implications for advancing and optimising ongoing marker-assisted selection (MAS) initiatives in the species.

Systematic analyses of AISNPs screening and classification algorithms based on genome-wide data for forensic biogeographic ancestry inference

Identifying the biogeographic ancestral origin of biological sample left at a crime scene can provide important evidence for judicial case, as well as clue for narrowing down suspect. Ancestry informative single nucleotide polymorphism (AISNP) has become one of the most important genetic markers in recent years for screening ancestry information loci and analyzing the population genetic background and structure due to their high number and wide distributions in the human genome. In this study, based on data from 26 populations in the 1000 Genomes Project Phase 3, a Random Forest classification model was constructed with one-vs-rest classification strategy for embedded feature selection in order to obtain a panel with a small number of efficient AISNPs. The research aim was to clarify differentiations of population genetic structures among continents and subregions of East Asia. ADMIXTURE results showed that based on the 58 AISNPs selected by the machine learning algorithm, the 26 populations involved in the study could be categorized into six intercontinental ancestry components: North East Asia, South East Asia, Africa, Europe, South Asia, and America. The 24 continental-specific AISNPs and 34 East Asian-specific AISNPs were finally obtained, and used to construct the ancestry prediction model using XGBoost algorithm, resulting in the Matthews correlation coefficients of 0.94 and 0.89, and accuracies of 0.94 and 0.92, respectively. The machine learning models that we constructed using population-specific AISNPs were able to accurately predict the ancestral origins of continental and intra-East Asian populations. To summarize, screening a set of high-perform AISNPs to infer biogeographical ancestral information using embedded feature selection has potential application in creating a layered inference system that accurately differentiates from intercontinental populations to local subpopulations.

Estimation of Additive and Dominance Effects in an Acacia crassicarpa Multi-Environment Progeny Trial Using Genomic Pedigree Reconstruction

Abstract Acacia crassicarpa is an important tree species in Southeast Asia, where hundreds of thousands of hectares of planted forests are supported by advancements in silviculture and genetic improvement. Although possible, controlled pollination is impractical for advancing breeding populations, requiring an unreasonable effort to produce more than a few crosses per year. For this reason, breeding populations often are bred by open pollination. This study used large-scale pedigree reconstruction in multi-environment trials to assess full-sib families to model the genetics of the quantitative traits survival, straightness, height, diameter at breast height, tree volume, mean annual increment (MAI), and basic density. The traits were predominantly controlled by additive effects, with heritabilities between 0.09 for survival and 0.45 for basic density. The genetic correlation across sites was high for all traits, showing the low impact of genotype-by-environment interaction. The trait-trait correlation showed that straightness was independent of any other traits, survival was only correlated with MAI, and growth traits were highly correlated among themselves. Basic density was positively correlated with growth traits and MAI. Study Implications: Parentage analysis using an informative single nucleotide polymorphism panel was used to reconstruct pedigree and allow a full-sib family model to estimate additive and dominance effects and genetic correlations across sites and among important traits in an open-pollinated population. The genetic control of all traits assessed in this study was mainly additive. In this scenario, the recommended breeding strategy is forward selection of outstanding progeny for advanced generation breeding and backward selection of outstanding parents to produce seed for deployment via family forestry. Full-sib families can be identified by pedigree reconstruction at a seedling stage, followed by tissue culture multiplication, rooted cutting propagation, and plantation establishment.

Genotyping by sequencing for the construction of oil palm (Elaeis guineensis Jacq.) genetic linkage map and mapping of yield related quantitative trait loci.

Oil palm (Elaeis guineensis Jacq.) is one of the major oil-producing crops. Improving the quality and increasing the production yield of oil palm have been the primary focuses of both conventional and modern breeding approaches. However, the conventional breeding approach for oil palm is very challenging due to its longevity, which results in a long breeding cycle. Thus, the establishment of marker assisted selection (MAS) for oil palm breeding programs would speed up the breeding pipeline by generating new oil palm varieties that possess high commercial traits. With the decreasing cost of sequencing, Genotyping-by-sequencing (GBS) is currently feasible to many researchers and it provides a platform to accelerate the discovery of single nucleotide polymorphism (SNP) as well as insertion and deletion (InDel) markers for the construction of a genetic linkage map. A genetic linkage map facilitates the identification of significant DNA regions associated with the trait of interest via quantitative trait loci (QTL) analysis. A mapping population of 112 F1 individuals from a cross of Deli dura and Serdang pisifera was used in this study. GBS libraries were constructed using the double digestion method with HindIII and TaqI enzymes. Reduced representation libraries (RRL) of 112 F1 progeny and their parents were sequenced and the reads were mapped against the E. guineensis reference genome. To construct the oil palm genetic linkage map, informative SNP and InDel markers were used to discover significant DNA regions associated with the traits of interest. The nine traits of interest in this study were fresh fruit bunch (FFB) yield, oil yield (OY), oil to bunch ratio (O/B), oil to dry mesocarp ratio (O/DM) ratio, oil to wet mesocarp ratio (O/WM), mesocarp to fruit ratio (M/F), kernel to fruit ratio (K/F), shell to fruit ratio (S/F), and fruit to bunch ratio (F/B). A total of 2.5 million SNP and 153,547 InDel markers were identified. However, only a subset of 5,278 markers comprising of 4,838 SNPs and 440 InDels were informative for the construction of a genetic linkage map. Sixteen linkage groups were produced, spanning 2,737.6 cM for the maternal map and 4,571.6 cM for the paternal map, with average marker densities of one marker per 2.9 cM and one per 2.0 cM respectively, were produced. A QTL analysis was performed on nine traits; however, only QTL regions linked to M/F, K/F and S/F were declared to be significant. Of those QTLs were detected: two for M/F, four for K/F and one for S/F. These QTLs explained 18.1-25.6% of the phenotypic variance and were located near putative genes, such as casein kinase II and the zinc finger CCCH domain, which are involved in seed germination and growth. The identified QTL regions for M/F, K/F and S/F from this study could be applied in an oil palm breeding program and used to screen palms with desired traits via marker assisted selection (MAS).

An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing.

Objective: The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the performance of simple clinical models in predicting whether a diabetic foot ulcer (DFU) will heal. Approach: We utilized a cohort study (n = 206) that included clinical factors, measurements of circulating endothelial precursor cells (CEPCs), and fine sequencing of the NOS1AP gene. We derived and selected relevant predictive features from this patient-level information using statistical and machine learning techniques. We then developed prognostic models using machine learning approaches and assessed predictive performance. The presentation is consistent with TRIPOD requirements. Results: Models using baseline clinical and CEPC data had an area under the receiver operating characteristic curve (AUC) of 0.73 (0.66-0.80). Models using only single nucleotide polymorphisms (SNPs) of the NOS1AP gene had an AUC of 0.67 (95% confidence interval, CI: [0.59-0.75]). However, models incorporating baseline and SNP information resulted in improved AUC (0.80, 95% CI [0.73-0.87]). Innovation: We provide a rigorous analysis demonstrating the predictive potential of genetic information in DFU healing. In this process, we present a framework for using advanced statistical and bioinformatics techniques for creating superior prognostic models and identify potentially predictive SNPs for future research. Conclusion: We have developed a new benchmark for which future predictive models can be compared against. Such models will enable wound care experts to more accurately predict whether a patient will heal and aid clinical trialists in designing studies to evaluate therapies for subjects likely or unlikely to heal.

Species-specific SNP arrays for non-invasive genetic monitoring of a vulnerable bat

Genetic tagging from scats is one of the minimally invasive sampling (MIS) monitoring approaches commonly used to guide management decisions and evaluate conservation efforts. Microsatellite markers have traditionally been used but are prone to genotyping errors. Here, we present a novel method for individual identification in the Threatened ghost bat Macroderma gigas using custom-designed Single Nucleotide Polymorphism (SNP) arrays on the MassARRAY system. We identified 611 informative SNPs from DArTseq data from which three SNP panels (44–50 SNPs per panel) were designed. We applied SNP genotyping and molecular sexing to 209 M. gigas scats collected from seven caves in the Pilbara, Western Australia, employing a two-step genotyping protocol and identifying unique genotypes using a custom-made R package, ScatMatch. Following data cleaning, the average amplification rate was 0.90 ± 0.01 and SNP genotyping errors were low (allelic dropout 0.003 ± 0.000) allowing clustering of scats based on one or fewer allelic mismatches. We identified 19 unique bats (9 confirmed/likely males and 10 confirmed/likely females) from a maternity and multiple transitory roosts, with two male bats detected using roosts, 9 km and 47 m apart. The accuracy of our SNP panels enabled a high level of confidence in the identification of individual bats. Targeted SNP genotyping is a valuable tool for monitoring and tracking of non-model species through a minimally invasive sampling approach.