Tuesday, 12 May 2015


Biotechnology is a technological application that uses biological systems, living organisms or derivatives thereof, to make or modify products or processes for specific use. One of its chief applications nowadays is QTL analysis which deals with quantitative trait loci mapping.
First of all, we should have some knowledge about the Quantitative Trait Loci (QTLs) which are stretches of DNA containing or linked to the genes that underlie a quantitative trait. Quantitative traits refer to phenotypes (characteristics) that vary in degree and can be attributed to polygenic effects, i.e., product of two or more genes, and their environment. An example of a polygenic trait is human skin colour variation. Moreover, a single phenotypic trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait. These QTLs are often found on different chromosomes. Knowing the number of QTLs that explains variation in the phenotypic trait tells us about the genetic architecture of a trait. It may tell us that plant height is controlled by many genes of small effect, or by a few genes of large effect.
QTL analysis is a statistical method that links two types of information — phenotypic data (trait measurements) and genotypic data (usually molecular markers) — in an attempt to explain the genetic basis of variation in complex traits. QTL analysis allows researchers in fields as diverse, as agriculture, evolution, and medicine to link certain complex phenotypes to specific regions of chromosomes. The goal of this process is to identify the action, interaction, number, and precise location of these regions. 
In order to begin a QTL analysis, scientists require two things. First, they need two or more strains of organisms that differ genetically with regard to the trait of interest. For example, they might select lines fixed for different alleles influencing egg size (one large and one small). Second, researchers also require genetic markers that distinguish between these parental lines. Molecular markers are preferred for genotyping, because these markers are unlikely to affect the trait of interest. Several types of markers are used, including single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs, or microsatellites), restriction fragment length polymorphisms (RFLPs), and transposable element positions. Then, to carry out the QTL analysis, the parental strains are crossed, resulting in heterozygous (F1) individuals, and these individuals are then crossed using one of a number of different schemes. Finally, the phenotypes and genotypes of the derived (F2) population are scored. Markers that are genetically linked to a QTL influencing the trait of interest will segregate more frequently with trait values (large or small egg size in our example), whereas unlinked markers will not show significant association with phenotype.
The principal goal of QTL analysis has been to answer the question of whether phenotypic differences are primarily due to a few loci with fairly large effects, or to many loci, each with minute effects, thus working for the improvement of crop qualities and developing disease treatments. It appears that a substantial proportion of the phenotypic variation in many quantitative traits can be explained with few loci of large effect, with the remainder due to numerous loci of small effect. Once QTL have been identified, molecular techniques can be employed to narrow the QTL down to candidate genes. One important emerging trend in these analyses is the prominent role of regulatory genes, or genes that code for transcription factors and other signaling proteins. For instance, in rice, three flowering time QTL have been identified at the molecular level, and all of these loci encode regulatory proteins known from studies of Arabidopsis thaliana.
The prospect of QTL analysis is dazzling in no doubt. Genome-wide association studies (GWAS) are becoming increasingly popular in genetic research, and they are an excellent complement to QTL mapping. Whereas QTL contains many linked genes, which are then challenging to separate, GWAS produce many unlinked individual genes or even nucleotides, but these studies are riddled with large expected numbers of false positives. Though GWAS remain limited to organisms with genomic resources, combining the two techniques can make the most of both approaches and help provide the ultimate deliverable: individual genes or even nucleotides that contribute to the phenotype of interest.
Indeed, combining different QTL techniques and technologies has great promise. For example, some biotechnologists used data on gene expression in fat and kidney tissue from two previously generated, recombinant rat strains to study hypertension. Alternatively, samples adapted to different environments may be compared, or other populations of interest might be selected for expression analysis. This approach permits measurement of hundreds or even thousands of traits simultaneously. Differences in expression may be co-localized with phenotypic QTL that have been previously determined to create manageable lists of positional candidate genes. Other interesting questions concerning gene regulation can be addressed by combining expression or eQTL and QTL, such as the relative contributions of cis-regulatory elements versus trans-regulatory elements. These integrated approaches will become more common, and they promise a deeper understanding of the genetic basis of complex traits, including diseases. Integrating phenotypic QTL with protein QTL can also give investigators a more direct link between genotype and phenotype via co-localization of candidate protein abundance with a phenotypic QTL. Still more kinds of data can be integrated with QTL mapping for a “total information” genomics approach (e.g., eQTL, proteomics, and SNPs).
QTL studies have a rich history and have played an important role in gene cloning and characterization; however, there is still a great deal of work to be done. The existing data on model organisms needs to be expanded to the point at which meta-analysis is feasible in order to document robust trends regarding genetic architecture. Data generated by lab-based QTL studies can also be used to direct and inform other efforts, such as population genomics, wherein a large number of molecular markers are scored in the attempt to identify targets of selection and thus genes underlying ecologically important traits. Furthermore, QTL studies can inform functional genomics, in which the goal is to characterize allelic variation and how it influences the fitness and function of whole organisms. Thus, although the map between genotype and phenotype remains difficult to read, QTL analysis and a variety of associated innovations will likely to continue providing key landmarks.

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