Validation of reference genes for gene expression analysis in olive (Olea europaea) mesocarp tissue by quantitative real-time RT-PCR

BACKGROUND: Gene expression analysis using quantitative reverse transcription PCR (qRT-PCR) is a robust method wherein the expression levels of target genes are normalised using internal control genes, known as reference genes, to derive changes in gene expression levels. Although reference genes have recently been suggested for olive tissues, combined/independent analysis on different cultivars has not yet been tested. Therefore, an assessment of reference genes was required to validate the recent findings and select stably expressed genes across different olive cultivars. RESULTS: A total of eight candidate reference genes [glyceraldehyde 3-phosphate dehydrogenase (GAPDH), serine/threonine-protein phosphatase catalytic subunit (PP2A), elongation factor 1 alpha (EF1-alpha), polyubiquitin (OUB2), aquaporin tonoplast intrinsic protein (TIP2), tubulin alpha (TUBA), 60S ribosomal protein L18-3 (60S RBP L18-3) and polypyrimidine tract-binding protein homolog 3 (PTB)] were chosen based on their stability in olive tissues as well as in other plants. Expression stability was examined by qRT-PCR across 12 biological samples, representing mesocarp tissues at various developmental stages in three different olive cultivars, Barnea, Frantoio and Picual, independently and together during the 2009 season with two software programs, GeNorm and BestKeeper. Both software packages identified GAPDH, EF1-alpha and PP2A as the three most stable reference genes across the three cultivars and in the cultivar, Barnea. GAPDH, EF1-alpha and 60S RBP L18-3 were found to be most stable reference genes in the cultivar Frantoio while 60S RBP L18-3, OUB2 and PP2A were found to be most stable reference genes in the cultivar Picual. CONCLUSIONS: The analyses of expression stability of reference genes using qRT-PCR revealed that GAPDH, EF1-alpha, PP2A, 60S RBP L18-3 and OUB2 are suitable reference genes for expression analysis in developing Olea europaea mesocarp tissues, displaying the highest level of expression stability across three different olive cultivars, Barnea, Frantoio and Picual, however the combination of the three most stable reference genes do vary amongst individual cultivars. This study will provide guidance to other researchers to select reference genes for normalization against target genes by qPCR across tissues obtained from the mesocarp region of the olive fruit in the cultivars, Barnea, Frantoio and Picual

Statistical Modeling and Testing for Joint Association in Genome-Wide Association Studies

University of Minnesota Ph.D. dissertation. July 2015. Major: Biostatistics. Advisor: Saonli Basu. 1 computer file (PDF); ix, 139 pages.Most common human diseases are complex genetic traits, with multiple genetic and environmental components contributing to the disease susceptibility. Genome-wide Association Studies (GWASs) offer a powerful approach to identify the genetic variants (single nucleotide polymorphisms or SNPs) that modulate the susceptibility to these complex diseases. GWASs have identified hundreds of SNPs associated with such diseases, but these SNPs appear to explain very little of the genetic risk. This dissertation aims at investigating several alternative hypotheses for explaining the disease risk and develop statistical techniques to improve the power to detect SNPs influencing such diseases. A Bayesian dimension reduction model is developed to study the joint effect of a group of SNPs on the disease status for unrelated individuals. Modeling the joint effects of multiple SNPs can help in the detection of SNPs that jointly have significant risk effects but individually make only a small contribution. Thus, our method based on the proposed dimension reduction model, Bayesian partitioning model (BPM), may have improved power over multiple single-SNP association analysis when testing the association of multiple SNPs with a single binary trait. Similarly, joint analysis of multiple disease-related traits may also improve detection of SNPs associated with a disease. GWASs often collect data on multiple disease-related traits. These traits may share a common set of SNPs influencing them and a joint analysis of these traits may improve the power to detect these SNPs which may provide a better understanding of the underlying disease mechanism. Multivariate analysis of variance (MANOVA) can perform such an association analysis at a GWAS level. The behavior of MANOVA is investigated, both theoretically and using simulations, and the conditions where MANOVA loses power are derived. Based on these findings, a novel unified score-based association test (USAT) is proposed that adaptively uses the data to optimize power to detect association of a single SNP with multiple quantitative phenotypes/traits. This test and other such multivariate tests are based on the assumption of random sampling, and may suffer from severely inflated type I error in case of selected sampling. This motivated us to explore scenarios in which popular methods would fail to provide valid tests of the null hypothesis of no association of a single SNP with multiple traits within the framework of a case-control study. Two alternative hypothesis testing approaches (one based on maximum p-value and the other based on propensity score) are proposed for such scenarios.Ray, Debashree. (2015). Statistical Modeling and Testing for Joint Association in Genome-Wide Association Studies. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/190503

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power

Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021.

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs

A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

AbstractHeadache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)—Signal Transducer and Activator of Transcription 6 (STAT6)—Short chainDehydrogenase/Reductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10–62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10–9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.</jats:p

A meta-analysis of the genome-wide association studies on two genetically correlated phenotypes (self-reported headache and self-reported migraine) identifies four new risk loci for headaches (N=397,385)

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank cohort and the self-reported migraine phenotype from the 23andMe resource using the metaUSAT for genetically correlated phenotypes (N=397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and 4 loci were newly identified. The LRP1-STAT6-SDR9C7 region in chromosome 12 was the most significantly associated locus with a leading P value of 1.24 × 10−62 of rs11172113. The ONECUT2 gene locus in chromosome 18 was the strongest signal among the 4 new loci with a P value of 1.29 × 10−9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more new variants for headaches. This study has paved way for a large GWAS meta-analysis study involving cohorts of different, though genetically correlated headache phenotypes

Precision subclassification of type 2 diabetes:a systematic review

Background: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.Methods: We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.Results: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.Conclusion: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.</p

Precision subclassification of type 2 diabetes: a systematic review

Background: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. Methods: We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. Results: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. Conclusion: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes

Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke

Background and Objectives Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke. Methods We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS. Results We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008). Discussion The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.Peer reviewe