Glioblastoma multiforme (GBM) is one of the most common primary brain tumours in adults, accounting for almost 65% of all cases. Among solid tumours, GBM is characterised by strong angiogenesis, including the highest degree of vascular proliferation and endothelial cell hyperplasia. Despite numerous improvements in existing treatment approaches, the prognosis of GBM patients remains poor, with a mean survival of only 14.6 months. Growing evidence has shown significant overexpression of the ephrin type-A receptor 2 (EphA2) receptor in various malignancies, including GBM, as well as a correlation to poor prognoses. It is believed that EphA2 receptors play important roles in mediating GBM tumourigenesis, including invasion, metastasis, and angiogenesis. Despite the clinical and pathological importance of tumour-associated vasculature, the underlying mechanism involving EphA2 is poorly known. Here, we have summarised the current knowledge in the field regarding EphA2 receptors’ roles in the angiogenesis of GBM. Ephrin type-A receptor 2 (EphA2) overexpression has been correlated with a poor prognosis in most cancer types, including endometrial , colorecThe human EphA2 gene is located on chromosome 1, which encodes the 130kDa EphA2 protein with approximately 976 amino acids. It is known that 90% of its sequences are homologous to the EphA2 mouse . DespiteForward signaling is a signal transduction that originates from ephrin ligands and is directed toward Eph receptors. This is also called ephrin:EphA2 forward. Meanwhile, reverse signaling originates from Eph receptors and is directed toward ephrin ligands. This is also called EphA2:ephrin reverse. Due to their membrane localisation, these signals can also be simultaneously activated in both forward and reverse directions, which is known as ephrin-EphA2 bidirectional signaling.In a situation where two or more Eph-ephrin interactions occur simultaneously, the signaling can be classified as either parallel or anti-parallel. Parallel signaling is induced when forward and reverse signaling occur simultaneously and in parallel to each other, while anti-parallel signaling is a simultaneously occurring forward signaling in which forward ephrin-EphA2 signals are conveyed toward the different directions of the Eph receptor.The EphA2 receptors could be triggered and activated upon binding to the Ephrin A1 ligand. The membrane attachment of both EphA2 and Ephrin A1 provides various mode mechanisms of interactions that are unique from other receptor tyrosine kinases families. EphA2 and Ephrin A1 can function independently of each other through an interchange with other signaling systems . PrimariThe EphA2 receptor phosphorylation and kinase activity have been proven to induce tumour malignancy and confer the cells’ oncogenic potential . EphA2 rStimulation of EphA2 causes powerful changes in a tumour cell’s behaviour. Interaction between EphA2 and Ephrin A1, along with other guidance molecules, will navigate developmental guidance that causes sheets of cell layers to become tumours. The contact of Ephrin A1 and the EphA2 receptor in neighboring cells conveys the signal forward, which will cause the cells to repel from each other. If the EphA2 receptor is neither activated nor called as reverse signaling, then the interaction with the ligand will cause either cell adhesion or repulsion . As a reIn addition, ephrin-EphA2 interaction can induce short distance signaling and can mediate cellular processes, such as cell proliferation, migration, formation of tissue boundaries, axon guidance, and platelet aggregation , 21, 22.In brain tumours, high expression of EphA2 is mostly detected in advanced grades of tumours, such as anaplastic astrocytoma and GBM . EphA2 iIn addition to being highly expressed in GBM tumour cells, EphA2 expression has also been correlated with the subpopulation of GSC regarding their propagating ability and pool size. It is known that the heterogeneity, molecular genetic make-up, and epigenetics of GBM populations make tumours resilient to current therapeutic strategies. The GSC subpopulation has particularly emerged as one of the key players in GBM recurrence. Specific roles of EphA2 via GSC mediation have shown an interesting relationship with the capacity of the cells to expand their pool size, which ultimately led to GBM recurrence in a previous study. Even though EphA2 receptors’ regulatory functions in GBM tumourigenesis remain limited, knowing that the ability to knock down GSC self-renewal and tumourigenic capabilities exists via modulation of EphA2 expression is intriguing .Angiogenesis, or the formation of new blood vessels, from the existing vasculature is one of the hallmarks of GBM, which is characterised as a highly vascularised solid tumour. Continuous recruitment of new blood vessels creates a favourable microenvironment in GBM, which allows the malignant transformation of a tumour. Several recent studies have clearly indicated involvement of EphA2 in GBM angiogenesis. Its high expression was found along the tumour vasculature of GBM, suggesting potential roles in neovascularisation . While tIn general, EphA2 works via Ephrin A1’s signaling axis to regulate multiple events in the transformation of tumour malignancies. This includes the modulation of tumour-associated angiogenesis, which is important to the survival and maintenance of tumour growth. In ovarian cancer and mammary tumourigenesis, EphA2 overexpression has been associated with increased microvascular density, indicating that EphA2 has a role in promoting angiogenesis in the tumour microenvironment , 29. MorIt has been acknowledged that the EphA2-mediated angiogenesis process in cancer occurs via the recruitment of phosphoinositide 3-kinase (PI3K) and the stimulation of downstream molecules of the Vav family of guanine nucleotide exchange factors (GEFs) and Rac1-GTP. Functionally, this process is partly mediated through crosstalk with pro-angiogenic molecules, such as growth factor receptors and adhesion molecules, including integrins and cadherins .In GBM cell lines, recent evidence has shown that EphA2 regulates vascular endothelial growth factor receptor 2 (VEGFR-2) expression at both the gene and protein levels. Nevertheless, inhibition of EphA2 did not show any impairment of VEGF expression in the same study. It is therefore interesting to suggest that EphA2 may regulate vessel sprouting during developmental angiogenesis independently via VEGFR-2 without affecting VEGF in GBM cells . VEGFR-2From another perspective, it is intriguing to explore the correlation of EphA2 overexpression in GSC in relation to the GBM angiogenesis process. The GSC population can generally maintain an undifferentiated state, which supports their self-renewal and tumourigenicity. Interestingly, it has been demonstrated that GSCs in GBM tumours can transdifferentiate into endothelial-like cells, which leads to the sprouting of new blood vessels known as vasculogenic mimicry (VM). VM is often detected in high-grade gliomas and has been correlated with poor patients’ prognoses . LocalisGBM are typically aggressive, infiltrative, and resistant to conventional therapies, and poor survival rate is often correlated with tumour recurrence. The first lines of defense in a clinical setting include surgery, chemotherapy, and radiotherapy, which are rarely curative for GBM. Because GBM is one of the most vascularised solid tumours, one potential therapeutic strategy for GBM is targeting angiogenesis. Growing evidence has shown that combination therapy using anti-angiogenic drugs and/or radiotherapy and chemotherapy may be beneficial for treating recurrent GBM. Understanding the molecular pathways behind aberrant blood vessel recruitment in GBM provides an exciting set of potential targets for therapeutic intervention. Overexpression of EphA2 receptors provides a hint for further exploration of the underlying mechanisms behind GBM tumour-associated vasculature formation as well as their crosstalk with other molecules in the signaling pathways. The intricacies of recent studies have not only enhanced our understanding of EphA2 involvement in the pathogenesis and various cellular processes of GBM but also provided an avenue for realising the potential of EphA2 as a therapeutic target for treating malignancies. In addition to its high and localised expression in GBM, very low expression of the receptor is also found in a normal brain, making it an ideal molecular target for medical intervention.


For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The algorithm makes use of personal genomic information and performs an unbiased alignment towards genome indices carrying both reference and alternative bases. Importantly, this breaks the dependency on reference genome splice site dinucleotide motifs and enables iMapSplice to discover personal splice junctions created through splice site polymorphisms. We report comparative analyses using a number of simulated and real datasets. Besides general improvements in read alignment and splice junction discovery, iMapSplice greatly alleviates allelic ratio biases and unravels many previously uncharacterized splice junctions created by splice site polymorphisms, with minimal overhead in computation time and storage. Software download URL:  Besides its unprecedented resolution due to deep sequencing, one of the great advantages of HTS is that it does not rely on any prior knowledge of sequence in sampling the genomic content from any given subject, which is critical for the discovery of previously uncharacterized genomic features. Recognizing these opportunities, large studies such as The Cancer Genome Atlas  ,2 and thih.gov/)  have genih.gov/) . These dThis paper studies whether and how SNPs called from DNA-seq of a given subject can be used to perform more accurate alignment of RNA-seq reads from that same individual. As we mentioned above, sequencing is a sampling process that is subject-specific and priori knowledge-independent. Unfortunately, a level of bias can be introduced if RNA sequencing data are aligned to a standard reference genome. Although the current state-of-the-art spliced aligners already perform quite well in the discovery of previously unannotated splice junctions and even some structure variations, they almost always utilize the reference genome of a species as the template for read alignment –10. The Additionally, splice site polymorphisms, where non-canonical splice site can be converted into canonical splice sites  are likely to be returned and have to be filtered out. Eventually, this may result in a partial alignment (S3).The left panel in iMapSplice resolves this issue by including knowledge of the SNPs in each alignment step, as shown in the right panel of kphased (201bp by default) from the two genomic haplotypes are extracted. The reference nucleotides are then replaced with alternate nucleotides at all of the SNP positions. However, iMapSplice-phased cannot be used when the phase information is not available, in which cases iMapSplice-unphased will be applied.SNP-mers can be derived in two ways corresponding to two variants of iMapSplice: iMapSplice-phased and iMapSplice-unphased. iMapSplice-phased applies when the phased genotype data are available (such as in the 1000 genomes project). In this case, SNP-mers of a fixed length kmax (201bp by default) and kmin (31bp by default) denote the maximum and the minimal length of a SNP-mer, respectively. In extracting the SNP-mer for each SNP, there are three scenarios according to the distance d between each SNP and its nearest neighbor SNP. When d > kmax, a SNP-mer of length kmax is extracted; when kmax ≥ d ≥ kmin, a SNP-mer of length d is extracted. In the two cases above, except for the specific SNP, all of the other bases are exactly the same as in the reference genome since there are no other SNPs within the genomic window where the corresponding SNP-mer covers. However, when d < kmin, there is more than one SNPs within the kmin window size. This indicates a SNP-rich region. In such cases, if there are n SNPs within this window and one of them is the SNP that all SNP-mers are centered upon, the rest are the n-1 SNPs within the SNP-mer region. These additional SNPs increase the variety of the SNP-mers. Since the variant in the middle is set, there would be 2n-1 possible nucleotide base combinations for the other n-1 SNPs, all these are different from reference. iMapSplice-unphased will randomly select m of them. The selection of m value affects the performance of iMapSplice-unphased on mapping reads covering this SNP. A small m may miss the perfect SNP-mer which matches read sequences without any error. However, a large m could confuse the aligner as too many possibilities are provided and also increase the running time. However, such SNP-rich regions are rare, and thus have little impact on the overall performance. In iMapSplice-unphased, a combination of variable length SNP-mers are used. Let kphased in iMapSplice-phased, kmax and kmin in iMapSplice-unphased) also affects how well iMapSplice works. A SNP-mer has to be long enough to allow a partial read segment to be confidently mapped. However, a SNP-mer too long is not necessary, as they will repeat the exact sequence from the reference genome. To investigate this relationship, we conducted an experiment to study the impact of SNP-mer length on iMapSplice-phased performance. The results and a more extensive discussion of SNP-mer length selection strategy can be found in Furthermore, the selection of SNP-mer length related parameters  in the reference genome to detect splice junctions. However, a canonical splice site with subject-specific SNPs will go undetected as its reference counter-part is non-canonical and cannot be recognized by reference-based aligners. Even though some aligners are capable of reporting non-canonical splice junctions, high penalties are given to their alignments to avoid false positives . To solvThe last step of iMapSplice completes segment assembly, candidate alignment scoring, and selection. In this step, aligned segments are assembled and different candidate alignments are generated with different potential combinations of segments. Candidate alignments are then scored based on the total number of mismatches, spliced alignment, and mapped length. iMapSplice will only report the alignment with the highest score. If multiple alignments have the same highest score, all of them will be reported. One of the most important metrics when scoring is the number of mismatches for each alignment. iMapSplice removes the mismatches that can be attributed to SNPs.In this section, we report the performance of iMapSplice with regard to its capability for unbiased alignment of reads harboring SNPs and for the discovery of splice junctions with polymorphic splice sites.Performance was assessed using two types of data: one based on simulated RNA-seq reads and the other using real human datasets.Simulated datasets: Simulated RNA-seq reads were generated by BEERS  with twoReal datasets: 74 randomly selected RNA-seq datasets and their corresponding genotypes were downloaded from the Geuvadis RNA sequencing project  and the To assess the performance of iMapSplice, we compared it to a number of publicly available RNA-seq aligners. All of the methods, version information, and the indices used are listed in We first assessed improvements of read alignment performance that resulted from incorporating the knowledge of SNPs using the four simulated datasets. In this experiment, we compared iMapSplice-unphased against all the “Ref-based” tools and HISAT2 SNP. iMapSplice-phased and HISAT2 POP are not applicable since the SNP data were not phased and not consistent with the population profile. The “Mask-based” tools and rPGA are not proposed for general read mapping. We collected the numbers of accurate unique alignments reported by each method. As shown in Incorporating SNPs into the index also improved the alignment performance for HISAT2 in three out of the four datasets. Note that the improved alignment percentage varied since it is a function of the percent of SNP affected reads, which is much less in low error data than in high error data. For the subset of simulated reads that achieved an accurate unique alignment with iMapSplice-unphased but failed by MapSplice, we analyzed their alignment status with MapSplice that utilizes the typical method of mapping to reference genome . In geneIt is known that mapping reads to a reference genome introduces an overestimation of reference allele frequency and underestimation of non-reference allele frequency . Reads cWe applied iMapSplice-phased to datasets from 74 individuals  the gain of a canonical splice site  and (ii) the loss of a canonical splice site. We examined how these polymorphisms affected steady state expression (read counts) at the corresponding splice junctions . In thisAmong both types of polymorphic splice junctions, although many had low coverage, iMapSplice-phased detected hundreds of personal splice junctions that exhibited significant expression changes in association with the gain and/or loss of canonical splice site motifs.For the type (i) polymorphic splice junctions , previouC14orf159, ANXA6, and TMEM216 . The polymorphism (G > C) in TMEM216 . Resulting changes in splice site utilization can be functionally significant and are important to detect. iMapSplice enhances the detection of personal splice sites by considering both reference and individual alternate alleles in determining the optimal alignment for each RNA-seq read.Arabidopsis thaliana, Drosophila melanogaster, and Mus musculus, in the same manner.Performance-wise, iMapSplice is a lightweight approach with minimum overhead in both storage and running time compared to other alignment methods that are also capable of considering individuals SNPs during the mapping process. iMapSplice can be readily applied to the datasets collected in large consortium, such as TCGA, ICGC, as well as the 1000 genomes project by taking either the original reads or the alignment file as input, making it possible to alleviate reference bias and uncover functionally important personalized transcript variants as a result of either polymorphic splice site or allele specific transcript. We expect to continue to improve iMapSplice in the near future to incorporate other structure variations such as small indels. The alignment strategy will be fairly similar and the extension should be straightforward. Also, besides human data, iMapSplice could also be applied to other model species, such as Some approaches ,19 have S1 File(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Unreliable terminology and incompatible units of antioxidant activity/concentration expression lead to the failure of antioxidant clinical trials, ambiguity of conclusions about the effect of a chosen therapy in medicine and evaluation of food quality, diet, difficulties using information in monitoring the training process in sports, etc. Many different terms  and methods: Trolox equivalent capacity assay (TEAC), Ferric Reducing Antioxidant Power assay (FRAP), Cupric Reducing Antioxidant Capacity assay (CUPRAC), antioxidative activity assay (ABTS), the oxygen radical absorbance capacity (ORAC), and different options of electrochemical ones) proposed for the determination of antioxidants are described. Possible approaches to the development of this field of science and practice are considered. The 18th International Congress of the International Society of Antioxidants and Health (ISANH), held in Beirut on 3–4 May 2017 ,6. Thus,As a result, the current situation limits the use of numerous available data ,8. The rAntioxidants are the main reactive oxygen species (ROS) and reactive nitrogen species (RNS) scavengers. ROS and RNS are oxidative stress generators ,11,12. RHalliwell and Gutteridge proposed defining an antioxidant as “any substance that, when present at low concentration compared with those of an oxidizable substrate, significantly delays or prevents oxidation of that substrate” .This is a very fast-growing section of chemistry, biochemistry and bioanalysis in particular. A lot of works are published in the field. According to Scopus, the number of papers containing the word “antioxidants” in journals published in 2000–2018 is 301,795.The aim of the work is a brief analysis of terminology and methods and consideration of methods for analyzing different samples and expressing results, discussing the prerequisites for obtaining comparable results.The situation as it had developed by 2013 is covered in . It has It is worth paying attention to the terms ‘antioxidant’ and ‘antiradical’ properties: An antiradical property/activity characterizes the ability of components to react with free radicals, but an antioxidant property/activity represents the ability to inhibit (reduce) all molecules having high Ox/Red potential, which makes them destructive for body structures . This maAntioxidant properties are proposed to be defined as “antioxidant capacity” ,19 and “Sometimes, different meanings are attributed to the terms “antioxidant activity” and “antioxidant capacity”.There is a mixture of thermodynamic and kinetic concepts: For example, the term antioxidant activity in different works is used as a thermodynamic or kinetic one. In general, it should be used as a thermodynamic term and not as a kinetic one. The term “antioxidant activity” as a kinetic one  is used The use of these terms in the literature by year (covering the period from 2000 to 2018) can be seen in the diagram presented in The frequency of one or another term application is illustrated by the diagram in It is easy to see that the term “antioxidant activity” is the most commonly used. This seems to be natural, since this term gives direct information about the total concentration of antioxidants/oxidants in the sample.We note an important fact: In  and a nuin vitro assay of phenolic and polyphenolic antioxidants , and Ferric Reducing Antioxidant Power assay (FRAP) are described in original papers /K4[Fe(CN)6], I2/2I−, Br2/2Br− [Electrochemical methods are based on redox reactions occurring on the electrode surface  or in the solution volume , the result of which is recorded as current or potential ,32,33,34Br2/2Br−  or a reaBr2/2Br−  are usedBr2/2Br− ,36,37,38-Most fully meet the nature of the OS and the action of the body’s antioxidant defense system;-They are characterized by instrumental and operator simplicity, financial, temporal, and informative efficiency;-They are easily applied in the clinical laboratory, and on their basis, sensory systems are created for work in onsite and in situ formats.As regards the electrochemical methods:Electrochemical methods give direct information about the concentration of the analyte. In connection with these, we believe that the term “activity” is preferred. We are convinced that it is precisely this term and not “capacity” that does not have a certain physical meaning, so it is advisable to use it to characterize the antioxidant system. The latter is especially significant when using electrochemical monitoring methods, for example, in , becauseThe questions considered in the article are fundamental for the development of the antioxidants and oxidative stress science, evaluation of its role in the state of health, and the use of AO in diet and therapy. Taking into account the very significant role of AO in life support, it is crucial to recognize the need of studies focusing on the food–nutrients–health–therapy chain.Understanding and choosing a treatment strategy, diet, etc. is impossible without the creation of uniform standardized methods and comparable ways of expressing concentration, conducting clinical trials, and making recommendations.We see no reason for skepticism when considering the informativeness of data on antioxidant/oxidant activity. Rather, the issue is the inconsistency of these data and the lack of standardized analytical methods and clear and comparable ways of expressing measurement results.Trends and ways of development, in addition to the ones discussed above, include expansion of applications, in particular, in sports, the development of new sensors, for example, wearable, and instruments for onsite and online application.Monitoring the oxidant/antioxidant state of the media is a tool for the development of a number of areas of science and technology: medicine, sports, food, pharmaceutical, cosmetic, and other technologies. Research in this area plays an important role in the detection of serious diseases in the early stages, the choice of therapy, and the evaluation of its effectiveness.


Association mapping led to the identification of seven significant associations for three traits using MLM approach at a P value of ≤ 0.001. Three associations were identified for total dry weight, two each for leaf area index and rachis length. The qtlLA1 was found to be highly significant at a P value of 7.39E-05 (18.4% phenotypic variance) which is located on chromosome 4. Two QTLs (qtlLA2 and qtlRL1) were located on chromosome 1, which explained 11.9% and 12.4% of phenotypic variance respectively. Three QTLs for total dry weight were located on chromosome 2, 14 and 16, all-together explained 40% phenotypic variance. The results showed that the SNP-trait associations identified in the present study could be used in selection of elite oil palm germplasm for higher yields.The marker-trait association for complex traits using genotyping by sequencing (GBS) method is being widely spread in plants. The study aimed to identify significant single nucleotide polymorphism (SNP) associations for rachis length (RL), leaf area (LA) and total dry weight (TrDW) in oil palm among diverse African germplasm. The Illumina NextSeq platform has been used for SNP genotyping and retained 4031 fully informative SNPs after applying the filter criterion. These 4031 SNPs were used for genome wide association study for the above three traits. The LD decay rates of the African germplasm using GBS data of SNP is observed to be 25 Kb at 0.45 of average pair wise correlation coefficient (r Elaeis guineensis Jacq.) belongs to the family Arecaceae, which is a major contributor (approximately 40%) of world edible vegetable oil. The genome size of oil palm is 1.8 Gb with haploid chromosome number of 16. The genome sequence of an elite dura palm was published [1 populations, which lacks clear segregation pattern which can be improved by using advanced segregating generations or highly diverse natural population for association mapping [Oil palm  by increasing the photosynthetic efficiency as evidenced by high significant correlation observed by several workers [In perennial crops, natural populations offer advantages like much higher mapping resolution, greater allele number and broader reference population, and less research time in establishing an association . The Afr workers . The lon0 480N, longitude 81070E). All the germplasm belongs Dura fruit type. The 96 oil palm germplasm were treated identically in terms of fertilizer application and pest controls. The samples selection was based on the phenotypic variation of selected morphological traits. The experiments were conducted with the consent of Director, ICAR-IIOPR, India. The genomic DNA of oil palm genotypes was isolated by standard method as described by Murray and Thomson [Oil palm germplasm representing four African countries were used for SNP genotyping . The ger Thomson  with few Thomson . The genThe phenotypic data of leaf area, rachis length and total dry weight content of oil palm were recorded for four consecutive years (2010–2014) according to Corley et al. . The traSNP genotyping among the oil palm genotypes was done by following the GBS approach as given by Elshire et al.  with Ape-4. The genome wide patterns of LD decay were estimated by average of R2 in distance intervals across the 16 chromosomes of whole genome of oil palm.The population structure analysis was done by model based clustering method  as adoptviz., Cameroon, Tanzania, Guinea-Bissau, and Zambia were used for SNP discovery by genotyping by sequencing method. The genomic DNA of 96 African germplasm were subjected to ApeKI digested paired end libraries and sequenced on Illumina NextSeq500 platform. The sequencing resulted in 325 million reads covering 50.78 Gb, at a mean of 3.4 million reads per sample. The sequence data obtained by GBS is lesser than earlier studies [1 oil palm population. Teh et al. [1 progeny. The GBS raw data was imputed to SNP calling and data normalization has been done using MAF of 0.05 and by removing 80% missing SNP calls. Only SNP calls >90% has been considered for further analysis. A total of 325 million raw reads generated, of which 2.4 million SNPs passed bar coding and quality thresholds. Finally, 4031SNPs were used for mapping to the reference genome with a range between 157 on chromosome 15 to 455 on chromosome 1  values. The highest peak value of ΔK was observed at K = 4. High amount of admixture also presented among different groups which may be due to involvement of exotic germplasm. The same type of admixture was also found in earlier reports . The str2). The long range LD in the highly diverse populations can be further explained by breeding selections. The process of selective sweep will much enhance the breeding value of the crops like oil palm. The LD of oil palm plantations in general decays considerably faster than in annual crops like rice and foxtail millet. In both the crops LD decays 100 Kb to 200 Kb (2–5), which decays to 50% of its initial value by 1 Kb with in 150 Kb. This difference may be due to low genome coverage of markers and fewer genotypes in the study. Similar LD decay also observed by Teh et al. [2 value.The pattern of LD decay is crucial for identifying significant QTLs of any association mapping studies  and for h et al.  where thviz., 1, 2, 4, 14, and 16 which explained a phenotypic variance from 11.5 to 18.5%. Two significant QTLs were identified for leaf area, which together explained 30.5% of phenotypic variance. Among them, qtlLA1 (SGI|741241788|) found significant at P value of 7.39E-05 (18.5% phenotypic variance). The qtlLA2 (SGI|741241880|) linked at P of 0.001 and explained 12% R2 value. These two QTLs located on chromosome 4 and 1 respectively. The manhatton plot for leaf area given in High yielding oil palm germplasm is an immediate output required for the farming community. There is a big gap in the theoretical CPO yield 18 tons/ha) and global actual average yield (2.0 to 4.0 tons/ha) over the past 50 years  tons/ha . It is i2 value  located on chromosome 14 found significant at P of 0.0005 which explained 15% phenotypic variance. The three QTLs for total dry weight content together explained 42% of total phenotypic variance. Likewise two QTLs were found significant for rachis length. The manhatton plots for rachis length is given in 2 value . The preee et al  also ideviz., leaf area, rachis length and total dry weight content. Association mapping resulted in identification of seven highly significant QTLs for different traits at a P value of ≤ 0.001 by MLM approach. More genetic loci were identified to be associated with total dry weight, followed by leaf area and rachis length. The QTLs identified in the present study could be used in selection of elite oil palm germplasm for higher yields.The genotyping by sequencing method used for GWAS study of African germplasm using GBS method to identify the QTLs for three yield related traits S1 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Nanofluids have great potential in a wide range of fields including solar thermal applications, where molten salt nanofluids have shown great potential as a heat transfer fluid (HTF) for use in high temperature solar applications. However, no study has investigated the use of molten salt nanofluids as the HTF in direct absorption solar collector systems (DAC). In this study, a two dimensional CFD model of a direct absorption high temperature molten salt nanofluid concentrating solar receiver has been developed to investigate the effects design and operating variables on receiver performance. It has been found that the Carnot efficiency increases with increasing receiver length, solar concentration, increasing height and decreasing inlet velocity. When coupled to a power generation cycle, it is predicted that total system efficiency can exceed 40% when solar concentrations are greater than 100×. To impart more emphasis on the temperature rise of the receiver, an adjusted Carnot efficiency has been used in conjunction with the upper temperature limit of the nanofluid. The adjusted total efficiency also resulted in a peak efficiency for solar concentration, which decreased with decreasing volume fraction, implying that each receiver configuration has an optimal solar concentration. The overall efficiency of a concentrating solar power system depends on three main factors, the efficiency of the receiver, field efficiency  and the Carnot efficiency. With the use of nanofluids both receiver and Carnot efficiencies can be improved ,2. NanofThe performance of the receiver can be tuned to suit conditions by altering the size, shape, solar concentration and material type of the nanoparticles, as the optical properties of the nanofluid are dominated by the nanoparticles ,22.DACs do not require a surface absorption plate, which results in a significantly simpler receiver design and reduced cost and labour, as surface-absorbing plates require complex manufacturing processes Nanofluids also possess superior thermophysical properties, such as enhanced thermal conductivity, heat transfer coefficient and in some cases enhanced specific heat capacity ,24,25.In a conventional solar thermal receiver the solar radiation is directed onto a high-absorptive surface where it is converted to thermal energy ,6,7,8,9.Tyagi et al.  develope3-KNO3) nanofluids as the HTF. The model has been validated by comparing to the results found in the literature. Factors of the receiver to be taken into account are the thermal re-radiation of the HTF to the environment, convective and conductive heat transfer with the environment, forced convection due to wind, volume fraction of nanoparticles and height of receiver. This research is therefore significant because molten salt nanofluids have been shown as an HTF for high temperature applications. There is a large amount of published literature on the thermophysical and rheological properties of low temperature nanofluids. These works mainly include aqueous and glycol based nanofluids with the temperatures generally less than 100 °C. There is also comprehensive literature on the possible mechanisms behind the changes in thermophysical and rheological properties, which have shown good agreement with the experimental works in some instances. Numerous works have also presented on empirical models with good results. Molten salt nanofluids have received very little attention in comparison. These have been shown to act differently to other nanofluids, as with the addition of nanoparticles in molten salt nanofluids show an increase in specific heat capacity while other nanofluids, including those that are aqueous and glycol based, show a decrease. Similarly, some research studies have been conducted on modelling CSP nanofluid receivers. However, these works have also been limited to low to medium temperature nanofluids, the majority of which are aqueous, and glycol based . There iThe paper is structured as follows: a comprehensive literature is reviewed and the research gap is identified in the introduction above, details of the computational model, including governing equations, solution method, initial and boundary conditions, model validation, and results and discussion is presented next. Finally, conclusion of this new study has been presented at the end.® engineering software , which is assumed to be volumetrically heated as it flows between two parallel plates of height h. The model is 3D to account for a volumetric heat source but is 2D in nature as the width of the receiver does not affect the outcome of the model in any way and the sidewalls are modelled as planes of symmetry. The flow is modelled as fully developed with the two plates as no-slip walls and three heat losses are included, namely convection to the ambient, surface to ambient radiation and thermal re-emission from within the fluid, the first two of which act on the top surface. As shown in Equation (1), the thermophysical properties of the nanofluid are taken as those of the base fluid due to low concentrations of particles considered and are modelled as temperature dependent; the rheological properties are estimated by using Krieger type viscosity model. The bottom of the receiver is modelled as an adiabatic black wall and reflective losses are not considered.This study investigates a flowing solar receiver in which the concentrated solar radiation is assumed to be incident normally and directly absorbed within the channel due to suspended nanoparticles, as illustrated in Krieger-Dougherty model:The optical properties of nanofluids are dependent on a number of factors, namely, nanoparticle size, material type, shape and volume fraction. Different types of material are quantified by the complex refractive index, which is a combination of the refractive and absorptive indexes :(2)snp=nThe effects of particle shape are complicated and difficult to quantify. Currently there is no reliable theory that can be used to describe the impact of varying shape of nanoparticles . ReliablTo account for the absorption and scattering of spheres, the Mie theory is used ,37. WhilThe extinction, scattering and radar backscattering efficiencies for Rayleigh scattering are given in Equations (5)–(7), respectively :(5)Qext=If ly unity , therefoFurthermore, if ion (10) : (10)QababsQ and scatQ that α4 << α. This is due to nanoparticles being significantly smaller than the wavelengths, meaning that α < 1 resulting in α4 << α; therefore scattering effects can be ignored [Additionally the extinction efficiency can be reduced to just the absorbing as it can be seen in the above equations when comparing  ignored . This si ignored ,36. In a ignored :(11)IsI0The extinction coefficient of the nanoparticles can then be represented as follows:Under the assumption that no aggregation occurs the extinction coefficient becomes:These equations assume that the base fluid is completely transparent, while this is desirable, in reality it is not the case. To account for this, the total extinction coefficient is proposed as the addition of the particles and the base fluid’s extinction coefficient :(14)κ=κnAs the incident radiation in question is concentrated solar radiation, it can be approximated using Planck’s black body distribution:In the spectrum of the solar radiation outside of Earth’s atmosphere, 96.3% of the total irradiance is in the range of 200–2500 nm, and this range will be considered in this study.Two physics modules are incorporated to take into account both the heat transfer and the fluid dynamics of the receiver. The heat transfer uses the following governing equation:The fully developed velocity profile is determined taking into account particles being suspended in the fluid. The governing equations for the mixture model are listed below:In a number of previous studies plug flow was used in place of fully developed flow ,28,39, uTo simplify the model, it is assumed that there is no temperature difference between the particles and the base fluid because the high surface to volume ratio of the nanoparticles leads to instantaneous heat transfer between the two mediums .The absorbed thermal radiation is modelled as a volumetric heat release assuming that the change in normally incident spectral flux due to attenuation by the nanofluid is dissipated as a local heat release. The heat release function is determined from the radiative transfer equation :(27)cosθy is the distance from the top of the receiver. Integrating both sides yields:Given that the solar radiation is normally incident, scattering is negligible and the radiative transfer equation is in its quasi-steady form of the heat release function is defined as Equation (28) . The quaThe top surface of the receiver consists of two types of heat losses, convective loss and radiative loss. For the heat losses to be combined, the radiative loss needs to be approximated as a linear heat loss coefficient, so that the two heat loss coefficients can be summed together. The radiative linear heat loss coefficient is a good approximation for low temperatures but loses the accuracy for high temperatures (T > 750 K) . As thisThe convective heat loss is quantified using an overall heat transfer coefficient that takes into account convection between the nanofluid and the top surface as well as conduction through the receiver top plate:−6–1.354 × 10−5 [Fused quartz was chosen as the material for the top surface as it has a relatively low thermal conductivity of 1.3 (W/mK) and stability at high temperatures with an annealing point of 1140 °C. Fused quartz also possesses an extremely low coefficient of thermal expansion, reducing the effects of thermal shock and most importantly, it is virtually transparent in the solar range with an absorptive index ranging from 1.72 × 104 × 10−5 . An arbiThe average Nusselt number is given as follows:The bottom surface of the receiver is considered to be an adiabatic black wall to the incident radiation, so any radiation that reaches the surface is completely absorbed and converted to heat. The absorbed heat is modelled as a heat flux by substituting the receiver height for Finally, the receiver efficiency is defined as the ratio of usable thermal energy to the incident solar energy:Converting the receiver efficiency equation to 2D gives the following equation:The solution method of the governing equations using COMSOL Multiphysics modelling package is shown in The values of the parameters presented in The thermal insulation or adiabatic surface, symmetry and outflow boundary conditions were simulated as:Opaque Surface:The temperature boundary condition was used to simulate the inlet temperature of the receiver:Heat Flux:The grid system used in this study is presented in 3-KNO3) at a mole fraction of 60:40 respectively and is also commonly referred to as solar salt. This molten salt was chosen to be doped with graphite nanoparticles due to graphite’s high absorptivity in the solar range and its common occurrence in literature as a dopant. Moreover, molten salt nanofluid is very well suited to high temperature direct absorption solar collector systems. Very low volume concentrations of nanoparticles are considered (fv < 0.006) in this study and therefore it is assumed that the particles will have negligible effects on the thermophysical properties of the nanofluid. This study considers a combination of sodium nitrate and potassium nitrate salts (NaNOTo increase the accuracy of the model the properties of the nanofluid are modeled as temperature dependent :(44)ρnf=Thermal conductivity of the solar salt is not strongly dependent on the operating temperature as varies between 0.42 and 0.58 W/mK for the temperature range of 600 to 730 K. Therefore, a constant thermal conductivity of 0.45 W/mK is found to be a good choice, showing a 10.12% deviation from the average :(47)knf=The operational temperature range of the solar salt lies between its melting point of 495 K and boiling point of 873 K . Since, In order to validate this model, it is used to re-create the results of a similar validated model from literature. The model that was re-created was that of Veeraragavan et al. , who prePe = Peclet number.To determine the effect that the receiver length has on the overall efficiency, a parametric sweep of the length was conducted. The sweep ranged from 0.01 m to 0.32 m with a step of 0.01 m was considered. This range equates to a dimensionless length range of 0.123 to 3.94 which is the same as that from the literature. This is determined from equation which defines the dimensionless receiver length:The performance of the receiver is evaluated by three factors: the receiver efficiency, the Carnot efficiency and the total efficiency and compared with those in the literature as shown in Due to the acceptable level of agreement between results of current study and Veeraragavan’s model , this moThe influence of the parameters like receiver length, receiver height, inlet velocity, solar concentration and volume fraction w investigated. The volume fraction was set at 0.00001, height of the receiver was 0.05 m, solar concentration was 10×, ambient temperature was assumed at 297 K and the inlet velocity was set at 0.0001 m/s. To determine the overall power plant efficiency the product of the receiver and power generation system efficiencies are considered.This total efficiency represents the maximum achievable efficiency that such a power plant system can theoretically reach. The receiver length was varied between 0.0595 m and 1m for the parameters previously stated and the results are shown in 2K [It can be seen from the results that as the receiver length is increased the receiver efficiency decreases while the Carnot efficiency increases, resulting in a decreasing total efficiency. The Carnot efficiency increases as the longer the receiver is, the longer the heat transfer fluid is exposed to the solar radiation and the greater amount of radiation it is exposed to, resulting in a higher outlet temperature. However, the longer the receiver is the larger the surface area and the hotter the fluid is resulting in greater losses. The plot shows that there does not exist a length where optimal total efficiency is achieved. This result is in contrast with the findings of Veeraragavan et al.  who show2K  and the This implies that for high temperature receivers an optimal length cannot be determined from the overall system efficiency. Instead, other factors need to be considered to determine the optimal length. Effect of receiver height on the efficiencies for a certain length and fixed parameters of the HTF were investigated as shown in As the depth of the HTF increases with the height of the receiver, larger portion of the incident radiation is absorbed by the bottom surface of the receiver than that of the HTF at shallow depth. This the phenomenon alters gradually with the increase of the receiver height. Therefore, peak Carnot efficiency is achieved at a very low receiver height, while the receiver efficiency is still increasing to a moderate receiver height before levelling off. For total efficiency there is an optimum height for a certain receiver length. From Setting the previously defined lengths and their associated optimal heights, the effect of HTF inlet velocity was then investigated. To further analyse the effect that the fluid velocity has on the receiver, the dominating effects of surface to ambient radiation need to be considered. To do this the exposure time of the heat transfer fluid is to be kept constant and the inlet velocity varied. This is done by increasing the receiver length so that in all cases the fluid is exposed to the radiation for the same amount of time, arbitrarily chosen as 2500 s. Four cases were considered, a length of 0.25 m at inlet velocity of 0.0001 m/s, length of 0.5 m at inlet velocity of 0.0002 m/s, length of 1m at inlet velocity of 0.0004 m/s and a length of 5 m at inlet velocity of 0.002 m/s. Setting the volume fraction to 0.00001, the inlet velocity to 0.0001 and the receiver height to 0.0908 m, the solar concentration was then varied for different receiver lengths. The solar concentration was varied from 10×–25× for different receiver lengths as illustrated in Given that the adjusted Carnot efficiency gives rise to an optimal efficiency for the volume fraction, the receiver length was re-investigated with the adjusted Carnot efficiency as shown in It can be seen from the plot that a peak in the adjusted efficiency occurs for the receiver length, implying that there exists a length that provides the best trade-off of receiver efficiency for average temperature rise. It can also be seen that the trade-off becomes more pronounced and the optimal receiver length decreases with an increase in solar concentration. Therefore, to achieve the best balance between the receiver efficiency and the temperature rise, short receivers with high solar concentrations are desirable. From this model the suggested optimal design for a solar salt and graphite nanofluid receiver is that consisting of a solar concentration of 25×, volume fraction of 0.00001, inlet velocity 0.0001 m/s, receiver length of 0.406 m and a receiver height of 0.0908 m. These parameters result in an average outlet temperature of 835 K, peak temperature of 870 K, receiver efficiency of 43.9% and a total system efficiency of 28.3%It should be noted that the model begins to break down at higher concentrations and longer receiver lengths because the viscosity equation results in a negative value due to the high temperatures in those situations. As solar salt begins to break down at 873 K, the maximum temperature should also be considered in the model. To investigate even higher solar concentrations without the viscosity equation breaking down, the inlet velocity is increased while keeping the receiver length constant, allowing for suitable temperatures to be achieved for high concentrations. The effect that solar concentration has on the total efficiency of the system is illustrated in However, this plot does not show the peak temperature of the nanofluid. This is shown in Considering the adjusted Carnot efficiency, To further investigate the effects of solar concentration, it was varied for each volume fraction and the inlet velocities adjusted such that the peak temperature of the receiver equaled that of solar salt’s upper temperature limit. For a volume fraction of 0.0005 the total efficiency and the adjusted efficiency with respect to solar concentration are depicted in The same simulations were conducted for different volume fractions and the results are illustrated in 3-KNO3, also commonly known as solar salt base fluids doped with graphite. A 2D CFD model is developed to investigate the effects of design and operating parameters on the receiver efficiency, temperature rise and system efficiency. Parameters taken into consideration included receiver length, inlet velocity and solar concentration. The model has been evaluated using three types of heat losses from the nanofluid: surface to ambient radiation loss, convective loss and re-emission loss. Nanofluid volumetric flow receivers have been studied for high-temperature concentrating solar thermal applications using sodium nitrate + potassium nitrate salts, NaNOThe model shows that the receiver efficiency increases with increasing solar concentration, decreasing receiver length and decreasing nanoparticle volume fraction and, by extension, increasing receiver height. It also showed that the temperature rise across the receiver increases with an increase in receiver length, decrease in inlet velocity and an increase in solar concentration. When the receiver is connected to a power generation cycle, the total system efficiency is found to be in excess of 40% when solar concentrations are greater than 100×. The reason behind the high concentration receivers resulting in a higher efficiency is that the increase in receiver efficiency outweighs the decrease in Carnot efficiency, as the receiver efficiency is found to exceed 90% in certain cases. It was also revealed that an optimal receiver length exists which increases with increasing solar concentration, and is also dependent on the inlet velocity. In addition, adjusted total efficiency resulted in a peak for solar concentration, which decreased with decreasing volume fraction, implying that each receiver configuration has an optimal solar concentration. This study provides a comprehensive model of a direct absorption high temperature nanofluid solar receiver which can be used to determine an optimal receiver design given the operating conditions of the system.


The detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep.A resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations.Ovis aries (OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits.PCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content on PCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information. The identification of genomic regions that affect traits of interest and the application of marker- or gene-assisted selection  in livesNevertheless, the discovery of thousands of single-nucleotide markers (SNPs) and cost-effective tools (SNP arrays) to genotype them on a large number of animals as well as the recent availability of affordable whole-genome sequencing techniques, has opened new perspectives. It is expected that polymorphisms with small effects that collectively explain an increasing amount of the genetic variance may be gradually identified by using denser molecular marker maps on larger structured resource populations . Thus, iAmong the available QTL detection approaches, those based on the combined use of linkage disequilibrium (LD) and linkage analysis (LA) information (LDLA) have been indicated as the most powerful, robust and precise in populations that are structured in families , 25, 26.An alternative way to process IBD information is principal component analysis (PCA), which has the desirable feature of collapsing information that is contained in a set of correlated variables by a smaller set of orthogonal variables. As such, PCA has been proposed as a technique to reduce the dimensionality of predictors in genomic selection , 38.The aim of this study was to detect genomic regions that affect milk traits in the resource population of Sarda sheep by applying an LDLA approach combined with PCA to overcome computational issues of the IBD matrix and minimize the loss of information.bloodlines represented in the RP. Sarda sires were always chosen based on their genetic impact on the registered population among rams belonging to the artificial insemination centre of the breed.The generation of the resource population (RP) started in 1999 when 10 Lacaune × Sarda F1 sires were mated to Sarda ewes to produce 928 back-cross female lambs in the framework of an European project aimed at detecting QTL in the main European sheep breeds . Subsequently, we focused on the detection of QTL segregating in the pure Sarda breed, and since 2002 we used exclusively Sarda rams (SA) to produce the yearly replacement of RP. Until 2009, the average size of the sire families was 43 daughters whereas, from 2010 onward the average size of families decreased to nine daughters, in order to increase the number of Sarda In total, 3949 ewes from 161 rams (10 F1 and 151 SA) were generated until 2015. Ewes of RP were kept until the 4th  lactation on an experimental farm. The farming system was similar to that commonly applied in Sardinia with most of the adult ewes lambing in autumn and yearlings lambing between January and March. The ewes were milked twice a day by machine from weaning (3–4 weeks from lambing) until the end of July. The feeding regime was based on controlled grazing, supplemented by hay and concentrates in winter and late spring.All the ewes of RP and their sires as well as the 10 Lacaune sires of F1 and 11 Sarda sires of SA were genotyped with the Illumina Inc. OvineSNP50 Beadchip. SNP editing was performed using call rate and MAF thresholds of 95% and 1%, respectively. The ovine genome assembly v4.0 and the software SNPchimMpv.3  were useA large range of traits of economic interest was measured in the RP. In the current study, we focused on milk traits: milk yield (MY); fat yield (FY); protein yield (PY); fat content (FP) and protein content (PP). MY, FP and PP were measured twice a month during the milking period at the am and pm milking. Lactation records were computed by the Fleischmann method, using records from the milking period only (in agreement with ICAR recommendations), by considering an initial suckling period of 30 days. Finally, 13,059 lactations of 3731 ewes recorded from 2000 to 2017 were retained. The average number of records per ewe was 3.5 ± 1.02, ranging from 1  to 5 ; most of the ewes (55.5%) had four records.First, in order to adjust for the main environmental effects, raw lactation records were analysed with single-trait repeatability animal models using the ASReml 4.1 software . GeneticBy “gamete”, we refer to the whole haploid set of autosomes that are inherited by an individual from one of the two parents. Moreover, we classified the gametes of the population as base haplotypes (BH) when inherited from an ungenotyped parent and replicated haplotypes (RH) when inherited from a genotyped parent. The pool of BH comprised both gametes of F1 rams and of 63 SA rams, the maternal or paternal gametes of the 35 ewes with an unknown sire or dam, respectively, and the maternal gametes of the 928 back-cross ewes and of 85 SA rams for which the sire was genotyped. Finally, 1207 gametes were classified as BH, i.e. the 10 F1 sires paternal Lacaune gametes  in addition to that from the population-wide linkage disequilibrium (LD) to estimate IBD probabilities.First, IBD probabilities between BH and RH were calculated by LA   and IBD Grossman . The addc method , the max purpose –36, 48 w purpose . Second,r et al. , 50, whiThus, at each SNP position Below, we explain how the PC scores of the As far as the The Note that when t}Vl see . When \dSince the mentclass2pt{minimAs far as the entclass1pt{minimaIn Eq. , \documeNote that mentclass2pt{minimIn addition, in accordance with The model was tested at each SNP position by F-tests. Three null hypotheses were tested, Genome-wide (GW) significance thresholds were determined by 2000 within-trait permutations of the residuals  lower than 0.01. Only SNPs located on the 26 autosomes were retained.Ovis aries (OAR) chromosome 21.The phasing procedure allowed the reconstruction of the sequence of the alleles carried by the investigated BH and RH for more than 99.5% of the SNP positions. After phasing, another 120 SNPs were excluded, because their genotypes were inconsistent with the phase estimated from neighbouring SNPs. Finally, 43,390 SNPs were retained for further analyses. The explored genomic portion was 2437 Mb long and the average distance between SNPs was 56 ± 49 kb with a maximum gap of 2.377 Mb on On average, the maximum locus-wide The distribution of the genome-wide number of replicates of the 1207 BH in RP  was observed at 12.34 Mb on OAR4. This QTLR affected also PP. The most significant results for the three yield traits  were detected at 55.43 Mb on OAR11 where a significant peak for FP was also identified . As described above, the resource population investigated here is constituted by families based on male ancestors. LDLA mapping approaches are expected to be more suitable than linkage and genome-wide association analyses to fine map QTL regions in such populations. In fact, the LDLA analysis combines both the within-family linkage and population-wide linkage disequilibrium information to estimate IBD probabilities between haplotypes .The proposed approach allowed us to solve the model by the least square method, which avoid a computational expensive variance component analysis. The advantages of approaches based on LDLA regression versus those on variance components, in term of ease of use and computing time, are well known  and haveIn our study, LDLA mapping greatly benefits from the structure of the population, in which the ewes born after the first generation have both parents genotyped, which allows a precise reconstruction of the base gametes of the population and their transmission through generations. IBD information can be efficiently captured with PCA and the computational constraints due to the multi-collinearity generated by the high timation , 34.Moreover the strategy used here to collapse IBD information into principal components , which is mapped to OAR6 within the 85.00–85.23 Mb interval. This interval overlaps with the position of the strongest signal for PP found in this study. At almost the same position, a QTL for PP was detected in Churra sheep by GWA ; -log10 negative logarithm of the p-value corresponding to the null hypothesis that the effects of principal components that explain 99% of the variability due to the Sarda base gametes (BHS) are zero; MY milk yield; FY fat yield; PY protein yield; FP fat content; PP protein content.Additional file 4. Application of the proposed method to the XVI QTLMAS simulated population data and comparison to LDLA results presented by Garcia-Gamez et al. [10 corresponding to the null hypothesis that the effects of principal components that explain 99% of the variability due to base gametes of the XVI QTLMAS simulated population at each locus  are zero. The dashed black lines indicate the 0.05 genome-wide significance threshold determined by Bonferroni correction for all the tests . Orange diamonds and grey vertical lines indicate the location of true simulated QTL [z et al. . Manhattated QTL . Green tated QTL .


Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations.Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip  to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1–4, 6–7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens.Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this FThe use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs. Poultry breeding programs were developed to increase growth rate, body condition (performance traits) and improve carcass traits in broiler chickens , 2. Neve2 Chicken Resource Population) for feeding, growth and carcass traits [The Chicken QTL database (release 35)  hosts 75s traits , 9. Howes traits  were 5.4CCKAR gene associated with daily feed intake and daily gain. Mebratie et al. [Recent studies have identified QTL, candidate genes and mutations associated with performance traits in chickens , 10, 11.e et al.  identifie et al. , therebye et al. , 13.2 Chicken Resource Population was performed using 134,528 SNPs generated from a genotyping by sequencing (GBS) approach that used the restriction endonuclease PstI [Recently, GWAS for performance traits in Embrapa Fase PstI . Althoug2 Chicken Resource population that was derived from a reciprocal cross between a broiler and a layer line. In addition, selection signature regions and a SNP dataset derived from re-sequencing of grandparental individuals were integrated to refine the list of candidate genes and the search for potential causative mutations.The aims of this study were to estimate the genomic heritability for performance traits, and to identify genomic regions and positional candidate genes associated with these traits in a Brazilian FThe number of animals, means and standard errors, variance components, and estimated genomic heritabilities are given in Table 1 birds did not have phenotypic data and were not considered for GWAS. A total of 489 F2 chickens from seven different families were used in the association analysis.As described by Moreira et al. , from thFrom the 580,961 SNPs on the SNP chip array, 399,693 segregating SNPs were kept for further analyses. All these SNPs had a call rate ≥ 98%. Among these, 4304 were removed due to minor allele frequency (MAF) criteria (MAF ≤ 0.02), and 23,603 SNPs that were located on the sex chromosomes and linkage groups were also removed, such that 371,558 markers remained for GWAS. An average density of 541 SNPs/Mb per chromosome was observed, with the lowest density on GGA2 (297 SNPs/Mb) and the highest density on GGA21 (816 SNPs/Mb). Missing genotypes were replaced with the average covariate value of that locus as reported by Cesar et al. .The characterization of the 943 1-Mb non-overlapped windows and their respective percentage of the genetic variance explained are available in Additional file The associated SNP windows cumulatively explained 2.12, 20.59, and 17.31% of the genetic variance for BW1, BW35 and BW41, respectively. Manhattan plots with the percentage of genetic variance explained by all 943 non-overlapped SNP windows for each trait analyzed herein are shown in Fig. From the 20 unique genomic windows (Table From the 20 unique genomic windows (Table 2 Chicken Resource Population) [From the 20 unique genomic windows associated with body weight traits (Table ulation)   and also, no enriched clusters of genes were detected. Thus, positional candidate genes within each genomic window were evaluated. From the 386 genes annotated within the associated genomic windows  were annotated in 11 positional candidate genes Table , integra2 population that exhibits high phenotypic and genotypic variability, and were adjusted using BW35 as a covariate. The short interval between the two measurements could explain the low genomic heritability estimates observed [The genomic heritability estimates ranged from 0.0002 to 0.13 , Pértille et al. [In contrast, this study was performed using an Fe et al.  reportedThe genetic variance explained by each SNP window associated with body weight traits ranged from 0.53 to 4.74%, with windows located on GGA1–4, 6, 7, 10, 14, 24, 27 and 28. Ninety-five percent (19 out of 20) of the genomic windows identified overlapped with at least two known QTL for body weight traits  [From the 20 unique genomic windows associated with body weight traits (Table ulation)    and their receptors may promote cell differentiation in various tissues, such as skeletal muscle in mammals.Within the associated genomic windows, 38 genes were selected as candidates for body weight regulation in chickens, based on their respective biological GO terms and literature information Table . Three oLCORL , 27, PPAPPARGC1A , 29 and  [CHRNA9 . Fifteen3, HIPK2 , KLB [322 [, KLB  and PHB KLB [PHB , 36); em [TXNRD1 , IGF-1R R [RBM47  and VEGF7 [VEGFA , 41) or A [LONP1 , GRIN2A , GRIN2A  and BMPR [BMPR1A ). It is elopment  and stemelopment . In addiHSP90AB1. The HSP90AB1 gene encodes a heat shock protein (HPS), which was associated with the response to heat stress in cattle [HSP90AB1 gene can affect performance traits and, consequently, body weight in chickens.One positional candidate gene was associated with heat stress: n cattle , 47 and n cattle . Interesn cattle , 50, corACVR1, ACVR1C and ACVR2A. The ACVR1 gene is associated with ossification and its expression can inhibit osteogenesis [ACVR1C gene was associated with adiposity and body weight in mice [ACVR2A was associated with breast and carcass weight in chickens [ACVR2A gene overlapped with one signature selection region previously identified in the founders of the Embrapa F2 Chicken Resource Population [Three actin A receptor types were identified as positional candidate genes: ogenesis , 52, pot in mice . The ACVchickens . Additiopulation , which iBreeders have long been selecting chickens for rapid growth, body weight gain, feed efficiency and breast muscle weight –57. SeleSLC41A2, SLC37A3, SLC25A6, SLC29A1, SLC1A6, SLC35B1, AKR1D1, ANGPTL4 [, RB1 [, CHST11 [, PMCH [, NR4A2 [, HTR2A [, KCNJ5 and KCNJ1. It is important to highlight that SLC41A2, SLC37A3, SLC25A6, SLC29A1, SLC1A6 and SLC35B1 belong to the solute carrier family already known to be associated with energy metabolism [AKR1D1 gene is involved with bile acid and steroid hormone homeostasis [KCNJ5 and KCNJ1 genes belong to the potassium channel family, that may affect food intake, energy expenditure and glucose homeostasis [Fifteen positional candidate genes were associated with adipose tissue development, energy and lipid metabolism:  ANGPTL4 , RB1 [594 [, RB1 , CHST11 , CHST11 , PMCH [6 [, PMCH , NR4A2 [[, NR4A2 , HTR2A [[, HTR2A , KCNJ5 atabolism , 65–67 atabolism , 69. Theeostasis  and, inteostasis . The KCNeostasis  and, conCHST11, RB1, HTR2A, NR4A2, ANGPTL4 and SLC1A6, suggesting that these genes may have pleiotropic effects. Those regions associated with body weight and fatness traits may help to explain why selection for weight gain is associated with increased fat deposition. Moreover, CHST11 and NR4A2 genes overlapped with a signature selection region previously identified in the founders of the Embrapa F2 Chicken Resource Population [From all the positional candidate genes identified, six were located within two QTL previously mapped for fatness traits  Table 44, and wepulation . These rNR4A2 gene overlapped with a selection signature region, indicating that this gene is under positive selection, may affecting lipid metabolism in one of the founder lines and, consequently, body weight. Thus, predicted deleterious and high impact SNPs in these genes could be causative mutations.Thirteen predicted deleterious and three high impact SNPs were identified in 11 positional candidate genes from our gene list Table . As prevIn summary, we identified 20 unique 1-Mb genomic windows associated with body weight traits  and within them, we detected 38 positional candidate genes. Through our integrative approach, we refined our list of candidate genes investigating the overlap between sequence SNPs and signatures of selection detected in the founders of the population. Curiously, three positional candidate genes overlapped with regions exhibiting selection signatures. In addition, thirteen predicted deleterious and three high impact SNPs were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. Further functional studies need to be performed to validate the role of these mutations in body weight regulation, thus providing important information for poultry breeding.2 population and the integration of regions exhibiting signatures of selection in their pure line ancestors along with sequence SNPs detected in pure line grandparents allowed the identification of candidate genes and candidate causal variants within those genes. Annotation of candidate genes indicates the importance of osteogenesis, cell growth and differenciation, skeletal muscle development, energy metabolism and lipid metabolism in the control of growth and, consequently, body weight in chicken. Our findings form a basis for further functional studies that can elucidate the role of specific genes in body weight regulation in chickens, generating useful information for poultry breeding programs.The use of a high-density SNP array to identify QTL in an FAll experimental protocols related to animal experimentation in this study were performed in agreement with resolution number 010/2012 approved by the Embrapa Swine and Poultry National Research Center Ethics Committee on Animal Utilization to ensure compliance with international guidelines for animal welfare.We used the same population described in Moreira et al. . In addi2 Chicken Resource Population  were genotyped  [In summary, 529 chickens from an Embrapa Frations)  with a hrations) .2 population being created [2 Chicken Resource Population are described by Nones et al. [Breifly, the layer line (CC) was selected for eight generations for improved egg production, egg weight, feed conversion, viability, sexual maturity, fertility, hatchability, egg quality, and low body weight, prior to the F created . The bro created . More des et al.  and Rosás et al. .2 population were reared with free access to water and a corn and soybean meal-based diet up to 42 days of age [Chickens from the Fs of age . As descs of age  and Pérts of age , between− 1. Diluted genomic DNA was prepared for genotyping following an Affymetrix protocol, and then genotyped with a 600 K Affymetrix Axiom Chicken Genotyping Array . That SNP chip contains segregating SNP for different chicken lines as described by Kranis et al. [The DNA extraction, genotyping and quality control have been described in Moreira et al. . Brieflys et al. .http://www.r-project.org/), and SNPs with call rate ≥ 98% and minor allele frequency (MAF) ≥ 2% were kept for further analyses. SNPs monomorphic, located on the sex chromosomes or linkage groups without genomic annotation were removed.Quality control analysis and genotype calling were performed using Affymetrix Power Tools v1.17.0 (APT). Samples that exhibited DishQC ≥0.82 and call rates ≥90% were kept for further analyses. Filtering was performed with the SNPolisher package using R software . Those values were then used to run a BayesB model, to estimate genomic heritability and perform GWAS, as had been adopted by Cesar et al.  and Morey represents a vector of phenotypic values, X an incidence matrix for fixed effects, b the vector of fixed effects, k the number of SNP, aj the column vector representing SNP locus j as a covariate coded with the number of B alleles, βj the random substitution effect for locus j assumed normally distributed N  when δj = 1, with δj being a random indicator variable 0/1, indicating the absence (with probability π) or presence (with probability 1-π) of locus j in the model, and e the vector of the residual effects assumed normally distributed N . Sex and hatch were included as fixed effects in the model and BW35 was fitted as a covariate for FI, FC, FE and WG.In this model, We adopted π = 0.9988 in the BayesB model to fit approximately 445 SNP per iteration of the Markov chain comprising 41,000 MCMC samples with the first 1000 samples being discarded. A map file was used to allocate the markers to each of 943 1-Mb non-overlapping windows. Based on previous studies that adopted genomic prediction methodology to perform GWAS , 80–82, We expect that each window would explain 0.1060% of the genetic variance (100% / 943) in an infinitesimal model as mentioned by Van Goor et al. , and win2 Chicken Resource Population) [All genomic windows detected were compared with published QTL previously mapped to the chicken genome, using the information available at the Chicken QTLdb - release 35 . The seaulation) . Previoup-value < 0.05 and p-value adjusted for multiple testing using the Benjamini & Hochberg [The genes located within every genomic window that had been shown to be associated with a trait, and their corresponding Gene Ontology terms, were retrieved from the Ensembl Genes 92 database available at Ensembl BioMart . A literHochberg  procedurGenes identified in this study were evaluated to determine if they resided within selection signature regions that were previously identified in 28 grandparental chickens (14 TT and 14 CC) that were ancestors of our experimental population . The desAdditionally, we integrated a sequence SNP dataset from re-sequencing these grandparental animals to identify candidate mutations located in our positional candidate genes. In this study, we only investigated SNPs located in coding regions. To predict whether SNPs that cause changes in amino acids may affect the function of the gene product, we utilized the SIFT (sorting intolerant from tolerant) score to assess the level of conservation in homologous protein sequences using the SIFT algorithm  implemenAdditional file 1. The characterization of all the 943 1-Mb SNP windows analyzed including the percentage of the genetic variance explained by each one.Additional file 2. – Genomic windows, associated traits and published overlapping QTL for body weight traits, available at the Chicken QTL database (release 35). The underlined published QTL were previously mapped with microsatellite markers for the same traits in the same population (Embrapa F2 Chicken Resource Population).Additional file 3. List with the Ensembl Gene ID and their respective gene names of the 386 genes annotated within the QTL detected.


ST) scores were used to track the majority of significant QTL and to reduce the dimensionality of the association model.It becomes clear that the increase in the density of marker panels and even the use of sequence data didn’t result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power. Even when the variant effects are not directly estimated using VC based approaches, the genomic relationships didn’t improve after the marker density exceeded a certain threshold. SNP prioritization-based fixation index (FG) was calculated for each set of selected SNPs based on their FST score and similar numbers of SNPs were selected randomly for comparison. Using all 400 K SNPs, 46% of the off-diagonal elements (OD) were between − 0.01 and 0.01. The same portion was 31, 23 and 16% when 80 K, 40 K and 20 K SNPs were selected based on FST scores. For randomly selected 20 K SNP subsets, around 33% of the OD fell within the same range. Genomic similarity computed using SNPs selected based on FST scores was always higher than using the same number of SNPs selected randomly. Maximum accuracies of 0.741 and 0.828 were achieved when 20 and 10 K SNPs were selected based on FST scores in P1 and P2, respectively.Two populations with average LD between adjacent markers of 0.3 (P1) and 0.7 (P2) were simulated. In both populations, the genomic data consisted of 400 K SNP markers distributed on 10 chromosomes. The density of simulated genomic data mimics roughly 1.2 million SNP markers in the bovine genome. The genomic relationship matrix  contains supplementary material, which is available to authorized users. A large number of polymorphic variation  is being identified across the genome of livestock species. A continuous decrease in the costs of high-throughput genotyping and sequencing techniques has allowed for the generation of a massive amount of genomic information on a large number of individuals. This wealth of genomic information was useful in understanding the association between complex phenotypes and genetic variation with applications in human, plants and livestock species , 19, 29.ST based prioritization resulted in sight superiority compared to BayesB.In livestock and plants, genomic information was mainly used for breeding purposes. In fact, genomic enhanced breeding values (GEBV), which are computed as a linear function of the SNP effects and their associated genotypes, were accurately estimated through the so called genomic selection (GS). The latter is superior to its pedigree-based counterpart due to a better modeling of the Mendelian sampling and a substantial reduction in generation interval. In fact, genomic selection (GS) is becoming the standard tool for genetic evaluation in several livestock and poultry species. However, the continuous increase in the density of marker panels and number of genotyped individuals and the presence of low (rare) frequency variants are posing major challenges to GS. Regression (RM) based approaches model directly the association between the phenotype and variant genotypes. The large number of unknown parameters in the association model and the high LD will undoubtably lead to noticeable shrinkage. Splitting the effect of a QTL between a large number of linked markers will negatively affect the statistical power , 39. ThuG) rather than the expected (A) additive relationship matrix which allows for the correction of erroneous and unreported pedigree information, and a better modeling of Mendelian sampling [An increase in the number of variants does not directly affect the dimensionality of the association model using variance component (VC) based approaches, such as GBLUP or ssBLUP , 16. Howsampling , 8, 18.G. In fact, accuracy obtained using the 777 K SNP panel was not any different from using the 54 K SNP panel [Several studies have clearly shown that an increase in SNP density, after a certain threshold, does not seem to affect the quality of the estimated observed relationship matrix NP panel , 35.G. The algorithm for proven and young animals (APY) method developed by Fragomeni et al. [G is intrinsically data-driven and could result in computational problems. As a data-driven approximation, its performance is not guaranteed with a continuous increase in the number of genotyped animals, which may span several generations and have more complex pedigree structures (inbreeding). As currently implemented, the ss-GBLUP method does not benefit from high density genomic data.These challenges are further exuberated by the added computational costs. For RM approaches, the computational cost increases almost linearly with the increase in the number of genotyped animals. However, that is not the case with the increase in the number of variants which will make the approach almost impossible computationally when using sequence data. Such costs will not be reduced even when methods for variant prioritization  due to the cost of identifying the “relevant” variants. For VC-based approaches, the number of variants will have very little computational costs. However, the latter increases cubically with the number of genotyped animals, complicating the direct inversion of i et al. , 14 to aST) have been frequently applied in RM, little has been done to evaluate the impact of marker prioritization on the estimation of the genomic relationship matrix (G) and the potential impact in GS using VC approaches [G, removing the contribution of non-influential SNPs that could increase or decrease the realized genetic similarity between two individuals, especially for low MAF markers; 2) some prioritization methods  could provide a simple and systematic approach for weighting the contribution of different markers to the estimation of G. For example, this could be accomplished by using the individual marker FST score as a weight factor. In this study, a marker prioritization method presented by Chang et al. [ST score as suggested by Chang et al. [Although prioritization methods based on statistical , external prior information , and population genetics criteria  with average LD between adjacent markers of 0.3 and 0.7, respectively. Gametes were randomly sampled from both male and female gamete pools. To produce a realistic level of LD in population P1, 300 historical generations were generated based on random mating of an initial 8000 animals, increasing to 15,000 animals at generation 305, decreasing to 12,000 animals at generation 1000, and then increasing to 17,000 animals at the last generation. For population P2, the initial 8000 animals were also simulated for 300 generations but followed by an additional 5 generations with 15,000, 5 generations with 12,000, and 5 generations with 17,000 animals. In a second step, 1000 males and 15,000 females were randomly selected from the historical population and used to create the founder population (G0) for P1 and P2. A trait with heritability equal to 0.30 was simulated assuming that all genetic variation was due to the simulated QTL. An additional 7 selection generations (G1-G7) of 15,000 animals each were simulated. Parents were chosen based on their estimated breeding values (EBVs). The replacement rate for males and females was 50 and 20%, respectively. Throughout, one progeny per mating was assumed and the sex ratio of progeny was set to 50%. The average effective population size ranged between 323 and 350 for P1 and P2, respectively. The sixth generation (G6) was considered as the training population and the last generation (G7) was used to evaluate  our proposed method.Data was simulated to mimic high-density marker panels using the QMSim simulation software . First, 1 and P2 were similar with the exception that P2 had higher LD between adjacent markers. The residual variance was scaled accordingly in each scenario of selected SNPs such that the heritability and phenotypic variance were constant at the values of 0.3 and 1, respectively. Trait phenotypes were generated as the sum of an overall mean, the random additive effects of QTL and their associated genotypes, and the residual terms. The later were sampled from a normal distribution with zero mean and variance-covariance matrices Iσe2, where σe2 is the residual variance.In both populations, only animals in the training and validation populations were genotyped. Genotypes were simulated for 400 K biallelic SNP markers uniformly-distributed along 10 chromosomes of 100 cM in length each to roughly mimic 1.2 million SNP markers in the bovine genome. Two hundred biallelic QTL were sampled from a Gamma distribution with shape parameter equal to 0. 4. No overlap between SNP markers and QTL was allowed. Additionally, QTL were assumed not to be genotyped. In general, the genotype structures for PA real dataset consisting of weaning weight (WW) records of 3012 animals from a composite beef cattle breed born between 2002 and 2011 at the USDA-ARS, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT , 28 was These animals were genotyped with a mixture of different density SNP commercial arrays. Only SNPs with call rate greater than 0.90, minor allele frequency (MAF) greater than 0.05, and heterozygous deviation smaller than 15% from Hardy-Weinberg Equilibrium (HWE) were kept. Animals with call rate less than 0.90 were discarded. Animals genotyped with low-density panels were imputed to the 50 K SNP array using FImpute software . The samST in particular, have been used to measure the level of variation among subpopulations with respect to the variation in the total population. FST measures genetic differentiation through the change in allele frequencies among groups. The greater the divergence between subpopulation, the larger are the FST scores. In this study, FST scores were calculated following the estimators presented by Nei [6) were grouped into three sub-populations  based on the distribution of their phenotypes. Genotypes of individuals in sub-populations S1 and S2 (1500) were used to calculate the FST scores. For each locus, the global FST estimator was defined as:Wright’s fixation indexes, Fd by Nei  and Chand by Nei . BrieflypSi and qSi are the allele frequencies in subpopulation i, ns1 and ns2 are the number of individuals of the subpopulations, HS is the average of sub-population heterozygosities and HT is the heterozygosity based on the total population.where, G, as most of the animals will have the same genotype (homozygous major). Using prioritized SNPs, it is likely to result in an increase in genomic similarity between individuals of similar genetic values or phenotypes. This is the case as individuals with dissimilar genetic values or phenotypes are likely to have much lower genomic sharing. Genomic similarity between two individuals (i and j) was calculated as:Sk is the number of IBS shared alleles between individuals i and j at locus k. Genetic similarity was computed based on all SNPs in the panel and subsets of 2.5, 5, 10, 20, 40, 80 and 160 K markers selected either based on FST scores or at random.Historically, genetic similarity between individuals is measured by their average expected additive relationships derived from pedigrees. With the availability of genetic markers, SNP panels with reasonable density provide an alternative tool to estimate genetic similarity based on realized relationships or other measurements. Currently, genomic relationships are calculated based on identity by state (IBS) between alleles of SNP markers . It basi1 and P2), 10,000 and 5000 animals were randomly selected from G6 and G7, respectively. For each population, several data sets with different number of SNPs (from 10 to 400 K) selected either using FST scores or at random were generated. Data was analyzed using the following mixed linear model:For both simulated populations  approach with the Average-Information algorithm , was useST scores of the 400 K SNPS are shown in Fig. 1). Subsequently, the distribution of simulated QTL across the 10 chromosomes based on their FST score for population P2 (LD = 0.7) for the top 10 K and 5 K selected SNPs, are represented in Fig. G and A when selected subsets of SNPs are used is shown in Table G are presented in Table 1 and P2, respectively . The results of the analysis of real data under different numbers of selected SNPs are presented in Table The distribution and effects of simulated 200 QTL are presented in Fig. ST scores. In fact, there was an almost perfect overlap between the peaks in Fig. ST scores). This overlap persists even for QTL with moderate to small effects indicating the ability of FST scores to track the distribution and effects of the majority of simulated QTL. Obviously, the ability to track QTL using FST scores depends primarily on the heritability of the trait, the genetic variance explained by the QTL, the population structure, and LD between markers and QTL and among markers. For population P2, 50, 27 and 21% of QTL explained less than 0.1, between 1 and 0.1 and greater than 1% of genetic variance each. Similar percentages were observed for population P1. Although this distribution of QTL effects is unlikely in human populations, it is not unexpected in highly selected plant and animal populations. From the distribution of simulated QTL across the 10 chromosomes  than when markers are preselected based on FST scores. This is clearly shown in Table G is intrinsically weighted by its MAF, not the magnitude of its effect. Thus, after a certain threshold on the number of SNP markers is reached, little to no improvement is expected in G and ultimately in the performance of the association model with additional markers. The limited change in G with additional markers could be an indicator of the sufficiency of available SNPs in estimating the realized relationships. However, such sufficiency is not a guarantee of the optimality of the matrix for the implementation of association analyses. In fact, as the number of randomly selected SNPs increased from 20 K to 400 K, the matrix G gets closer to the expected additive relationship matrix (A) as indicated in Table G computed based on a selected subset of 20 K markers is markedly different from A, especially in the tails of the distribution of off-diagonal elements indicating higher genetic similarity between individuals .When constructed from a sufficiently large number of randomly selected markers, the ividuals , 10. Howals Fig. . More imG for both populations P1 and P2 (Table 1), less than half of the genetic variance was recovered when G was estimated based on 2.5 K SNPs selected either randomly or using FST scores. The percentage increased steadily to reach a maximum when all 400 K SNP markers were used, at which point over 83% of the genetic variance was recovered. The inability to recover all the genetic variance in this case is due to the large number of QTL with very small effects. In fact, 55% of QTL have a true effect smaller than one tenth of 1 % and an additional 20% of QTL have an effect smaller than 0.5% of the total genetic variance. These small QTL are hard to track effectively when the LD is moderate to low. Although the general trend was similar when LD was set equal to 0.7 (population P2), the percentage of genetic variance explained for a given number of SNPs was in general higher than in P1  when all 400 K SNPs were used to compute G. For the random selection scenario, there was an over-estimation of the residual variance, except for the case when 160 K SNPs were used  and under-estimated when the number of markers exceed 40 K. In between these two numbers of selected SNPs, the residual variance was precisely estimated , accuracy increased with the increase of the number of selected SNPs under both 0.3 and 0.7 LD scenarios. Further, accuracy was always smaller than when all 400 K SNPs were used  was equal to 0.27. When 2.5, 5, or 20 K SNPs were randomly selected, accuracy ranged between 0.25 and 0.27  to further enhance the accuracy of genomic selection.High-density SNP panels and whole genome sequence data were expected to increase the accuracy of genomic selection in livestock. However, because of the limitations of current methods used for implementation, an increase in genomic data did not result in any significant improvement of accuracy. The dramatic increase in the dimensionality of the association models led to an over-parameterization problem, such as increased co-linearity and lack of statistical power. FAdditional file 1:Figure S1. Effects and distribution of the 200 simulated quantitative trait loci (QTL) along the ten chromosomes (a) and their associated FST scores distribution (b) when the LD between adjacent markers was equal to 0.3 (DOCX 1094 kb)