Dataset Information

Network Analysis of Linkage Disequilibrium Reveals Genome Architecture in Chum Salmon.

ABSTRACT: Many studies exclude loci that exhibit linkage disequilibrium (LD); however, high LD can signal reduced recombination around genomic features such as chromosome inversions or sex-determining regions. Chromosome inversions and sex-determining regions are often involved in adaptation, allowing for the inheritance of co-adapted gene complexes and for the resolution of sexually antagonistic selection through sex-specific partitioning of genetic variants. Genomic features such as these can escape detection when loci with LD are removed; in addition, failing to account for these features can introduce bias to analyses. We examined patterns of LD using network analysis to identify an overlapping chromosome inversion and sex-determining region in chum salmon. The signal of the inversion was strong enough to show up as false population substructure when the entire dataset was analyzed, while the effect of the sex-determining region on population structure was only obvious after restricting analysis to the sex chromosome. Understanding the extent and geographic distribution of inversions is now a critically important part of genetic analyses of natural populations. Our results highlight the importance of analyzing and understanding patterns of LD in genomic dataset and the perils of excluding or ignoring loci exhibiting LD. Blindly excluding loci in LD would have prevented detection of the sex-determining region and chromosome inversion while failing to understand the genomic features leading to high-LD could have resulted in false interpretations of population structure.

SUBMITTER: McKinney G

PROVIDER: S-EPMC7202013 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Network Analysis of Linkage Disequilibrium Reveals Genome Architecture in Chum Salmon.

McKinney Garrett G McPhee Megan V MV Pascal Carita C Seeb James E JE Seeb Lisa W LW

G3 (Bethesda, Md.) 20200504 5

Many studies exclude loci that exhibit linkage disequilibrium (LD); however, high LD can signal reduced recombination around genomic features such as chromosome inversions or sex-determining regions. Chromosome inversions and sex-determining regions are often involved in adaptation, allowing for the inheritance of co-adapted gene complexes and for the resolution of sexually antagonistic selection through sex-specific partitioning of genetic variants. Genomic features such as these can escape det ...[more]

PMID: 32165371

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Project description:MotivationThe completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction.ResultsWe propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data.Availability and implementationData are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Dataset Information

Network Analysis of Linkage Disequilibrium Reveals Genome Architecture in Chum Salmon.

Publications

Network Analysis of Linkage Disequilibrium Reveals Genome Architecture in Chum Salmon.

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