The Ultimate Guide to Whole Genome Resequencing: Basic and Adva

• Posted by Samantha James April 28, 2020 Filed in Business 231 views
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  3.3 Resequencing QTL positioning (used in plants)

  The research object selected by this method can be inbred recombinant line groups (RILs) or F2 generation groups obtained after the cross of a pair of varieties with extreme traits. 20-50 individuals with two different extreme traits in the selected group construct DNA separately. After pooling, re-sequencing is performed, and the QTL is located by comparing a parameter (SNP-index) related to the sequencing depth of the SNP sites of the two pools. The method can be used for population genetics research, and can quickly identify chromosomal regions where artificial breeding and natural selection occur.

  Two varieties with opposite phenotypes on the same trait were used as parents. F2 generation was obtained after crossing, and then self-breeding to F7 generation through single seed transmission to obtain inbred recombinant populations (RILs). If the trait in the population conforms to a normal distribution, it means that the locus associated with this trait is a quantitative trait, which can be analyzed by QTL. Here, we select the individuals with the most obvious and least obvious phenotypes of this trait in this group as the research object. Generally, 20-50 individuals are mixed separately to generate two pools, which are the strongest phenotype pool and the weakest pool. Then re-sequence the two mixed pools separately. The sequencing platform used is Illumina Genome Analyzer IIx, and the sequencing depth is generally greater than 6 ×. The two pools should represent the two alleles of a certain genomic region. From this, we observed the unequal performance in the genomes from the two parents to identify the genomic regions containing QTL that caused the differences in the traits of the two pools.

  Compared with plants, it takes a long time to build a group for reasons such as the reproduction rate and genetic cycle of domesticated animals.

  3.4 Domestication process and population historical dynamics (Population inheritance (selection pressure analysis))

  By comparing the genomes of wild species and domesticated species, you can find genes and regions related to domestication.

  Liu Jianquan and his team from Lanzhou University in China, as well as researchers from research institutions such as the University of St Andrews in the United Kingdom and Utrecht University in the Netherlands, sequenced and compared the genome-wide genetic variation map analysis of wild yaks and domestic yaks in 26 regions of China, People had domesticated wild yaks on the Qinghai-Tibet Plateau in the early Neolithic as early as 7300 years ago, and the number of domestications increased by about 6 times before 3600 years. The study also estimated that the massive increase in yak populations and the spread of human populations in this geographical area during the late Holocene occurred simultaneously.

  The research team found that the domestic yak genome showed signs of genetic selection: about 200 genes were artificially domesticated, and these choices may affect animal behavior, especially docility. This domesticated gene is also very similar to that found in dogs and other domesticated animals (Qiu et al. 2015).

  3.5 Origin, domestication, GWAS, and population evolution (population genetics (selection pressure analysis))

  The research on the origin and domestication process of species will be right.

  Example: A total of 58 canine animals distributed in Europe, Africa, Southern and Northern East Asia, Central Asia, Siberia, and America, including 12 gray wolves, 27 soil dogs, and 19 dogs of different breeds, were averaged 15 × Sequencing depth resequencing.

  3.6 Conduct somatic mutation or germ cell mutation research (for tumor, cancer, excellent phenotype of perennial plants, etc.)

  Therefore, we need to do an in-depth analysis in studying the origin of these mutations, how the mutations are affected by the DNA repair mechanism, and the laws of mutations during disease development and evolution. Natural selection generally plays a role in two aspects, namely, retaining mutations that are conducive to disease development and evolution, while limiting mutations in important functional regions of the genome, such as transcriptional regulatory regions and protein-coding regions. Therefore, (1) if the experimental design is to compare primary disease with normal control, systematic analysis can analyze the possible mechanisms and natural selection factors of complex diseases in the process of forming mutations. (2) If the experimental design is based on samples of lesions and their metastasis or adjacent locations, we can build a model of mutation evolution and metastasis to analyze the dynamic mode of mutation and the pattern of unstable state mutations in the genome.

  Hereditary tumors-germline mutation detection

  Looking for targeted drugs, tumor burden monitoring, etc.-somatic mutation detection

  High-throughput sequencing to identify de novo's somatic and germ line mutations, structural variation-SNV, including rearrangement mutations (deletion, duplication and copy number variation); analysis of SNP functionality: we will analyze the relationship between gene function (including miRNA), recombination rate, loss of heterozygosity (LOH) and evolutionary selection and mutation; and how these relationships will make the disease (cancer) produces corresponding susceptibility mechanisms and functions. We will explore disease genomes and cancer genomes on a comprehensive level of genomics, comparative genomics, and population genetics. (Erquiaga et al. 2014)