The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective

\ua9 2023 by the authors.Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications

Common polymorphism in H19 associated with birthweight and cord blood IGF-II levels in humans

Background: Common genetic variation at genes that are imprinted and exclusively maternally expressed could explain the apparent maternal-specific inheritance of low birthweight reported in large family pedigrees. We identified ten single nucleotide polymorphisms ( SNPs) in H19, and we genotyped three of these SNPs in families from the contemporary ALSPAC UK birth cohort ( 1,696 children, 822 mothers and 661 fathers) in order to explore associations with size at birth and cord blood IGF- II levels. Results: Both offspring's and mother's H19 2992C> T SNP genotypes showed associations with offspring birthweight ( P = 0.03 to P = 0.003) and mother's genotype was also associated with cord blood IGF-II levels ( P = 0.0003 to P = 0.0001). The offspring genotype association with birthweight was independent of mother's genotype ( P = 0.01 to P = 0.007). However, mother's untransmitted H19 2992T allele was also associated with larger birthweight ( P = 0.04) and higher cord blood IGF-II levels ( P = 0.002), suggesting a direct effect of mother's genotype on placental IGF-II expression and fetal growth. The association between mother's untransmitted allele and cord blood IGF-II levels was more apparent in offspring of first pregnancies than subsequent pregnancies ( P-interaction = 0.03). Study of the independent Cambridge birth cohort with available DNA in mothers (N = 646) provided additional support for mother's H19 2992 genotype associations with birthweight ( P = 0.04) and with mother's glucose levels ( P = 0.01) in first pregnancies. Conclusion: The common H19 2992T allele, in the mother or offspring or both, may confer reduced fetal growth restraint, as indicated by associations with larger offspring birth size, higher cord blood IGF-II levels, and lower compensatory early postnatal catch-up weight gain, that are more evident among mother's smaller first-born infants

Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response.

This article has been accepted for publication in Annals of the Rheumatic Diseases, 2020 following peer review, and the Version of Record can be accessed online at http://dx.doi.org/10.1136/annrheumdis-2020-217204OBJECTIVES: In this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis. METHODS: The genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the Interleukin 18 Receptor Accessory Protein (IL18RAP) gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression. RESULTS: We found that predicted expression of IL18RAP showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, IL18RAP expression in whole blood showed correlation with the change in ESR between baseline and follow-up (r=-0.35, p=0.0091). Change in ESR was also correlated with the expression of IL18RAP in synovial tissue (r=-0.28, p=0.02). CONCLUSION: Our results suggest that IL18RAP expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type

No Evidence That Genetic Variation at the Klotho Locus Is Associated With Longevity in Caucasians from the Newcastle 85+ Study and the UK Biobank

Copyright © The Author(s) 2021. The demographics of Western populations are changing, with an increase in the proportion of older adults. There is evidence to suggest that genetic factors may influence the aging process: studying these may lead to interventions to help individuals live a longer and healthier life. Evidence from several groups indicates that Klotho (KL), a gene encoding a single-pass transmembrane protein that acts as an FGF23 co-receptor, may be associated with longevity and healthy aging. We aimed to explore this area further by comparing the genotype counts in 642 long-lived individuals from the Newcastle 85+ Study with 18 295 middle-aged Newcastle-based controls from the UK Biobank to test whether variants at the KL gene locus are over- or under-represented in older individuals. If KL is associated with longevity, then we would expect the genotype counts to differ between the 2 cohorts. We found that the rs2283368 CC genotype and the rs9536338 C allele, but not the KL-VS haplotype, were associated with reaching very old age. However, these associations did not replicate in the remainder of the UK Biobank cohort. Thus, our results do not reliably support the role of KL as a longevity factor.Calico LLC (South San Francisco, California, United States); HAA is the recipient of a PhD studentship from the College of Health, Medical and Life Sciences, Brunel University London

Improved Statistics for Genome-Wide Interaction Analysis

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

<p>Abstract</p> <p>Background</p> <p>Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits.</p> <p>Findings</p> <p>Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run.</p> <p>Conclusions</p> <p>GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from <url>http://www.cceb.upenn.edu/~mli/software/GENIE/</url>.</p

Exome sequencing in dementia with Lewy bodies.

Dementia with Lewy bodies (DLB) is the second most common form of degenerative dementia. Siblings of affected individuals are at greater risk of developing DLB, but little is known about the underlying genetic basis of the disease. We set out to determine whether mutations in known highly penetrant neurodegenerative disease genes are found in patients with DLB. Whole-exome sequencing was performed on 91 neuropathologically confirmed cases of DLB, supplemented by independent APOE genotyping. Genetic variants were classified using established criteria, and additional neuropathological examination was performed for putative mutation carriers. Likely pathogenic variants previously described as causing monogenic forms of neurodegenerative disease were found in 4.4% of patients with DLB. The APOE ɛ4 allele increased the risk of disease (P=0.0001), conferred a shorter disease duration (P=0.043) and earlier age of death (P=0.0015). In conclusion, although known pathogenic mutations in neurodegenerative disease genes are uncommon in DLB, known genetic risk factors are present in >60% of cases. APOE ɛ4 not only modifies disease risk, but also modulates the rate of disease progression. The reduced penetrance of reported pathogenic alleles explains the lack of a family history in most patients, and the presence of variants previously described as causing frontotemporal dementia suggests a mechanistic overlap between DLB and other neurodegenerative diseases.This study was funded by the NHS National Institute of Health Research Biomedical Research Unit for Lewy body dementia at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. Tissue for this study was provided by Newcastle Brain Tissue Resource, which is funded in part by a grant from the UK Medical Research Council and by Brains for Dementia Research, a joint venture between Alzheimer’s Society and Alzheimer’s Research UK. MJK is a Wellcome Trust Clinical Research Training Fellow. PFC is a Wellcome Trust Senior Fellow in Clinical Science and National Institute for Health Research Senior Investigator. He receives funding from the Medical Research Council and the National Institute for Health Research Biomedical Research Centre for Ageing and Age-Related Disease award to the Newcastle upon Tyne Foundation Hospitals National Health Service Trust. The funding sources had no role in study design, data collection/analysis, the writing of the paper or the decision of when or where to publish it. The views expressed here are the views of the authors and not necessarily those of the NHS, NIHR or the Department of Health.This is the final published version. It first appeared at http://www.nature.com/tp/journal/v6/n2/full/tp2015220a.html

AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.</p> <p>Findings</p> <p>AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.</p> <p>Conclusions</p> <p>AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from <url>http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html</url>.</p

Discovering joint associations between disease and gene pairs with a novel similarity test

Genes in a functional pathway can have complex interactions. A gene might activate or suppress another gene, so it is of interest to test joint associations of gene pairs. To simultaneously detect the joint association between disease and two genes (or two chromosomal regions), we propose a new test with the use of genomic similarities. Our test is designed to detect epistasis in the absence of main effects, main effects in the absence of epistasis, or the presence of both main effects and epistasis. Results: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's chi(2) test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes. Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson 's chi(2) test. Conclusions: In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's chi(2) test