Linkage Phase Research Articles

The Coordination of Aluminum Sulfate with a Water-Soluble Block Copolymer Containing Carboxyl, Amide, Sulfonic and Anhydride Groups Providing Both Accelerating and Hardening Effects in Cement Setting

Two water-soluble block copolymers composed of acrylic acid (AA), 2-acrylamido-2-methylpropane sulfonic acid (AMPS), and optionally maleic anhydride (MAH) were synthesized through ammonium persulfate-catalyzed free radical polymerization in water. The introduction of aluminum sulfate (AS) into the resulting mixtures significantly reduced the setting times of the paste and enhanced the mechanical strength of the mortar compared to both the additive-free control and experiments facilitated solely by pure AS. This improvement was primarily attributed to the inhibition of rapid Al3+ hydrolysis, which was achieved through coordination of the synthesized block copolymers, along with the formation of newly identified hydrolytic intermediates. Notably, the ternary copolymer (AA–AMPS–MAH) exhibited superior performance compared to that of the binary copolymer (AA–AMPS). In the early stages of cement setting, clusters of ettringite (AFt) were found to be immobilized over newly detected linkage phases, including unusual calcium silicate hydrate and epistilbite. In contrast to the well-documented role of polymers in retarding cement hydration, this study presents a novel approach by providing both accelerating and hardening agents for cement setting, which has significant implications for the future design of cement additives.

A governance framework for facilitating cross-agency data sharing

Linking data across local authorities and health boards has the potential to reduce inequalities and improve outcomes. However, cross-agency data sharing leads to complexities around data ownership, legal conditions for processing, organisational controls and risk assessments to ensure privacy and confidentiality of subjects whilst facilitating research and service improvements. We share a governance framework for cross-agency data sharing between a local authority and regional health board in Scotland. A cross-sector Child Neglect working group was established with representation from public sector agencies, the NHS, University researchers and a Trusted Research Environment to support research on children’s mental health prescribing and secondary specialist services provision and linking to the Aberdeen City Council Child Protection Register. Lessons learned were captured throughout the governance, data transfer and data linkage phases. Communication is the most important driver, with key individuals, deputies and routes of escalation agreed and involved throughout for clarity of roles and expectation setting. Key lessons learned include requiring input from all parties at the project initiation stage, particularly IG teams, including agreements on necessary templates. Knowledge exchange is required between the research team, data owners and data processors to ensure research protocol requirements are correctly understood and data transfer is supported. If possible, internal and/or external validation of data should also be performed before data is linked. Cross-agency data sharing is a vital means to understanding and improving outcomes for individuals and populations. Critical to this is establishing an action plan and formalised project process to achieve outcomes.

Barriers and enablers to people-centred viral hepatitis care in Vietnam and the Philippines: Results of a patient journey mapping study.

In Vietnam and the Philippines, viral hepatitis is the leading cause of cirrhosis and liver cancer. This study aims to understand the barriers and enablers of people receiving care for hepatitis B and C to support both countries' efforts to eliminate viral hepatitis as a public health threat by 2030. Retrospective, semi-structured interviews were conducted with a purposive, quota-based sample of 63 people living with hepatitis B or C in one province of Vietnam and one region of the Philippines. A rapid deductive approach to thematic analysis produced key findings among the three phases of care: (1) pre-awareness and testing, (2) linkage and treatment initiation and (3) ongoing treatment and recovery. The research found that participants followed five typical journeys, from a variety of entry points. Barriers during the pre-awareness and testing phase included limited awareness about hepatitis and its management, stigma and psychological impacts. Enablers included being familiar with the health system and/or patients benefiting from social connections within the health systems. During the linkage and treatment initiation phase, barriers included difficult physical access, complex navigation and inadequate counselling. In this phase, family support emerged as a critical enabler. During the ongoing treatment and recovery phase, the cost of care and socially and culturally informed perceptions of the disease and medication use were both barriers and enablers. Exploring peoples' journeys with hepatitis B and C in Vietnam and the Philippines revealed many similarities despite the different cultural and health system contexts. Insights from this study may help generate a contextualized, people-centred evidence base to inform the design and improvement of primary care services for hepatitis in both research sites.

Genomewide marker estimates of the level of dominance in maize heterosis

AbstractHeterosis is the superiority of an F1 hybrid over its parents, and overdominance and dominance are the two main hypotheses for the genetic basis of heterosis. Our objectives were to determine if genomewide marker effects accurately reflect the level of dominance at quantitative trait loci (QTL) and assess the distribution of per‐marker estimates of the level of dominance in maize (Zea mays L.). We simulated different levels of dominance at QTL that were in coupling, repulsion, and random linkage phases. Genomewide marker effects from ridge regression‐best linear unbiased prediction led to accurate estimates of the level of dominance when all linkages were in coupling phase, but repulsion and random linkage phases led to an upward bias. We then estimated levels of dominance from genomewide marker effects in a reciprocal testcross experiment with 1642 hybrids genotyped with 4805 single nucleotide polymorphism markers, and in a legacy Design III experiment with 351 backcross progenies (subjected to three generations of random mating to dissipate linkages) genotyped with 160 simple sequence repeats. In both experiments, the level of dominance was higher for yield than for moisture. For each trait, some loci showed little or no dominance, others showed partial to complete dominance, and others showed overdominance. A high level of dominance was typically due to a small difference between homozygote values. Overall, the results were consistent with previous QTL mapping results indicating that heterosis in maize is due to varying levels of dominance rather than a prevalent level of dominance.

Female meiotic drive shapes the distribution of rare inversion polymorphisms in Drosophila melanogaster.

In all species, new chromosomal inversions are constantly being formed by spontaneous rearrangement and then stochastically eliminated from natural populations. In Drosophila, when new chromosomal inversions overlap with a pre-existing inversion in the population, their rate of elimination becomes a function of the relative size, position, and linkage phase of the gene rearrangements. These altered dynamics result from complex meiotic behavior wherein overlapping inversions generate asymmetric dyads that cause both meiotic drive/drag and segmental aneuploidy. In this context, patterns in rare inversion polymorphisms of a natural population can be modeled from the fundamental genetic processes of forming asymmetric dyads via crossing-over in meiosis I and preferential segregation from asymmetric dyads in meiosis II. Here, a mathematical model of crossover-dependent female meiotic drive is developed and parameterized with published experimental data from Drosophila melanogaster laboratory constructs. This mechanism is demonstrated to favor smaller, distal inversions and accelerate the elimination of larger, proximal inversions. Simulated sampling experiments indicate that the paracentric inversions directly observed in natural population surveys of Drosophila melanogaster are a biased subset that both maximizes meiotic drive and minimizes the frequency of lethal zygotes caused by this cytogenetic mechanism. Incorporating this form of selection into a population genetic model accurately predicts the shift in relative size, position, and linkage phase for rare inversions found in this species. The model and analysis presented here suggest that this weak form of female meiotic drive is an important process influencing the genomic distribution of rare inversion polymorphisms.

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations.

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

P-708 Establishment of linkage phase, using Oxford Nanopore Technologies, for Preimplantation Genetic Testing of Coffin-Lowry syndrome with a de novo RPS6KA3 mutation

Abstract Study question There was a challenge to establishing haplotype for PGT in the family with de novo mutation because of the lack of information from affected members. Summary answer The ONT platform combined with MARSALA method can be used to perform PGT for DNM patients without the need for other samples as a reference. What is known already The proband was a 31-year-old female who was born with mental retardation and language delay. On physical examination, she was found to have distinctive Coffin-Lowry Syndrome features, such as distance between the eyes and low nasal bridge. However, the proband’s parents did not show any abnormalities. The couple underwent genetic counseling and whole-exome sequencing (WES). The results showed that a frameshift mutation (c.1496delG, p.Gly499Valfs*25) was identified in the RPS6KA3 gene, with a true mutation in the proband but no mutations in the parents. Study design, size, duration A couple requested genetic counseling at the reproductive center of Zhongshan Boai Hospital and underwent whole-exome sequencing (WES) in 2019. The proband then underwent PGT with Oxford Nanopore Technologies (ONT) and the MARSALA platform to block the transmission of the pathogenic mutation to her offspring in 2022. Participants/materials, setting, methods The proband underwent PGT to block the transmission of the pathogenic mutation to her offspring.We used long-read sequencing on the ONT platform to directly detect the mutation and nearby SNPs for construction of the haplotype in PGT. The MARSALA method was used to detect both the SNP-based haplotype and chromosome copy number variations in each blastocyst. Finally, a normal embryo was selected by comparison to the haplotype of the proband and transferred into the uterus. Main results and the role of chance Using WES, we found the novel, heterozygous, pathogenic c.1496delG (p.Gly499Valfs*25) mutation of the RPS6KA3 gene in the proband. The SNP-based haplotype that was linked to the pathogenic mutation site was successfully established in the proband, without the need for other family members to be tested with ONT. Eight blastocysts were biopsied to perform PGT and were assessed with a haplotype linkage analysis (30 SNP sites selected), to give results that were consistent with direct mutation detection using Sanger sequencing. The results of PGT showed that three of the eight blastocysts were normal, without the DNM. Moreover, the patient had a successful pregnancy, after transfer of a normal blastocyst into the uterus, and delivered a healthy baby. Limitations, reasons for caution This strategy has only been applied to one case and more cases with de novo mutaition are needed to prove the feasibility of this strategy for PGT-M. Wider implications of the findings This study is the first report of a de novo RPS6KA3 c.1496delG mutation in patient with CLS, which can expands the RPS6KA3 mutation spectrum and also provides a new strategy based on ONT and MARSALA to perform PGT-M with DNMs without the need for a pedigree sample as a reference. Trial registration number Not applicable

Non-Parametric MCMC Gibbs Sampler Approach and Misclassification Assessment of Estimating Haplotype Frequencies among Related Statistical Approaches

Introduction: Haplotype analysis allows higher resolution analysis in genetic association studies and is used as a reference panel for genotype imputation in genome-wide association studies. Haplotypes estimates from genotypes among unrelated individuals but misclassification of the haplotype reconstruction will directly affect the accuracy of the results.
 Methods: This study proposes a novel statistical method Gibbs sampler algorithm to estimate haplotype frequency and quantify the influence of misclassification bias of the estimate haplotype. The performance of the algorithm is evaluated on simulated datasets assuming that linkage phase unknown. The simulation used different minor allele frequencies at each single nucleotide polymorphism (SNP) and different linkagedisequilibrium between the SNPs.
 Results: The Gibbs sampler algorithm presents higher accuracy among over seven SNPs or less, validated, and deals with missing genotype compared to previous related statistical approaches. Misclassification of estimated haplotypes leads to non-differential bias in exposure and affects haplotype estimates in haplotype analysis. The observed odds ratio underestimates the association between haplotype and phenotype by 36% to 99%.
 Conclusion: The Gibbs sampler algorithm provides higher accuracy and robust effectiveness performance, handles missing genotypes and provides uncertain probabilities of haplotype frequencies. The misclassification bias of the estimate haplotype underestimates the genetic association by more than forty percent.

Establishment of linkage phase, using Oxford Nanopore Technologies, for preimplantation genetic testing of Coffin-Lowry syndrome with a de novo RPS6KA3 mutation.

Background: This study aimed to perform preimplantation genetic testing (PGT) for a female Coffin-Lowry Syndrome (CLS) patient with a de novo mutation (DNM) in RPS6KA3. It was challenging to establish the haplotype in this family because of the lack of information from affected family members. Hence, we explored a new and reliable strategy for the detection of the DNM in PGT, using Oxford Nanopore Technologies (ONT) and the MARSALA platform. Methods: We performed whole-exome sequencing (WES) on the proband and confirmed the pathogenic mutation by Sanger sequencing. The proband then underwent PGT to prevent the transmission of the pathogenic mutation to her offspring. We diverged from the conventional methods and used long-read sequencing (LRS) on the ONT platform to directly detect the mutation and nearby SNPs, for construction of the haplotype in the preclinical phase of PGT. In the clinical phase of embryo diagnosis, the MARSALA method was used to detect both the SNP-based haplotype and chromosome copy number variations (CNVs), in each blastocyst. Finally, a normal embryo was selected by comparison to the haplotype of the proband and transferred into the uterus. Sanger sequencing and karyotyping were performed by amniocentesis, at 17weeks of gestation, to confirm the accuracy of PGT. Results: Using WES, we found the novel, heterozygous, pathogenic c.1496delG (p.Gly499Valfs*25) mutation of RPS6KA3 in the proband. The SNP-based haplotype that was linked to the pathogenic mutation site was successfully established in the proband, without the need for other family members to be tested with ONT. Eight blastocysts were biopsied to perform PGT and were assessed with a haplotype linkage analysis (30 SNP sites selected), to give results that were consistent with direct mutation detection using Sanger sequencing. The results of PGT showed that three of the eight blastocysts were normal, without the DNM. Moreover, the patient had a successful pregnancy, after transfer of a normal blastocyst into the uterus, and delivered a healthy baby. Conclusion: The ONT platform, combined with the MARSALA method, can be used to perform PGT for DNM patients without the need for other samples as a reference.

Covariance between nonrelatives in maize.

The covariance between relatives is a tenet in quantitative genetics, but the covariance between nonrelatives in crops has not been studied. My objective was to determine if a covariance between nonrelatives is present in maize (Zea mays L.). The germplasm comprised 272 maize lines that were previously genotyped with 28,626 single nucleotide polymorphism (SNP) markers. Pairs of unrelated lines were identified on the basis of their membership probabilities in five subpopulations. The covariance between nonrelatives was assessed as the regression of phenotypic similarity on SNP similarity between unrelated lines. Out of 77 regressions, seven were significant at a 5% false discovery rate: anthesis and silking dates in unrelated B73 and Oh43 lines; plant height and ear height in unrelated Oh43 and PH207 lines; oil in unrelated A321 and Mo17 lines; starch in unrelated B73 and PH207 lines; and protein in unrelated B73 and Mo17 lines. The latter covariance was negative, and this negative covariance between nonrelatives was attributed to the subpopulations having different linkage phases between the markers and underlying causal variants. Overall, the results indicated that a covariance between nonrelatives in maize is not ubiquitous but is sometimes present for specific traits and for certain groups of unrelated individuals. I propose that the covariance between nonrelatives and the covariance between relatives be combined into a generalized covariance between individuals, thus giving a unified framework for expressing the resemblance regardless of the degree of relatedness.

Recombination rates in pigs differ between breeds, sexes and individuals, and are associated with the RNF212, SYCP2, PRDM7, MEI1 and MSH4 loci

BackgroundRecombination is a fundamental part of mammalian meiosis that leads to the exchange of large segments of DNA between homologous chromosomes and is therefore an important driver of genetic diversity in populations. In breeding populations, understanding recombination is of particular interest because it can break up unfavourable linkage phases between alleles and produce novel combinations of alleles that could be exploited in selection. In this study, we used dense single nucleotide polymorphism (SNP) genotype data and pedigree information to analyse individual and sex-specific variation and genetic architecture of recombination rates within and between five commercially selected pig breeds.ResultsIn agreement with previous studies, recombination rates were higher in females than in males for all breeds and for all chromosomes, except 1 and 13, for which male rates were slightly higher. Total recombination rate differed between breeds but the pattern of recombination along the chromosomes was well conserved across breeds for the same sex. The autosomal linkage maps spanned a total length of 1731 to 1887 cM for males and of 2231 to 2515 cM for females. Estimates of heritability for individual autosomal crossover count ranged from 0.04 to 0.07 for males and from 0.08 to 0.11 for females. Fourteen genomic regions were found to be associated with individual autosomal crossover count. Of these, four were close to or within candidate genes that have previously been associated with individual recombination rates in pigs and other mammals, namely RNF212, SYCP2 and MSH4. Two of the identified regions included the PRDM7 and MEI1 genes, which are known to be involved in meiosis but have not been previously associated with variation in individual recombination rates.ConclusionsThis study shows that genetic variation in autosomal recombination rate persists in domesticated species under strong selection, with differences between closely-related breeds and marked differences between the sexes. Our findings support results from other studies, i.e., that individual crossover counts are associated with the RNF212, SYCP2 and MSH4 genes in pig. In addition, we have found two novel candidate genes associated with the trait, namely PRDM7 and MEI1.

Using affected embryos to establish linkage phase in preimplantation genetic testing for thalassemia

BackgroundThis study aimed to evaluate the ability of next-generation sequencing (NGS) to conduct preimplantation genetic testing (PGT) for thalassemia using affected embryos.MethodsThis study included data from 36 couples who underwent PGT for thalassemia without probands and relative pedigrees. NGS results were compared with prenatal diagnosis results.ResultsThirty-six couples (29 α-thalassemia and 7 β-thalassemia) underwent 41 PGT cycles (31 α-thalassemia and 10 β-thalassemia). Analysis using NGS produced conclusive results for all biopsied blastocysts (100%, 217/217). One hundred and sixty (73.7%, 160/217) were unaffected by thalassemia. Preimplantation genetic testing for aneuploidy revealed that 112 (70.0%, 112/160) were euploid. Single blastocysts were transferred into the uteri of 34 women (53 frozen embryo transfer [FET] cycles). Thirty-two cycles resulted in clinical pregnancies, with a clinical pregnancy rate of 60.1% (32/53) per FET cycle. Twenty-two cycles (22 couples) resulted in 23 live births, with a live birth rate of 43.4% (23/53; 3 cycles were ongoing pregnancies). All 25 embryos’ prenatal diagnosis results and/or thalassemia gene analyses after delivery were concordant with the NGS-PGT results. Seven embryos (21.9%, 7/32) were miscarried before 12 weeks’ gestation, and the abortion villus in four showed a normal karyotype and thalassemia results consistent with the NGS-PGT results. Aborted fetus samples from 3 cycles were not available because the pregnancy lasted less than 5 weeks.ConclusionNGS can be used to conduct PGT for thalassemia using affected embryos as a reference.Trial registrationRetrospectively registered.

Inferring genetic characteristics of Japanese Black cattle populations using genome-wide single nucleotide polymorphism markers

This study attempted to infer the population structure of Japanese Black cattle by using genotype data on 33,063 genomewide single nucleotide polymorphism (SNP) markers of totally 4,348 fattened steers slaughtered at carcass markets in Tokyo, Osaka, Hyogo, Tottori, and Hiroshima prefectures. We evaluated allele frequency, heterozygosity, linkage disequilibrium, correlation of linkage phase, and genotype concordance among the steers. The distribution of allele frequencies in the steers sampled in Hyogo differed from the others, showing >10% of the SNPs as monomorphic. Observed heterozygosity was lowest and degree of linkage disequilibrium was highest in Hyogo. Genotypes were more similar among Hyogo steers than between other steer pairs. These results show the genetic characteristics of the Japanese Black cattle populations inferred from genotype data on genome-wide SNPs obtained using a commercial chip.

Prediction ability of genome-wide markers in Pinus taeda L. within and between population is affected by relatedness to the training population and trait genetic architecture.

Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population (n = 2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage (full-sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.

Calibration and validation of predicted genomic breeding values in an advanced cycle maize population

Key messageModel training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets.The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

Comparative Study of Record Linkage Approaches for Big Data

Record linkage is a challenging task for Big Data. This paper, hence, attempts to shed light on record linkage approaches for Big Data by comparing three dimensions involving record linkage phases, dataset properties, and parallel processing approach for Big Data. The current state of art have only conducted comparative studies between record linkage approaches. There has been only one comparative study exploring the whole record linkage framework of the relational database. It is believed that the focus of the present study on the dimensions of the parallel processing approaches for Big Data and dataset properties was worth exploring. It was found that first, data exploration was almost a non-existing phase despite its importance of exploring the dataset being examined; second, techniques that handle data standardization and preparation phase of the first dimension were not extensively covered in the literature which can directly affect the results’ quality; third, the record linkage in unstructured data was not yet explored in literature; fourth, the MapReduce was used in about 50 % of the selected studies to handle the parallel processing of Big Data, but due to its limitations, more recent and efficient approaches had been used, such as Apache Spark and Apache Flink. Apache Spark is just recently adapted to resolve duplicates due to its supporting of in-memory computation, which makes the whole linkage process more efficient. Although the comparative study includes many recent studies supporting Apache Spark, adopting Apache Spark to solve the problem of record linkage is not yet well explored in literature, as more researches need to be conducted. In addition, Apache Flink is still rarely used to solve the record linkage problem of Big Data. Fifth, pruning techniques, used to eliminate unnecessary comparisons, are not adequately applied in the covered studies despite their effect on reducing the search space resulting in a more effective Record Linkage process.

A Novel Robust Sensorless Technique for Field-Oriented Control Drive of Permanent Magnet Synchronous Motor

This paper proposes a novel rotor position estimation technique based on stator flux linkage to implement robust sensorless field-oriented control for permanent magnet synchronous motor drives. The proposed method can accurately estimate rotor position by mitigating the dependence on quality of the magnitude and phase angle of the estimated stator flux linkage. A high-accuracy load angle calculation based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -axis current estimation method is first developed for further rotor position determination. In the rotor position calculation, the reference flux linkage magnitude is used instead of the estimated one to avoid the influence of potential high noise levels during low speed operation. As a result, the rotor angle estimation depends only on the phase angle of stator flux linkage, without the effect of its magnitude. The estimation accuracy can thus be improved. Also, by employing a simple stator flux linkage phase lag compensator, the performance of the proposed sensorless method can be dramatically improved at low speed. Moreover, the problem of motor parameter variation (e.g., changes in winding resistance, stator inductances, and permanent magnetic flux linkage) has been analyzed and overcome. Finally, comparisons between conventional and proposed methods are presented with simulations and experiments to demonstrate the effectiveness and reliability of the proposed method.

Exploring the Potential Genetic Heterogeneity in the Incidence of Hoof Disorders in Austrian Fleckvieh and Braunvieh Cattle.

Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh were as follows: CBLB on BTA1, which causes arthritis in rats; CAV2 on BTA4, which affects skeletal muscles in mice; PTHLH on BTA5, which causes disease phenotypes related to the skeleton in humans, mice, and zebrafish; and SORCS2 on BTA6, which causes decreased susceptibility to injury in mice. Some of the significant SNPs (BTA1, BTA4, BTA5, BTA13, and BTA16) revealed allelic heterogeneity—i.e., different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA1, BTA5, BTA13, and BTA16) correlated to inter-breed differences in linkage disequilibrium (LD) structure and may thus represent false-positive heterogeneity. However, positions on BTA6 (SORCS2), BTA14, and BTA24 mark Braunvieh-specific regions. We hypothesize that the observed genetic heterogeneity of hoof disorders is a by-product of different selection goals defined for the analyzed breeds—toward dairy production in Braunvieh and toward beef production in Fleckvieh. Based on the current dataset, it is not possible to unequivocally confirm or exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The main reason for the problem is that the potential heterogeneity was explored through SNP–phenotype associations and not through causal mutations, due to a limited SNP density offered by the SNP-chip. The rationale against genetic heterogeneity comprises a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, different selection goals defined for the analyzed breeds accompanied by no systematic, genome-wide differences in LD structure between the breeds favor the heterogeneity hypothesis at some smaller genomic regions.

Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

Key messagePolypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F1 population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.

Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds

BackgroundSequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision.ResultsTo maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10−8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk.ConclusionsOur study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.