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Medicinal Plants: Future Thrust Areas and Research Directions
Published in Amit Baran Sharangi, K. V. Peter, Medicinal Plants, 2023
Different types of markers like restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), inter simple sequence repeats (ISSR), simple sequence repeats (SSR) and amplified fragment length polymorphism (AFLP) markers are used for validation purpose in MAPs. DNA barcodes using second internal transcribed spacer (ITS2) region are used for discriminating medicinal plant species (Pang and Chen, 2014). RAPD analysis was used for evaluation of genetic relationships in several medicinal plant species. ISSR markers were used to evaluate the genetic diversity in many of the medicinal plants. Molecular markers can be employed to characterize any phenotypic trait, biochemical, and/or physiological mechanisms. The direct measurement of such traits can be simultaneously mapped. The number of loci controlling genetic variation of any important agronomic trait(s) in segregating population can be estimated, and the map positions of these loci in the genome be determined by means of molecular linkage genetic maps and QTL mapping technology.
Race and the Role of Sociocultural Context in Forensic Anthropological Ancestry Assessment
Published in Heather M. Garvin, Natalie R. Langley, Case Studies in Forensic Anthropology, 2019
Michala K. Stock, Katie M. Rubin
Furthermore, while race is socially defined, there does exist observable phenotypic, or physically expressed, variation among human groups that is biological in nature. However, patterned variation in human variation does not equate to race; race is typically conceived of as discrete bins, whereas biological variation is clinal – that is, much of human physical variation occurs along gradients (typically geographic in nature) with no well-defined or distinct boundaries. The systematic nature of human variation means that more closely related populations of individuals tend to share greater similarity in their phenotypic expression – including skeletal traits – than do more distantly related groups, even though expression of those traits is not homogenous within any group (i.e., there is significant variation within groups) and any given expression of a trait is not unique to any one group (Hefner, 2009: Tables 3–13). Perhaps the simplest clinal or geographically patterned phenotypic trait to appreciate in humans is skin color (Relethford, 2002). Yet, when it comes to race, an individual’s skin color does not obligate them to self-identify by others’ external categories; for example, individuals who self-identify as “Black” can have less melanin (i.e., lighter skin tone) than those who self-identify as “White.”
Etiologies of obesity
Published in G. Michael Steelman, Eric C. Westman, Obesity, 2016
Many of the new insights being gleaned on genetic promoters of obesity have come from GWASs. Through huge meta-analyses, researchers are able to examine interactions between millions of SNPs and the phenotypic trait of interest (e.g., obesity) (56). To date, GWASs have identified 141 suggestive loci for obesity and closely related traits (e.g., BMI, WC), of which 57 loci reach genome-wide significance (10). Of note is that each obesity allele (genetic loci) has a different phenotype depending on the ethnic group (27).
Nurtured Genetics: Prenatal Testing and the Anchoring of Genetic Expectancies
Published in The American Journal of Bioethics, 2023
Rémy Furrer, Shai Carmi, Todd Lencz, Gabriel Lázaro-Muñoz
Due to advances in personalized genomics, it has become possible to quantify genetic variance and use it as an individual source of information. Polygenic Scores (PGS) are probabilistic scores that can provide information about an individual’s genetic likelihood of developing any (conceivably measurable) phenotypic trait. (i.e., extraversion, intelligence, height…etc.). Not only is genetic variance quantifiable and accessible as a source of information, but it can also precede most sources of environmental variance through prenatal testing. That is, parents can receive probabilistic genetic scores as the very first piece of evaluative information about their child. This genetic information will precede any (observable) environmental influences on their child’s development, and it is upon these initial probabilistic genetic estimates that they will create expectancies as to who their child will become and how they will behave. The concern is that polygenic scores—which only account for a relatively small portion of individual behavioral variability (see Plomin and Von Stumm 2022 for recent PGS estimates)—will lead parents to over-value genetic information over subsequent environmental information in forming an initial impression of their child.
Genetic diversity, allelic variation and marker trait associations in gamma irradiated mutants of rice (Oryza sativa L.)
Published in International Journal of Radiation Biology, 2022
S. Ramchander, M. T. Andrew Peter Leon, J. Souframanien, M. Arumugam Pillai
The mean, range and standard error of the morphological data were calculated and used for comparison between genotypes. Correlation (Pearson’s) between the traits were evaluated from the data. Marker polymorphisms were scored for the presence (1) or absence (0) of bands in a binary data matrix as discrete variables. Polymorphism Information Content (PIC) was calculated using the formula given by (Anderson et al. 1993). Both morphological and SSR data were analyzed using the R package (https://cran.r-project.org/). The genotypes (mutants and parent) were grouped based on Unweighted Pair Group Method based on Arithmetic Average (UPGMA) clustering; squared Euclidean distances were used as the coefficients in UPGMA analysis. Principal components analysis (PCA) of morphological data was performed with R and the scatter biplot was drawn using the first two principal components. To study the association between individual SSR markers and the phenotypic traits, single marker analysis was performed by regressing individual phenotypic trait data on whole 0–1 binary marker data for each marker using SPSS 16.0.
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
Most of the current GWAS characterize the association between genotype and high-order phenotypes, such as complex traits or diseases. However, this association may be determined by a certain “black box” behind the causal link from genotype to phenotype. Such a black box is widely recognized as a series of regulatory processes that drive DNA to genes to proteins to metabolites. We argue that the black box may involve the mediation of the gut microbiota because of increasing evidence that the gut microbiota is associated with human phenotypes. Here, we can test whether microbial networks serves as a black box to modulate genotype-phenotype relationship. Let zi denote the high-order phenotypic value of a quantitative trait on individual i. We calculate the Pearson correlation between y and z across individuals, denoted as ryz, to quantify and test the association between the network property and host phenotype. Meanwhile, we use Huo et al.’s58 mutual information approach to calculate the correlations between (discrete) genotype (g) and (continuous) network variable (y) and high-order phenotypic trait (z), denoted as rgy and rgz, respectively.