Customized Functional Variants Genotyping Array

A Functional Variants Genotyping Array refers to the chip that includes the loci that are all functional mutations that can affect the transcription levels or protein activity; compared with regular marker locus gene chips, Functional Variants Genotyping Arrays are more scientific in locus selection, which may directly impact the regulation of life processes and feature higher contents of functional information carried by loci.

The marker locus effects of the marker locus gene chips shall be determined by linked functional mutations. However, due to variations in the tightness of linkage in different generations and populations, the marker locus effects tend to be unstable, making it challenging to achieve data integration for genomic breeding efficiency improvement. Functional Variants Genotyping Arrays carry functional mutations, which can effectively bypass the limitations of linkage disequilibrium. The effects of mutation loci should be relatively stable in different populations, which is highly advantageous for multi-generational and multi-population data integration analysis and able to significantly enhance the efficiency of critical gene discovery and genomic breeding.

Compared with marker locus gene chips, Functional Variants Genotyping Arrays are closer to the "ideal gene chip". An ideal gene chip refers to the chip whose loci perfectly match all QTN loci of the studied phenotype. However, breeding target traits are often complex and influenced by minor polygene effects, making it difficult to design an ideal gene chip that captures all QTNs associated with the studied phenotype. However, it is certain that QTN must be a functional mutation. Therefore, compared with marker locus gene chips, Functional Variants Genotyping Arrays include more QTN loci in theory, making them closer to the ideal chip. Furthermore, compared with whole-genome sequencing, Functional Variants Genotyping Arrays may contain fewer QTNs, but their non-functional noise loci are also reduced greatly, and they have great advantages in terms of storage and computing costs. The relationships among the ideal gene chip, Functional Variants Genotyping Array, marker locus gene chip, and whole-genome sequencing are as follows (assuming the studied phenotype is controlled by 1000 QTN):

It is assumed that the phenotype studied is controlled by 1000 QTN

1.Genome Assembly

Primarily targeted at species without a reference genome. When designing a sequencing scheme, it is required take a diploid karyotype species as an example. If only consistent genome assembly is required, which is to select one copy from homologous chromosomes as a representative for assembly at the chromosomal level, a combination of PacBio HiFi+Hi-C+WGS technologies is at least necessary; If single haplotype genome assembly is required, in addition to the consistent genome assembly data, high-coverage WGS data from both the paternal and maternal lines are required.

2) Genomic Genetic Variation Detection

If the studied species lacks a high-quality genetic variation database or existing genetic variation information cannot effectively cover certain specific varieties, it is necessary to identify genomic genetic variations from scratch. Specific Method: Identify SNV, Indel, and SV of the studied species/variety using high-quality population genome resequencing data. Given the reduced sensitivity of detecting SV using second-generation sequencing data, representative individuals can be selected for third-generation PacBio HiFi resequencing to improve the efficiency of population SV detection.

3) Functional Genome Annotation

(1)Epigenome: Utilize the epigenome sequencing technologies, such as ATAC-seq and ChIp-seq/CUT&Tag, of specific tissues or various development stages of different tissues to comprehensively and accurately identify genomic regulatory elements and transcription factor binding motif.

(2) Conserved Elements of Genome Evolution: Genomic sequences that detect different conserved thresholds (completely conserved, highly conserved, and significantly conserved) in a collection of genomes from multiple species based on the concept of conservation.

4）Screening of Chip Candidate Functional Locus

Annotate and label genome-wide genetic variations, such as intergenic regions, introns, synonymous mutations, missense mutations, nonsense mutations, frame shift mutations, population's minimum allele frequency quantiles, regulatory elements, evolutionary conservation and candidate functional mutations. Combine all genomic feature weights and sequentially calculate the total score for genome-wide genetic variation features. Calculate the genomic haplotype group based on the genetic linkage information, and select the genetic variation with the highest feature score from each haplotype block as the tagged genetic variation and candidate variation locus for the gene chip.

5) Design and Evaluation of Whole-genome Capture Probe

Design a genome-wide probe sequence library by taking probe sequence length, GC content and specificity into account and predict the capture efficiency of all probes using deep learning models.

6) Determination of Chip Functional Locus and Capture Probe Sequencing

Design the first version of the functional gene chip for this species by taking chip locus feature score, genome representation, chip probe capturing efficiency, chip probe density, and chip size into account.

7) Breeding Evaluation and Iteration Optimization

Iteratively optimize chip loci and probe sequences based on the actual results from the first version of the functional gene chip population, including data on actual probe capture specificity and efficiency, locus integrity, polymorphism information content and genome evaluation accuracy, etc.