Computational and Experimental Analysis of Genetic Variants

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Computational and Experimental Analysis of Genetic Variants

Jeremy W. Prokop, Vladislav Jdanov, Lane Savage, Michele Morris, Neil Lamb, Elizabeth VanSickle, Cynthia L. Stenger, Surender Rajasekaran, Caleb P. Bupp

10.1002/cphy.c210012

Source: Volume 12, Issue 2, April 2022

Published online: March 2022

Full Article on Wiley Online Library

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Abstract

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome‐Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio‐based sequencing (parents and probands). However, the genomic associations require follow‐up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303‐3336, 2022.

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Jeremy W. Prokop, Vladislav Jdanov, Lane Savage, Michele Morris, Neil Lamb, Elizabeth VanSickle, Cynthia L. Stenger, Surender Rajasekaran, Caleb P. Bupp. Computational and Experimental Analysis of Genetic Variants. Compr Physiol 2022, 12: 3303-3336. doi: 10.1002/cphy.c210012

	Figure 1. Sequencing to association studies for rare and common variants. Created with BioRender. Genomics can be performed by SNP arrays or genome/exome sequencing chemistries, followed by various statistical strategies for rare or common variants.
	Figure 2. EBI/NHGRI GWAS catalog. (A) Associations per year. (B) The number of associations per trait is ranked based on the number of associations. Data was pulled from the GWAS catalog on March 6, 2021.
	Figure 3. Population variants and linkage disequilibrium for rs12126142. The population allele frequency is listed to the left in diverse populations based on gnomAD. Below rs12126142 is listed the chromosome annotation (chromosome_location_wildtype_minor allele). LD was imputed from the 1000 genomes project phase 3 using European imputation.
	Figure 4. GTEx v8 eQTL and sQTL analysis. (A) The number of genes with GTEx annotated significant eQTLs (blue) or sQTLs (red) in each of 49 different tissues. (B) A volcano plot of log2 fold change (x‐axis) relative to the −log10 p‐value (y‐axis) for all significant eQTLs. (C) A plot of mean allele frequency (MAF) relative to the ‐Log10 p‐value (y‐axis) for all significant sQTLs.
	Figure 5. The eQTL for PSPHP1 on chromosome 7. (A) Violin plot rs6593279 in skeletal muscle tissue for each genotype. (B) LD analysis for rs6593279 using 1000 genomes project phase 3 East Asian imputation.
	Figure 6. GTEx sQTLs for rs56105022 on CNIH4. (A) Violin plots for variants in skeletal muscle and cultured fibroblasts. (B) Exons of CNIH4 with splicing shown. In red is the alternative splicing event of panel A with the resulting splicing isoform shown below in red. (C) The genome browser view of the splicing site (red) in respect to the exon structure and chromosome positions. (D) The Ensembl table of isoforms with the resulting splice site highlighted, which does not code for a protein. (E) LD imputed from the 1000 genomes project phase 3 using European imputation for rs56105022.
	Figure 7. Representative plot of colocalized (A) or not colocalized (B) associations between two traits. The black line represents the significance cutoff.
	Figure 8. Epigenetic regulation dataset overlap to association regions. (A) Schematic of various epigenetic insights measured in ENCODE. (B) Representative GWAS hit in red box looking at the overlap of peaks for a transcription factor (TF, blue) or histone modification (red) relative to LD block for the site. Created with BioRender.
	Figure 9. Noncoding RNA. (A) A breakdown of the biotype annotations within the Gencode38 database. The top 6 groups are labeled with the percentage of total transcripts. (B) The number of transcripts for several of the noncoding RNA groups. (C) The percent of transcripts in each biotype that have publications (gray) or do not have publications (red). (D) The size of long noncoding RNA (lncRNA) transcripts. (E‐F) The number of disease associations (E) or VEP (F) annotated consequences for various noncoding RNA found within the ncRNAVar database (www.liwzlab.cn/ncrnavar/ncrnavar.html).
	Figure 10. Missense variants in GWAS LD blocks. (A) Plot of GWAS associations (x‐axis) from the GWAS catalog and the top‐ranked p‐value (y‐axis) for that variant. Outlier missense variants are labeled. (B) The ALDH2 missense variant E504K.
	Figure 11. Alzheimer's Disease GWAS. (A) All SNPs in GWAS database for Alzheimer's Disease showing the number of LD SNPs (x‐axis) and the significance (y‐axis). (B) rs429358 allele frequencies in gnomAD populations. (C) LD imputed from the 1000 genomes project phase 3 using African imputation for rs429358. (D) rs429358 results in the missense variant APOE C130R.
	Figure 12. Stroke GWAS. (A) All SNPs in GWAS database for stroke showing the number of LD SNPs (x‐axis) and the significance (y‐axis). (B) rs6025 allele frequencies in gnomAD populations. (C) LD imputed from the 1000 genomes project phase 3 using American imputation for rs6025. (D) rs6025 results in the missense variant F5 R534Q.
	Figure 13. Chronic Kidney Disease (CKD). (A) All SNPs in GWAS database for Chronic Kidney Disease (CKD) showing the number of LD SNPs (x‐axis) and the significance (y‐axis). (B) rs2147896 allele frequencies in gnomAD populations. (C) LD imputed from the 1000 genomes project phase 3 using African imputation for rs2147896. (D) rs2147896 results in an eQTL for PYROXD2. (E) rs17319721 allele frequencies in gnomAD populations. (F) LD imputed from the 1000 genomes project phase 3 using American imputation for rs17319721. (G) rs17319721 results in altered gene regulation of a shortened SHROOM3 isoform through disruption of TCF7L2 binding and looping between an enhancer and promoter.
	Figure 14. *Inheritance and de novo* rare variants of the genome*. 63 is a representative number of de novo* variants, with each individual of the pedigree have added variants in subsequent generations.
	Figure 15. Large‐scale population whole genomes completed in gnomAD and TOPMed. Data extracted on March 8, 2021.
	Figure 16. ClinVar variants (A) Clinical annotation of 774,966 ClinVar variants. (B) Molecular type of 774,966 ClinVar variants. (C) Percent of each molecular type that falls into pathogenic (red) or VUS (cyan) annotations. Data were extracted from the UCSC Genome Browser on October 14, 2020.
	Figure 17. ClinVar genes. (A) Plot of the number of genes within the grouped pathogenic (red) or VUS (cyan) variants. (B) Scatter plot of the number of pathogenic (x‐axis) vs. VUS (y‐axis) with the top genes labeled.
	Figure 18. Nonsense‐mediated decay (NMD). As it reads mRNA, the ribosome (cyan/green) can remove RNA interacting proteins (magenta) that form exon junction complexes (EJC) near splice sites. When a nonsense variant arises, the 3′UTR is enlarged, with the accumulation of decay‐inducing complex proteins (red) that initiate the mRNA's degradation to prevent partial protein production. Created with BioRender.
	Figure 19. Trio genomes: from variants to narrowed list. Created with BioRender.
	Figure 20. ACMG59 STRING plot. The 59 genes were processed with the STRING tools (https://string‐db.org/) for known associations and enrichments in the genes. Enriched pathways are labeled at the top, and colors of gene nodes correspond to terms.
	Figure 21. Narrowing rare missense variants in SHROOM3 with pathogenic outcomes in Chronic Kidney Disease. Modified, with permission, from Prokop JW, et al., 2018 124.
	Figure 22. ABCC8 evolution. (A) Phylogenic tree of 221 sequences for ABCC8. Human is the red box. Values at the nodes represent the clustering of 50 bootstrapped trees. (B) Conservation score for each codon. (C) Conservation score on a 21‐codon sliding window, where each site is added to the scores of 10 before and 10 codons after to smooth out enriched motifs/domains. (D) Pictoral of the transmembrane domains and the ATP binding sites. (E) Conservation of the two ATP binding site amino acids. Amino acids in red are those annotated in UniProt for association with familial hyperinsulinemic hypoglycemia.
	Figure 23. Screening variants for ABCC8 using protein model and variant analysis workflow.
	Figure 24. Viral Induced Genetics and RNASEH2B. (A) NMD of RNASEH2B alleles. (B) Pictorial of viral inhibition of NMD. (C) Viral activation of dominant‐negative genetics of patriation RNASEH2B protein inhibiting the protein complex. It was created with BioRender.

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Article Title:	Computational and Experimental Analysis of Genetic Variants
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