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Journal articles on the topic 'QTL analysi'

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Published: 9 March 2023

Last updated: 10 March 2023

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1

Batista, Luiz Fernando Dias, Madeline E. Rivera, Aaron B. Norris, Jordan Adams, Roberta Cracco, Morgan Jackson, and Luis O. Tedeschi. "44 Effect of Quebracho (Schinopsis balansae) extract inclusion in a high roughage diet upon in vitro gas production." Journal of Animal Science 98, Supplement_2 (November 1, 2020): 53–54. http://dx.doi.org/10.1093/jas/skz397.122.

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Abstract The utilization of natural plant secondary compounds as feed additives in animal nutrition has been extensively studied because of their ability to modify digestive and metabolic functions. Condensed tannin (CT) supplementation can potentially alter ruminal fermentation, and mitigate methane (CH4) emissions. The objective of this study was to determine the effect of quebracho CT extract (QT; Schinopsis balansae) within a roughage-based diet on overall fermentability and CH4 production utilizing the in vitro gas production technique (IVGP). Twenty rumen cannulated steers (227 ± 19 kg) were randomly assigned to four dietary treatments (n=4): QT at 0, 1, 2, and 3% of DM (QT0, QT1, QT2, and QT3). A roughage-based diet containing 88% bermudagrass hay and 12% concentrate was fed daily at 2.1% of shrunk body weight. The animals were adapted to the basal diet for 24-d then introduced to predetermined treatments for 35d. Rumen inoculum was collected weekly from each steer to perform the incubations. Two hundred milligrams of air-dried base diet were incubated for 48-h with a composite rumen inoculum for each treatment over 5 wk. Kinetic analysis of cumulative 48h gas production was performed using Gasfit. Measurements of CH4 were performed via gas chromatography and digested residue was determined post-incubation. Data were analyzed using a random coefficients model. Total gas production was higher for QT0 compared to QT1 and QT3 (P = 0.001), but not different from QT2 (P = 0.554). The fractional rate of gas production was higher for QT2 compared to QT0 (P = 0.011). First and second pool gas production decreased linearly as QT inclusion increased (P = 0.042 and 0.010, respectively). There was no dietary effect in ivNDFD (P = 0.567). However, there was a linear tendency to decrease CH4 production with the addition of QT (P=0.071) likely due to changes in the microbial population.

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2

Barjes Alrawi, Ezzideen, Erica D. Warlick, Qing Cao, Mukta Arora, Shernan G. Holtan, Tim Krepski, Michael R. Verneris, John Wagner, Daniel J. Weisdorf, and Claudio G. Brunstein. "High Peripheral Blood Stem Cell (PBSC) CD34+ Cell Dose Increases the Risk of Chronic Gvhd after Human Leukocyte Antigen (HLA) Matched Sibling Transplantation." Blood 128, no. 22 (December 2, 2016): 5877. http://dx.doi.org/10.1182/blood.v128.22.5877.5877.

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Abstract CD34+ cell dose is a critical determinant of outcomes after allogeneic PBSC transplantation, with a CD34 dose ≥2.0 x 10e6/kg shown to positively impact hematopoietic engraftment and survival. However, it is unknown whether additional benefits are observed with even higher CD34 cell doses. Therefore, we further explored the effect of intermediate, high and very high CD34 cell doses on the incidence of engraftment, acute and chronic graft-versus-host disease (GVHD) and transplant related mortality (TRM) and on probability of survival and GVHD-Relapse-free survival (GRFS). Three hundred and five consecutive patients transplanted with GCSF-mobilized PBSC from HLA-matched sibling donors (MSD) were evaluated. Patients were ≥16 years of age, had a hematological malignancy and received a myeloablative or a nonmyeloablative conditioning regimen between 2002 and 2012. The median recipient age was 52 years (r, 19-74 years) with most being male (n=194, 63.8%) diagnosed with leukemia (72%) or lymphoma (22%), and intermediate disease risk index (DRI, n=204, 67%). The median age for the donor were 49 years (r,17-76 years). In 159 patients (52%) the donor and recipient were sex matched with 89 male patients having a female door (29%). The ABO blood type was matched in 195 patients (64%), 153(50%) received a myeloablative (MA) conditioning regimen, and 37 (12%) received a reduce intensity conditioning regimen containing ATG. The median follow up of surviving patients was 793 days (r, 14-4562 days). Patients were divided in four CD34 dose quartiles: first quartile (QT1), ≤4.8 x10e6/kg, QT2 4.8-6.0 x10e6/kg, QT3 6.0-7.5 x10e6/kg, and QT4 ≥ 7.6 x 10e6/kg. Notably, the CD3 doses were similar for all quartiles: QT1 was 3.4 x 10e8/kg (r, 0.3-10.0), QT2 was 2.7 x 10e8/kg (r, 1.1-7.6), QT3 was 2.8 x 10e8/kg (r, 0.8-7.2) and QT4 was 2.8 x 10e8/kg (r, 1.4-7.7); there was no correlation between CD34 and CD3 cell doses. Patient and donor characteristics were similar in the four groups except for shorter median follow-up (P <0.01) in QT1, more sex mismatched grafts (P <0.01) in QT3, and lower median number of cell collections (P <0.01) and more female donor: male recipient pairs (P< 0.01) in QT4. Multivariate analysis results are summarized on the table. Higher CD34+ cell dose was associated with improved platelet recovery with trends toward lower TRM and improved overall survival. Chronic GVHD however was also higher. In summary, additional studies are needed to establish a survival benefit in recipients of higher cell doses >4.8 x 10e6 CD34 cells/kg. Unless survival is positively impacted, the higher risk of chronic GVHD would argue for assigning an upper CD34 cell dose limit to reduce this risk that can significantly impair quality of life. Table Table. Disclosures No relevant conflicts of interest to declare.

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3

Takahashi, Hidekazu. "QTL analysis using the Windows QTL Cartographer." Breeding Research 10, no. 1 (2008): 11–14. http://dx.doi.org/10.1270/jsbbr.10.11.

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4

Kang, Yiwei, Miao Zhang, Yue Zhang, Weixun Wu, Pao Xue, Xiaodeng Zhan, Liyong Cao, Shihua Cheng, and Yingxin Zhang. "Genetic Mapping of Grain Shape Associated QTL Utilizing Recombinant Inbred Sister Lines in High Yielding Rice (Oryza sativa L.)." Agronomy 11, no. 4 (April 7, 2021): 705. http://dx.doi.org/10.3390/agronomy11040705.

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Grain shape is a key factor for yield and quality in rice. To investigate the genetic basis of grain shape in the high-yielding hybrid rice variety Nei2You No.6, a set of recombinant inbred sister lines (RISLs) were used to map quantitative trait loci (QTLs) determining grain length (GL), grain width (GW), and length-width ratio (LWR) in four environments. A total of 91 medium/minor-effect QTL were detected using a high-density genetic map consisting of 3203 Bin markers composed of single nucleotide polymorphisms, among which 64 QTL formed 15 clusters. Twelve of 15 clusters co-localized with QTL previously reported for grain shape/weight. Three new QTL were detected: qGL-7a, qGL-8, and qGL-11a. A QTL cluster, qLWR-12c/qGW-12, was detected across all four environments with phenotypic variation explained (PVE) ranging from 3.67% to 11.93%, which was subsequently validated in paired lines of F17 progeny and tightly linked marker assay in F10 generation. Subsequently, 17 candidate genes for qLWR-12c/qGW-12 were detected in the 431 Kb interval utilizing bulk segregant analysis (BSA). Among these, OsR498G1222170400, OsR498G1222171900, OsR498G1222185100, OsR498G1222173400, and OsR498G1222170500 were the best candidates, which lays the foundation for further cloning and will facilitate high-yield breeding in rice.

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5

Liu, Xiaolu, Xiangyuan Wan, Xiaodong Ma, and Jianmin Wan. "Dissecting the genetic basis for the effect of rice chalkiness, amylose content, protein content, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice using the chromosome segment substitution line population across eight environments." Genome 54, no. 1 (January 2011): 64–80. http://dx.doi.org/10.1139/g10-070.

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Quantitative trait locus (QTL) mapping and stability analysis were carried out for 16 rice ( Oryza sativa L.) quality traits across eight environments, by using a set of chromosome segment substitution lines with ‘Asominori’ as genetic background. The 16 quality traits include percentage of grain with chalkiness (PGWC), area of chalky endosperm (ACE), amylose content (AC), protein content (PC), peak viscosity, hot paste viscosity, cool paste viscosity, breakdown viscosity (BDV), setback viscosity (SBV), consistency viscosity, cooked-rice luster (LT), scent, tenderness (TD), viscosity, elasticity, and the integrated values of organleptic evaluation (IVOE). A total of 132 additive effect QTLs are detected for the 16 quality straits in the eight environments. Among these QTLs, 56 loci were detected repeatedly in at least three environments. Interestingly, several QTL clusters were observed for multiple quality traits. Especially, one QTL cluster near the G1149 marker on chromosome 8 includes nine QTLs: qPGWC-8, qACE-8, qAC-8, qPC-8a, qBDV-8a, qSBV-8b, qLT-8a, qTD-8a, and qIVOE-8a, which control PGWC, ACE, AC, PC, BDV, SBV, LT, TD, and IVOE, respectively. Moreover, this QTL cluster shows high stability and repeatability in all eight environments. In addition, one QTL cluster was located near the C2340 marker on chromosome 1 and another was detected near the XNpb67 marker on chromosome 2; each cluster contained five loci. Near the C563 marker on chromosome 3, one QTL cluster with four loci was found. Also, there were nine QTL clusters that each had two or three loci; however, their repeatability in different environments was relatively lower, and the genetic contribution rate was relatively smaller. Considering the correlations among all of the 16 quality traits with QTL cluster distributions, we can conclude that the stable and major QTL cluster on chromosome 8 is the main genetic basis for the effect of rice chalkiness, AC, PC, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice. Consequently, this QTL cluster is a novel gene resource for controlling rice high-quality traits and should be of great significance for research on formation mechanism and molecule improvement of rice quality.

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6

Mangin, B., P. Thoquet, and N. Grimsley. "Pleiotropic QTL Analysis." Biometrics 54, no. 1 (March 1998): 88. http://dx.doi.org/10.2307/2533998.

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7

Ukai, Yasuo. "Theory of QTL analysis." Breeding Research 1, no. 1 (1999): 25–31. http://dx.doi.org/10.1270/jsbbr.1.25.

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8

Xu, Peng, Jin Gao, Zhibin Cao, Peng W. Chee, Qi Guo, Zhenzhen Xu, Andrew H. Paterson, Xianggui Zhang, and Xinlian Shen. "Fine mapping and candidate gene analysis of qFL-chr1, a fiber length QTL in cotton." Theoretical and Applied Genetics 130, no. 6 (March 30, 2017): 1309–19. http://dx.doi.org/10.1007/s00122-017-2890-8.

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9

Hina, Aiman, Yongce Cao, Shiyu Song, Shuguang Li, Ripa Akter Sharmin, Mahmoud A. Elattar, Javaid Akhter Bhat, and Tuanjie Zhao. "High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)." International Journal of Molecular Sciences 21, no. 3 (February 4, 2020): 1040. http://dx.doi.org/10.3390/ijms21031040.

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Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

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10

UKAI, Yasuo. "Quantitative Trait and QTL Analysis." Japanese journal of crop science 68, no. 2 (1999): 179–86. http://dx.doi.org/10.1626/jcs.68.179.

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11

T., Hayashi. "Recent methods for QTL analysis." Japanese Journal of Biometrics 17, no. 1/2 (1996): 91–102. http://dx.doi.org/10.5691/jjb.17.91.

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12

Pérez-Pérez, José Manuel, David Esteve-Bruna, and José Luis Micol. "QTL analysis of leaf architecture." Journal of Plant Research 123, no. 1 (November 3, 2009): 15–23. http://dx.doi.org/10.1007/s10265-009-0267-z.

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13

van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuberization." Theoretical and Applied Genetics 93, no. 3 (August 1996): 307–16. http://dx.doi.org/10.1007/bf00223170.

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van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuberization." TAG Theoretical and Applied Genetics 93, no. 3 (August 1, 1996): 307–16. http://dx.doi.org/10.1007/s001220050282.

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15

Freyer, G., and N. Vukasinovic. "Comparison of granddaughter design and general pedigree design analysis of QTL in dairy cattle: a simulation study." Czech Journal of Animal Science 50, No. 12 (December 11, 2011): 545–52. http://dx.doi.org/10.17221/4260-cjas.

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Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and locating QTL in a simulated complex pedigree. QTL analyses were performed under the variance component model containing a random QTL and a random polygenic effect. The covariance matrix of the polygenic effect was a standard additive relationship matrix. The (co)variance matrix of the random QTL effect contained probabilities that QTL alleles shared by two individuals were identical by descent (IBD). In the GDD analysis, IBD probabilities were calculated using sires’ and daughters’ marker genotypes. In the GPD analysis, IBD probabilities were obtained using a deterministic approach. The estimation of QTL position and variance components was conducted using REML algorithm. Although both methods were able to locate the region of the QTL properly, the GPD method showed better precision of QTL position estimates in most cases and significantly higher power than the GDD method.  

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16

Chung, Ill-Min, Tae-Ho Ham, Gi-Won Cho, Soon-Wook Kwon, Yoonjung Lee, Jeonghwan Seo, Yeon-Ju An, So-Yeon Kim, Seung-Hyun Kim, and Joohyun Lee. "Study of Quantitative Trait Loci (QTLs) Associated with Allelopathic Trait in Rice." Genes 11, no. 5 (April 26, 2020): 470. http://dx.doi.org/10.3390/genes11050470.

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In rice there are few genetic studies reported for allelopathy traits, which signify the ability of plants to inhibit or stimulate growth of other plants in the environment, by exuding chemicals. QTL analysis for allelopathic traits were conducted with 98 F8 RILs developed from the cross between the high allelopathic parents of ‘Sathi’ and non-allelopathic parents of ‘Nong-an’. The performance of allelopathic traits were evaluated with inhibition rate on root length, shoot length, total length, root weight, shoot weight, and total weight of lettuce as a receiver plant. With 785 polymorphic DNA markers, we constructed a linkage map showing a total of 2489.75 cM genetic length and 3.17 cM of average genetic distance between each adjacent marker. QTL analysis detected on QTL regions on chromosome 8 responsible for the inhibition of shoot length and inhibition of total length. The qISL-8 explained 20.38% of the phenotypic variation for the inhibition on the shoot length. The qITL-8 explained 14.93% of the phenotypic variation for the inhibition on total length. The physical distance of the detected QTL region was 194 Kbp where 31 genes are located.

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Wu, Sanling, Jie Qiu, and Qikang Gao. "QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq." Interdisciplinary Sciences: Computational Life Sciences 11, no. 4 (August 6, 2019): 730–37. http://dx.doi.org/10.1007/s12539-019-00344-9.

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18

TOGASHI, Kenji, Naoyuki YAMAMOTO, Osamu SASAKI, JEO Rege, and Hisato TAKEDA. "Marker-QTL-Association Analysis Incorporating Diversification of QTL Variance and its Application." Nihon Chikusan Gakkaiho 67, no. 11 (1996): 923–29. http://dx.doi.org/10.2508/chikusan.67.923.

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19

Burke, John M., Shunxue Tang, Steven J. Knapp, and Loren H. Rieseberg. "Genetic Analysis of Sunflower Domestication." Genetics 161, no. 3 (July 1, 2002): 1257–67. http://dx.doi.org/10.1093/genetics/161.3.1257.

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Abstract Quantitative trait loci (QTL) controlling phenotypic differences between cultivated sunflower and its wild progenitor were investigated in an F3 mapping population. Composite interval mapping revealed the presence of 78 QTL affecting the 18 quantitative traits of interest, with 2–10 QTL per trait. Each QTL explained 3.0–68.0% of the phenotypic variance, although only 4 (corresponding to 3 of 18 traits) had effects >25%. Overall, 51 of the 78 QTL produced phenotypic effects in the expected direction, and for 13 of 18 traits the majority of QTL had the expected effect. Despite being distributed across 15 of the 17 linkage groups, there was a substantial amount of clustering among QTL controlling different traits. In several cases, regions influencing multiple traits harbored QTL with antagonistic effects, producing a cultivar-like phenotype for some traits and a wild-like phenotype for others. On the basis of the directionality of QTL, strong directional selection for increased achene size appears to have played a central role in sunflower domestication. None of the other traits show similar evidence of selection. The occurrence of numerous wild alleles with cultivar-like effects, combined with the lack of major QTL, suggests that sunflower was readily domesticated.

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20

Byrne, Patrick F. "Quantitative Trait Locus (QTL) Analysis 1." Journal of Natural Resources and Life Sciences Education 34, no. 1 (2005): 124. http://dx.doi.org/10.2134/jnrlse.2005.0124.

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21

Byrne, Patrick F. "Quantitative Trait Locus (QTL) Analysis 2." Journal of Natural Resources and Life Sciences Education 34, no. 1 (2005): 124. http://dx.doi.org/10.2134/jnrlse.2005.0124a.

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22

Bekes, F., W. Ma, and K. Gale. "QTL analysis of wheat quality traits." Acta Agronomica Hungarica 50, no. 3 (September 1, 2002): 249–62. http://dx.doi.org/10.1556/aagr.50.2002.3.3.

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This paper aims to give an overview on the different aspects of QTL analysis of quality traits of wheat through the brief introduction of molecular genetics, cereal chemistry and the statistical methods developed and applied recently in this area. Some examples are also provided, based on the author's research activity carried out in the National Wheat Molecular Marker Program (NWMMP) established in Australia in 1996.

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23

Nganga, Joseph, Mabel Imbuga, and Fuad A. Iraqi. "Comparative genome analysis of trypanotolerance QTL." Veterinary Immunology and Immunopathology 128, no. 1-3 (March 2009): 216. http://dx.doi.org/10.1016/j.vetimm.2008.10.017.

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24

Iimura, Kazunari, Kimihisa Tasaki, Yoshiko Nakazawa, and Masayuki Amagai. "QTL analysis of strawberry anthracnose resistance." Breeding Research 15, no. 3 (2013): 90–97. http://dx.doi.org/10.1270/jsbbr.15.90.

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25

van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuber dormancy." Theoretical and Applied Genetics 93, no. 3 (August 1996): 317–24. http://dx.doi.org/10.1007/bf00223171.

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Ewing, E. E., R. L. Plaisted, S. McMurry, M. W. Bonierbale, and J. H. van den Berg. "QTL analysis of potato tuber dormancy." TAG Theoretical and Applied Genetics 93, no. 3 (August 1, 1996): 317–24. http://dx.doi.org/10.1007/s001220050283.

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27

Shimoi, Hitoshi, and Taku Kato. "QTL analysis of a sake yeast." Journal of Biotechnology 136 (October 2008): S746. http://dx.doi.org/10.1016/j.jbiotec.2008.07.1776.

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28

Khan, Nisar A., Stephen M. Githiri, Eduardo R. Benitez, Jun Abe, Shinji Kawasaki, Takeshi Hayashi, and Ryoji Takahashi. "QTL analysis of cleistogamy in soybean." Theoretical and Applied Genetics 117, no. 4 (May 27, 2008): 479–87. http://dx.doi.org/10.1007/s00122-008-0792-5.

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Verbyla, Arūnas P., Andrew W. George, Colin R. Cavanagh, and Klara L. Verbyla. "Whole-genome QTL analysis for MAGIC." Theoretical and Applied Genetics 127, no. 8 (June 14, 2014): 1753–70. http://dx.doi.org/10.1007/s00122-014-2337-4.

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30

Besnier, François, Arnaud Le Rouzic, and José M. Álvarez-Castro. "Applying QTL analysis to conservation genetics." Conservation Genetics 11, no. 2 (February 10, 2010): 399–408. http://dx.doi.org/10.1007/s10592-009-0036-5.

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Li, Ning, Jian Sun, Jingguo Wang, Hualong Liu, Hongliang Zheng, Luomiao Yang, Yingpei Liang, Xianwei Li, and Detang Zou. "QTL analysis for alkaline tolerance of rice and verification of a major QTL." Plant Breeding 136, no. 6 (October 29, 2017): 881–91. http://dx.doi.org/10.1111/pbr.12539.

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Evans, David M., Gu Zhu, David L. Duffy, Grant W. Montgomery, Ian H. Frazer, and Nicholas G. Martin. "Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q." European Journal of Human Genetics 12, no. 10 (July 28, 2004): 835–42. http://dx.doi.org/10.1038/sj.ejhg.5201248.

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Huang, W., Z. Xu, Y. Xiong, and B. Zuo. " QTL analysis for carcass composition and meat quality traits on SSC7q1.1-q1.4 region in Large White × Meishan F₂ pigs." Czech Journal of Animal Science 57, No. 6 (June 4, 2012): 283–89. http://dx.doi.org/10.17221/5963-cjas.

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Significant QTL for carcass and meat quality traits on Sus scrofa chromosome 7 (SSC7) were detected in various Meishan derived resource populations, especially on q1.1-q1.4 region. In order to confirm and narrow the QTL in this region, seven single-nucleotide polymorphisms (SNPs) and one insertion or deletion located in eight genes (BTNL1, SLC39A7, COL21A1, PPARD, GLP1R, MDFI, GNMT, and PLA2G7) were included for linkage mapping in a Large White × Meishan resource population, as well as two flanking microsatellite markers (SW2155 and SW352). Ten chromosome-wise significant QTL and two suggestive QTL were found. QTL affecting carcass weight and dressing percentage were mapped within the interval BTNL1 and SLC39A7. QTL for skin weight and percentage, bone weight and percentage in carcass were located between the interval PPARD and GLP1R. QTL for fat weight and percentage in carcass were detected between GNMT and PLA2G7 genes, while QTL for loin muscle width was found between GLP1R and MDFI. The results of this study will help to facilitate identifying the causative molecular genetic variation in this region.  

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34

Goffinet, Bruno, and Sophie Gerber. "Quantitative Trait Loci: A Meta-analysis." Genetics 155, no. 1 (May 1, 2000): 463–73. http://dx.doi.org/10.1093/genetics/155.1.463.

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Abstract This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.

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Sen, Śaunak, and Gary A. Churchill. "A Statistical Framework for Quantitative Trait Mapping." Genetics 159, no. 1 (September 1, 2001): 371–87. http://dx.doi.org/10.1093/genetics/159.1.371.

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AbstractWe describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

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KIM, JONG-JOO, HONGHUA ZHAO, HAUKE THOMSEN, MAX F. ROTHSCHILD, and JACK C. M. DEKKERS. "Combined line-cross and half-sib QTL analysis of crosses between outbred lines." Genetical Research 85, no. 3 (June 2005): 235–48. http://dx.doi.org/10.1017/s0016672305007597.

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Data from an F2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0·6. The combined and half-sib models had similar power for FD<0·4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0·5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.

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Uimari, Pekka, and Ina Hoeschele. "Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms." Genetics 146, no. 2 (June 1, 1997): 735–43. http://dx.doi.org/10.1093/genetics/146.2.735.

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A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.

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Tondas, Alexander Edo, Rido Mulawarman, Monica Trifitriana, Siti Nurmaini, and Irfannuddin Irfannuddin. "Arrhythmia Risk Profile and Ventricular Repolarization Indices in COVID-19 Patients: A Systematic Review and Meta-Analysis." Journal of Infection in Developing Countries 15, no. 02 (March 7, 2021): 224–29. http://dx.doi.org/10.3855/jidc.13922.

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Introduction: Coronavirus disease 2019 (COVID-19) has been associated with cardiac arrhythmias. Several electrocardiographic markers have been used to predict the risk of arrhythmia in patients with COVID-19. We aim to investigate the electrocardiographic (ECG) ventricular repolarization indices in patients with COVID-19. Methodology: We performed a comprehensive systematic literature search from PubMed, EuropePMC, SCOPUS, Cochrane Central Database, and Google Scholar Preprint Servers. The primary endpoints of this search were: Tp-e (T-peak-to-T-end) interval, QTd (QT dispersion), and Tp-e/QTc ratio in patients with newly diagnosed COVID-19 from inception up until August 2020. Results: There were a total of 241 patients from 2 studies. Meta-analysis showed that Tp-e/QTc ratio was higher in COVID-19 group (mean difference 0.02 [0.01, 0.02], p < 0.001; I2: 18%,). Tp-e interval was more prolonged in COVID-19 group (mean difference 7.76 [3.11, 12.41], p < 0.001; I2: 80%) compared to control group. QT dispersion (QTd) also was increased in COVID-19 group (mean difference 1.22 [0.61, 1.83], p < 0.001 ; I2:30%). Conclusions: Several electrocardiographic markers including Tp-e/QTc, Tp-e interval, and QTd are significantly increased in patients with COVID-19.

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TAKAI, Toshiyuki, Akihiro OHSUMI, Yumiko ARAI, Norio IWASAWA, Masahiro YANO, Toshio YAMAMOTO, Satoshi YOSHINAGA, and Motohiko KONDO. "QTL Analysis of Leaf Photosynthesis in Rice." Japan Agricultural Research Quarterly: JARQ 47, no. 3 (2013): 227–35. http://dx.doi.org/10.6090/jarq.47.227.

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Kenis, K., and J. Keulemans. "QTL ANALYSIS OF GROWTH CHARACTERISTICS IN APPLE." Acta Horticulturae, no. 663 (December 2004): 369–74. http://dx.doi.org/10.17660/actahortic.2004.663.63.

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K., Sato. "QTL analysis and related network in berley." Japanese Journal of Biometrics 17, no. 1/2 (1996): 79–90. http://dx.doi.org/10.5691/jjb.17.79.

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Piepho, Hans-Peter, and Klaus Pillen. "Mixed modelling for QTL × environment interaction analysis." Euphytica 137, no. 1 (2004): 147–53. http://dx.doi.org/10.1023/b:euph.0000040512.84025.16.

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Vreugdenhil, D., M. Koornneel, and L. I. Sergeeva. "Use of QTL analysis in physiological research." Russian Journal of Plant Physiology 54, no. 1 (February 2007): 10–15. http://dx.doi.org/10.1134/s1021443707010025.

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Hyne, V., and M. J. Kearsey. "QTL analysis: further uses of ‘marker regression’." Theoretical and Applied Genetics 91, no. 3 (August 1995): 471–76. http://dx.doi.org/10.1007/bf00222975.

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Kearsey, M. J., and V. Hyne. "QTL analysis: a simple ‘marker-regression’ approach." Theoretical and Applied Genetics 89, no. 6 (November 1994): 698–702. http://dx.doi.org/10.1007/bf00223708.

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Bhattacharjee, Samsiddhi, Chia-Ling Kuo, Nandita Mukhopadhyay, Guy N. Brock, Daniel E. Weeks, and Eleanor Feingold. "Robust Score Statistics for QTL Linkage Analysis." American Journal of Human Genetics 82, no. 3 (March 2008): 567–82. http://dx.doi.org/10.1016/j.ajhg.2007.11.012.

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Zeng, D. L., L. B. Guo, Y. B. Xu, K. Yasukumi, L. H. Zhu, and Q. Qian. "QTL analysis of seed storability in rice." Plant Breeding 125, no. 1 (February 2006): 57–60. http://dx.doi.org/10.1111/j.1439-0523.2006.01169.x.

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Ritter, E., N. N. Rodríguez-Medina, B. Velásquez, D. Rivero, J. A. Rodríguez, F. Martínez, and J. Valdés-Infante. "QTL (QUANTITATIVE TRAIT LOCI) ANALYSIS IN GUAVA." Acta Horticulturae, no. 849 (January 2010): 193–202. http://dx.doi.org/10.17660/actahortic.2010.849.21.

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Clarke, Jonathan H., Richard Mithen, James K. M. Brown, and Caroline Dean. "QTL analysis of flowering time inArabidopsis thaliana." Molecular and General Genetics MGG 248, no. 3 (August 1995): 278–86. http://dx.doi.org/10.1007/bf02191594.

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Teng, Sheng, Dali Zeng, Qian Qian, Yasufumi Kunihifo, Danian Huang, and Lihuang Zhu. "QTL analysis of rice low temperature germinability." Chinese Science Bulletin 46, no. 21 (November 2001): 1800–1803. http://dx.doi.org/10.1007/bf02900554.

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