KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/kmitl/1

Browse

Settings

Subject: search.filters.subject.Population structure

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Stock identification of short mackerel (Rastrelliger brachysoma) in Thai waters based on truss network system
    (2023-07-01)
    Thotsagotphairee, C.
    ;
    Kanloung, T.
    ;
    Viboonkit, K.
    ;
    Tavitchasri, P.
    ;
    Thongpradub, W.
    The results of discriminant function analysis (DFA) indicated the first two discriminant function (DF1 and DF2) accounted for 42.6% and 25.3%, respectively. The result of the stepwise discriminant function analysis revealed 16 characters from truss network system. Different characters of the DF1 were observed in head-related characters (1-17), anterior part of the body (2-3), posterior part of body (4-5, 15-16), and caudal trait (9-12). The DF2 showed the different characters in anterior part of the body (3-16), posterior part of body (5-16), and caudal trait (6-15, 6-14). According to the discriminant function analysis, 53.0% of the original groups were correct classification into their own populations. The plotting discriminant function exhibited the intermingling in all demographic groups. This morphometric study of Rastrelliger brachysoma indicated that short mackerel in Thai waters: the Gulf of Thailand and the Andaman Sea, had a common ancestor's origin.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    PCA-based informative SNP selection for analyzing population structure
    (2016-12-19)
    Limpiti, Tulaya
    ;
    Intarapanich, Apichart
    ;
    Tongsima, Sissades
    Phenotypic differences among individuals of the same species are the result of a set of genetic variations which can be observed in the DNA sequence. To conduct a population genetic study, a high throughput genotyping platform such as Single Nucleotide Polymorphism (SNP) array is popularly used to obtain a large set of SNPs for each individual. However, analyzing today's genotypic data can be computationally expensive due to its large size and complexity. Faulty substructure may also be detected if the data is noisy from redundant or non-informative SNPs. Considerable efforts have been done to extract a smaller informative SNP subset that still represents the same intrinsic structure of populations within a data set as the full panel of SNPs. This work describes a foundation of a PCA-based informative marker selection technique. The proposed technique is simple and efficient. It improves upon another spectral analysis technique called PCA-correlated SNPs. A new informativeness score based on a basis function expansion of the SNP variation patterns across individuals is introduced. Such score is computed for each SNP to select a subset of SNPs with the best scores. Using a bovine data set, we demonstrate that our technique is superior to the PCAcorrelated SNPs method, which requires accurate rank estimation to perform well. In contrast, our method is robust to the assumed rank of the data. High data representation accuracy is also achieved after a significant reduction of the number of SNPs while retaining information about the underlying population structure from the original data.
      1