Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease

A Novel Missense SNP in the Fatty Acid-Binding Protein 4 (FABP4) Gene is Associated with Growth Traits in Karakul and Awassi Sheep

The fatty acid-binding protein 4 (FABP4) plays a crucial role in the transportation and metabolism of fatty acids. It binds to long-chain fatty acids and facilitates their transport within cells. FABP4 is involved in lipid metabolism, insulin sensitivity, inflammation, and energy homeostasis. This study was conducted to assess the association between the FABP4 gene and growth traits in Karakul and Awassi sheep. A PCR-single strand conformation polymorphism (SSCP) protocol was utilized to assess the polymorphism of FABP4 PCR products with growth traits measured at birth, three, six, nine, and twelve-month intervals. One non-synonymous SNP was identified in the second exon, in which threonine was converted to aspartate in the 61st position in FABP4 (p.61Thr > Asp). This novel SNP showed significant associations with all growth traits measured at all age intervals. The results showed that lambs with the TT genotype exhibited higher growth traits than those with the GT and GG genotypes, respectively. The conducted prediction showed a clearly deleterious effect of p.61Thr > Asp on FABP4 structure, which was accompanied by reduced fatty acid binding efficiency. Owing to the predicted damaging effects caused by p.61Thr > Asp on FABP, lower levels of lipid transport and its consequent increased weight gain and other growth trait indices are expected. Therefore, a putative mechanism through which lambs with these genotypes exhibit higher growth traits is proposed. The FABP4 gene is suggested as a promising marker to improve growth traits in Karakul and Awassi sheep. However, more research is required to validate this mechanism.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

A missense SNP in the proopiomelanocortin (POMC) gene is associated with growth traits in Awassi and Karakul sheep

This study was conducted to assess the association between proopiomelanocortin (POMC) gene and growth traits in Awassi and Karakul sheep. PCR-single strand conformation polymorphism (SSCP) method was utilized to assess the polymorphism of POMC PCR amplicons with body weight and length, wither and rump height, chest and abdominal circumference measured at birth, 3, 6, 9, and 12 months intervals. Only one missense SNP (rs424417456:C > A) was detected in exon-2, in which glycine was converted to cysteine in the 65th position in POMC (p.65Gly > Cys). rs424417456 SNP showed significant associations with all growth traits in the third, sixth, ninth, and twelfth months. At the age of 3 months onward, lambs with CC genotype showed higher body weight, body length, wither and rump heights, and chest and abdominal circumferences than lambs with CA and AA genotypes, respectively. Prediction analyses indicated a deleterious effect of p.65Gly > Cys on POMC structure, function, and stability. Owing to the strong correlation between rs424417456:CC and better growth characteristics, this genotype is proposed as a promising marker to enhance growth traits in Awassi and Karakul sheep. The predicted damaging effects caused by rs424417456:CA and rs424417456:AA genotypes may entail a putative mechanism through which lambs with these genotypes exhibit lower growth traits.

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Association of chronic wasting disease susceptibility with prion protein variation in white-tailed deer (Odocoileus virginianus)

Chronic wasting disease (CWD) is caused by prions, infectious proteinaceous particles, PrP^CWD. We sequenced the PRNP gene of 2,899 white-tailed deer (WTD) from Illinois and southern Wisconsin, finding 38 haplotypes. Haplotypes A, B, D, E, G and 10 others encoded Q₉₅G₉₆S₁₀₀N₁₀₃A₁₂₃Q₂₂₆, designated ‘PrP variant A.’ Haplotype C and five other haplotypes encoded PrP ‘variant C’ (Q₉₅S₉₆S₁₀₀N₁₀₃A₁₂₃Q₂₂₆). Haplotype F and three other haplotypes encoded PrP ‘variant F’ (H₉₅G₉₆S₁₀₀N₁₀₃A₁₂₃Q₂₂₆). The association of CWD with encoded PrP variants was examined in 2,537 tested WTD from counties with CWD. Relative to PrP variant A, CWD susceptibility was lower in deer with PrP variant C (OR = 0.26, p < 0.001), and even lower in deer with PrP variant F (OR = 0.10, p < 0.0001). Susceptibility to CWD was highest in deer with both chromosomes encoding PrP variant A, lower with one copy encoding PrP variant A (OR = 0.25, p < 0.0001) and lowest in deer without PrP variant A (OR = 0.07, p < 0.0001). There appeared to be incomplete dominance for haplotypes encoding PrP variant C in reducing CWD susceptibility. Deer with both chromosomes encoding PrP variant F (FF) or one encoding PrP variant C and the other F (CF) were all CWD negative. Our results suggest that an increased population frequency of PrP variants C or F and a reduced frequency of PrP variant A may reduce the risk of CWD infection. Understanding the population and geographic distribution of PRNP polymorphisms may be a useful tool in CWD management.

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data

Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.

Altered Protein Function Caused by AMD-associated Variant rs704 Links Vitronectin to Disease Pathology

Purpose

Vitronectin, a cell adhesion and spreading factor, is suspected to play a role in the pathogenesis of age-related macular degeneration (AMD), as it is a major component of AMD-specific extracellular deposits (e.g., soft drusen, subretinal drusenoid deposits). The present study addressed the impact of AMD-associated non-synonymous variant rs704 in the vitronectin-encoding gene VTN on vitronectin functionality.

Methods

Effects of rs704 on vitronectin expression and processing were analyzed by semi-quantitative sequencing of VTN transcripts from retinal pigment epithelium (RPE) cells generated from human induced pluripotent stem cells (hiPSCs) and from human neural retina, as well as by western blot analyses on heterologously expressed vitronectin isoforms. Binding of vitronectin isoforms to retinal and endothelial cells was analyzed by western blot. Immunofluorescence staining followed extracellular matrix (ECM) deposition in cultured RPE cells heterologously expressing the vitronectin isoforms. Adhesion of fluorescently labeled RPE or endothelial cells in dependence of recombinant vitronectin or vitronectin-containing ECM was investigated fluorometrically or microscopically. Tube formation and migration assays addressed effects of vitronectin on angiogenesis-related processes.

Results

Variant rs704 affected expression, secretion, and processing but not oligomerization of vitronectin. Cell binding and influence on RPE-mediated ECM deposition differed between AMD-risk-associated and non-AMD-risk-associated protein isoforms. Finally, vitronectin affected adhesion and endothelial tube formation.

Conclusions

The AMD-risk-associated vitronectin isoform exhibits increased expression and altered functionality in cellular processes related to the sub-RPE aspects of AMD pathology. Although further research is required to address the subretinal disease aspects, this initial study supports an involvement of vitronectin in AMD pathogenesis.

Assessing predictions of the impact of variants on splicing in CAGI5

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Over the last 10 years, genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for autoimmune diseases. However, despite increasing power for the detection of both common and rare coding variants affecting disease susceptibility, a large fraction of disease heritability has remained unexplained. In addition, a majority of the identified loci are located in noncoding regions, and translation of disease-associated loci into new biological insights on the etiology of immune disorders has been lagging. This highlights the need for a better understanding of noncoding variation and new strategies to identify causal genes at disease loci. In this review, I will first detail the molecular basis of gene expression and review the various mechanisms that contribute to alter gene activity at the transcriptional and post-transcriptional level. I will then review the findings from 10 years of functional genomics studies regarding the genetics on gene expression, in particular in the context of infection. Finally, I will discuss the extent to which genetic variants that modulate gene expression at transcriptional and post-transcriptional level contribute to disease susceptibility and present strategies to leverage this information for the identification of causal mechanisms at disease loci in the era of whole genome sequencing.

Consensus Genome-Wide Expression Quantitative Trait Loci and Their Relationship with Human Complex Trait Disease

Most of the risk loci identified from genome-wide association (GWA) studies do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent expression quantitative trait locus (eQTL) association studies have provided information on genetic factors associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. To address this issue, we have generated a set of consensus eQTLs by integrating publicly available data for specific human populations and cell types. Overall, we find over 4000 genes that are involved in high-confidence eQTL relationships. To elucidate the role that eQTLs play in human common diseases, we matched the high-confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium GWA study and follow-up studies for 7 human complex trait diseases-bipolar disorder (BD), coronary artery disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The results show that the data are consistent with ∼50% of these disease loci arising from an underlying expression change mechanism.

CAGI4 Crohn's exome challenge: Marker SNP versus exome variant models for assigning risk of Crohn disease

Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn's Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of genome wide association studies marker SNPs. We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.