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Statistical Considerations and Biological Mechanisms Underlying Individual Differences in Adaptations to Exercise Training
Published in Peter M. Tiidus, Rebecca E. K. MacPherson, Paul J. LeBlanc, Andrea R. Josse, The Routledge Handbook on Biochemistry of Exercise, 2020
Jacob T. Bonafiglia, Hashim Islam, Nir Eynon, Brendon J. Gurd
Quantitative trait loci (QTL) are polymorphic regions (loci) associated with variability in a given phenotype. QTL mapping involves correlating tagged genetic variants (i.e., selected variants that are meant to cover a given genomic region) with variation in a given phenotype (87). It is important to note that the goal of QTL mapping is to determine whether phenotype variability is associated with a given locus or loci rather than specific genetic variants per se. Therefore, QTL mapping is often followed with more detailed analysis (e.g., “fine-mapping”) to identify specific genetic variants within a given loci that correlate with a given phenotype.
Identification of Genes Underlying Polygenic Obesity in Animal Models
Published in Claude Bouchard, The Genetics of Obesity, 2020
Craig H. Warden, Janis S. Fisler
The methods used for phenotyping will depend on the trait of interest. The most specific measure available should be used. For example, percent of carcass weight as fat is a better measure of obesity than body weight or body mass index. Particular care should be taken to minimize the error of the assay since nongenetic variation will reduce the power of the method. QTL mapping can be applied to any continuously variable trait which can be measured, such as carcass lipid, plasma insulin or glucose levels, oxygen consumption, or weight gain induced by a high-fat diet.
Impact of Integrated Omics Technologies for Identification of Key Genes and Enhanced Artemisinin Production in Artemisia annua L.
Published in Tariq Aftab, M. Naeem, M. Masroor, A. Khan, Artemisia annua, 2017
Shashi Pandey-Rai, Neha Pandey, Anjana Kumari, Deepika Tripathi, Sanjay Kumar Rai
Another method is recurrent selection, which is more suitable for A. annua, as it involves cross-pollinating species. Through different selection methods, a few high-yielding varieties/cultivars of A. annua, such as “CIM-Arogya,” “Jeevan Raksha,” and Asha, were developed by the Central Institute for Medicinal and Aromatic Plants (CIMAP), India, as superior lines rich in AN (Patra and Kumar 2005). Recently, Townsend et al. (2013) have also reported that selection of material for breeding using combining ability analysis of a diallele cross can be used for the selection/identification of elite parents to produce high-AN-producing A. annua hybrids. This selection method was found to be consistent with advanced QTL-based molecular breeding approaches. The advancement of plant breeding using different biotechnological tools is now opening a new platform for crop improvement. Among these tools is molecular/mutational breeding, which involves induced mutational changes through chemicals/radiation or by site-directed mutagenesis with the advantage of improving one or two yield-related characters without modifying the rest of the genetic constitution. A successful effort was made by Mediplant (a Swiss not-for-profit organization) by developing a hybrid of A. annua known as “Artemis” (F1 hybrid) with a high mean annual AN production of about 32 kg/ha. Further, they also created a new high-yielding hybrid with 40.5–52.0 kg/ha AN production (Simonnet et al. 2008).
A diversity outbred F1 mouse model identifies host-intrinsic genetic regulators of response to immune checkpoint inhibitors
Published in OncoImmunology, 2022
Justin B. Hackett, James E. Glassbrook, Maria C. Muñiz, Madeline Bross, Abigail Fielder, Gregory Dyson, Nasrin Movahhedin, Jennifer McCasland, Claire McCarthy-Leo, Heather M. Gibson
DO mice were generated by continued outbreeding beyond the CC project, and DO mice are now maintained by The Jackson Laboratory, currently at generation 45 (~74 total including the initial CC crosses). Each DO mouse is genetically distinct, providing a model optimized for high resolution Quantitative Trait Locus (QTL) mapping that enables identification of genomic regions that influence complex traits.11,14 The Mouse Universal Genotyping Array (MUGA), now in its third iteration (GigaMUGA) genotypes 143,259 markers spanning the entire genome of each mouse.15 These markers are not directly used to identify causal single nucleotide polymorphisms (SNPs), but instead enable haplotype block reconstruction. Identifying the founder strain contributions across all chromosomes (Chr) allows for imputation and evaluation of all genomic SNPs. QTL effect analysis can then be performed to determine the direction and effect size of a phenotypic association to a specific founder genotype at each locus across the genome. The entirety of the data analysis pipeline is performed in the R/qtl2 package, which is optimized for use with the DO mouse population.16 Locus validation can then be conducted using inbred CC lines selected for haplotypes matching the QTL effects data.13 We have crossed inbred C57BL/6 (B6) sires with several cohorts of DO dams to produce DOB6F1 mice, which are used to investigate germline factors that contribute to ICI response.
A behavioral model for mapping the genetic architecture of gut-microbiota networks
Published in Gut Microbes, 2021
Libo Jiang, Xinjuan Liu, Xiaoqing He, Yi Jin, Yige Cao, Xiang Zhan, Christopher H. Griffin, Claudia Gragnoli, Rongling Wu
We performed a computer simulation study to examine the statistical power of our model. We mimicked the sampling design of Davenport et al.’s6 study by simulating host QTLs that affect the abundance of microbes in the gut. We assume eight microbes that are interact with each other through cooperation or competition. As an example, we focus on two types of interactions, mutualisms and aggression. Three schemes were used to define QTLs in terms of the proportion of its genetic variance to the total phenotypic variance: big QTL (proportion = 0.10–0.20), moderate QTL (proportion = 0.05–0.10), and small QTL (proportion = 0.01–0.05). Traditional GWAS models can only detect the QTLs responsible for the abundance of each microbe, whereas our new model can identify QTLs that regulate how different microbes interact with one another to determine microbial communities using the same data. Based on 1,000 simulation replicates, we calculated and compared the detection power of significant QTLs from both the traditional and the new models (Table 2). We found that the power of detecting mutualism or aggression QTLs by the new model is strikingly larger than the power of detecting abundance QTLs by the traditional model, especially when the QTLs have small effects. For example, the traditional model has the power of only about 0.20 to detect a small QTL, whereas the new model increases the power of QTL detection to approximately 0.70. We also found that the new model shows reasonably good precision in parameter estimate and relatively low false positive rates for QTL detection (< 0.08).
Learning to collaborate: bringing together behavior and quantitative genomics
Published in Journal of Neurogenetics, 2020
Patricka A. Williams-Simon, Mathangi Ganesan, Elizabeth G. King
Mapping the genetic variants contributing to complex traits in general has presented a major challenge due to the difficulty of characterizing the effect of a single variant when there are many other variants also affecting a phenotype and the effects at individual loci are subtle (Boyle, Li, & Pritchard, 2017; Rockman, 2012). If trait categories are viewed as a hierarchy, Garland and Kelly (2006) have argued that behavior is expected to be one of the most complex, because it will be influenced by physiology, morphology, etc. at the lower hierarchical levels, leading to the expectation that the genetic basis of most behaviors will be highly complex. In addition, the processes of learning and memory are themselves the products of many other processes, such as sensory and motor functions, which further argues for their expected complexity (Schultzhaus, Saleem, Iftikhar, & Carney, 2017; Dolan et al., 2019). Early quantitative genetic approaches to map genetic variants used two-way quantitative trait loci (QTL) mapping, in which two parental strains are crossed to create an F1, then the F1s are either crossed to themselves or backcrossed to one of the parents to create an F2 generation. This creates a population with recombination breakpoints at different positions throughout the genome, allowing one to identify the association between the genotype at a given position and the phenotype of interest. However, because the individuals are only crossed for just a few generations, resulting in large haplotype blocks, the resolution for identifying individual genes, rather than regions of the genome is low (Mackay, 2001; Slate, 2004), with mapping regions typically wider than 10 cM (centiMorgans) and encompassing hundreds of genes. This has made it difficult to hone in on candidate genes that are influencing a particular phenotype.