Haplotype reconstruction error as a classical misclassification problem

Statistically reconstructing haplotypes from single nucleotide polymorphism (SNP) genotypes, can lead to falsely classified haplotypes. This can be an issue when interpreting haplotype association results or when selecting subjects with certain haplotypes for subsequent functional studies. It was our aim to quantify haplotype reconstruction error and to provide tools for it. By numerous simulation scenarios, we systematically investigated several error measures, including discrepancy, error rate, and R(2), and introduced the sensitivity and specificity to this context. We exemplified several measures in the KORA study, a large population-based study from Southern Germany. We find that the specificity is slightly reduced only for common haplotypes, while the sensitivity was decreased for some, but not all rare haplotypes. The overall error rate was generally increasing with increasing number of loci, increasing minor allele frequency of SNPs, decreasing correlation between the alleles and increasing ambiguity. We conclude that, with the analytical approach presented here, haplotype-specific error measures can be computed to gain insight into the haplotype uncertainty. This method provides the information, if a specific risk haplotype can be expected to be reconstructed with rather no or high misclassification and thus on the magnitude of expected bias in association estimates. We also illustrate that sensitivity and specificity separate two dimensions of the haplotype reconstruction error, which completely describe the misclassification matrix and thus provide the prerequisite for methods accounting for misclassification

Recombinant haplotypes narrow the ARMS2/HTRA1 association signal for age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of blindness in ageing societies, triggered by both environmental and genetic factors. The strongest genetic signal for AMD with odds ratios of up to 2.8 per adverse allele was found previously over a chromosomal region in 10q26 harboring two genes, ARMS2 and HTRA1, although with little knowledge as to which gene or genetic variation is functionally relevant to AMD pathology. In this study, we analyzed rare recombinant haplotypes in 16,144 AMD cases and 17,832 controls from the International AMD Genomics Consortium and identified variants in ARMS2 but not HTRA1 to exclusively carry the AMD risk with P-values between 10 × 10-773 and 6.7 × 10-5. This now allows prioritization of the gene of interest for subsequent functional studies.</p

Chronic Kidney Disease: Novel Insights from Genome-Wide Association Studies

Chronic kidney disease (CKD) is common, affecting about 10% of the general population, and causing significant morbidity and mortality. Apart from the risk conferred by traditional cardiovascular risk factors, there is a strong genetic component. The method of a genome-wide association study (GWAS) is a powerful hypothesis-free approach to unravel this component by association analyses of CKD with several million genetic variants distributed across the genome. Since the publication of the first GWAS in 2005, this method has led to the discovery of novel loci for numerous human common diseases and phenotypes. Here, we review the recent successes of meta-analyses of GWAS on renal phenotypes. UMOD, SHROOM3, STC1, LASS2, GCKR, ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, PRKAG2, PIP5K1B, ATXN2/SH2B3, DACH1, UBE2Q2, and SLC7A9 were uncovered as loci associated with estimated glomerular filtration rate (eGFR) and CKD, and CUBN as a locus for albuminuria in cross-sectional data of general population studies. However, less than 1.5% of the total variance of eGFR and albuminuria is explained by the identified variants, and the relative risk for CKD is modified by at most 20% per locus. In African Americans, much of the risk for end-stage nondiabetic kidney disease is explained by common variants in the MYH9/APOL1 locus, and in individuals of European descent, variants in HLA-DQA1 and PLA2R1 implicate most of the risk for idiopathic membranous nephropathy. In contrast, genetic findings in the analysis of diabetic nephropathy are inconsistent. Uncovering variants explaining more of the genetically determined variability of kidney function is hampered by the multifactorial nature of CKD and different mechanisms involved in progressive CKD stages, and by the challenges in elucidating the role of low-frequency variants. Meta-analyses with larger sample sizes and analyses of longitudinal renal phenotypes using higher-resolution genotyping data are required to uncover novel loci associated with severe renal phenotypes

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods

<p>Abstract</p> <p>Background</p> <p>We investigated the influence of genotyping errors on the type I error rate and empirical power of two haplotype based association methods applied to candidate regions. We compared the performance of the Mantel Statistic Using Haplotype Sharing and the haplotype frequency based score test with that of the Armitage trend test.</p> <p>Our study is based on 1000 replication of simulated case-control data settings with 500 cases and 500 controls, respectively. One of the examined markers was set to be the disease locus with a simulated odds ratio of 3. Differential and non-differential genotyping errors were introduced following a misclassification model with varying mean error rates per locus in the range of 0.2% to 15.6%.</p> <p>Results</p> <p>We found that the type I error rate of all three test statistics hold the nominal significance level in the presence of nondifferential genotyping errors and low error rates. For high and differential error rates, the type I error rate of all three test statistics was inflated, even when genetic markers not in Hardy-Weinberg Equilibrium were removed. The empirical power of all three association test statistics remained high at around 89% to 94% when genotyping error rates were low, but decreased to 48% to 80% for high and nondifferential genotyping error rates.</p> <p>Conclusion</p> <p>Currently realistic genotyping error rates for candidate gene analysis (mean error rate per locus of 0.2%) pose no significant problem for the type I error rate as well as the power of all three investigated test statistics.</p

EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data

Summary: The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene-strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion. Availability: EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (&gt;175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms

Bonding of articular cartilage using a combination of biochemical degradation and surface cross-linking

After trauma, articular cartilage often does not heal due to incomplete bonding of the fractured surfaces. In this study we investigated the ability of chemical cross-linkers to facilitate bonding of articular cartilage, either alone or in combination with a pre-treatment with surface-degrading agents. Articular cartilage blocks were harvested from the femoropatellar groove of bovine calves. Two cartilage blocks, either after pre-treatment or without, were assembled in a custom-designed chamber in partial apposition and subjected to cross-linking treatment. Subsequently, bonding of cartilage was measured as adhesive strength, that is, the maximum force at rupture of bonded cartilage blocks divided by the overlap area. In a first approach, bonding was investigated after treatment with cross-linking reagents only, employing glutaraldehyde, 1-ethyl-3-diaminopropyl-carbodiimide (EDC)/N-hydroxysuccinimide (NHS), genipin, or transglutaminase. Experiments were conducted with or without compression of the opposing surfaces. Compression during cross-linking strongly enhanced bonding, especially when applying EDC/NHS and glutaraldehyde. Therefore, all further experiments were performed under compressive conditions. Combinations of each of the four cross-linking agents with the degrading pre-treatments, pepsin, trypsin, and guanidine, led to distinct improvements in bonding compared to the use of cross-linkers alone. The highest values of adhesive strength were achieved employing combinations of pepsin or guanidine with EDC/NHS, and guanidine with glutaraldehyde. The release of extracellular matrix components, that is, glycosaminoglycans and total collagen, from cartilage blocks after pre-treatment was measured, but could not be directly correlated to the determined adhesive strength. Cytotoxicity was determined for all substances employed, that is, surface degrading agents and cross-linkers, using the resazurin assay. Taking the favourable cell vitality after treatment with pepsin and EDC/NHS and the cytotoxic effects of guanidine and glutaraldehyde into account, the combination of pepsin and EDC/NHS appeared to be the most advantageous treatment in this study. In conclusion, bonding of articular cartilage blocks was achieved by chemical fixation of their surface components using cross-linking reagents. Application of compressive forces and prior modulation of surface structures enhanced cartilage bonding significantly. Enzymatic treatment in combination with cross-linkers may represent a promising addition to current techniques for articular cartilage repair

A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography

Acknowledgments The authors thank the Age-Related Eye Disease Study participants and the Age-Related Eye Disease Study Research Group for their valuable contribution to this research, and all study participants for contributing to the Cooperative Health Research in the Region of Augsburg study.Peer reviewedPublisher PD

Photostress Recovery Time as a Potential Predictive Biomarker for Age-Related Macular Degeneration

Abstract Purpose: The purpose of this study was to assess recovery time following photostress and its association with age-related macular degeneration (AMD) cross-sectionally and longitudinally in an elderly population-based cohort. Methods: We analyzed photostress recovery time (PRT) and AMD in >1800 AugUR study participants aged 70+ years. On color fundus images from baseline and 3-year follow-up, presence of AMD was graded manually (Three Continent AMD Consortium Severity Scale). Visual acuity (VA) was assessed via Early Treatment Diabetic Retinopathy Study (ETDRS) charts. After a 30-second bleaching of the macular region via direct ophthalmoscope, PRT was measured as the seconds to regain VA. Results: First, we analyzed 1208 AugUR participants cross-sectionally (288 with early AMD, and 78 with late AMD). Prolonged PRT was associated with early and late AMD versus no AMD (median PRT = 119.5, 198.0 versus 80.0 seconds, respectively; logistic regression odds ratio [OR] = 1.109–1.165 per 10 seconds, P values < 0.0001). Sensitivity analyses using alternative models or restricting to participants after cataract surgery revealed similar ORs. Second, the association was confirmed in an independent cross-sectional AugUR sample (n = 486). Third, in longitudinal analysis of 233 AugUR participants without AMD, prolonged PRT was associated with incident AMD ascertained 3 years later (follow-up time = 3.2 ± 0.2 years, OR = 1.112–1.162 per 10 seconds, P < 0.05). Overall, we demonstrate a significant association of prolonged PRT with AMD cross-sectionally and longitudinally in elderly individuals. Conclusions: Prolonged PRT might capture retinal function impairment after cell damage before early AMD is visible via color fundus imaging. Translational Relevance: Our results suggest PRT as quantitative predictive biomarker for incident AMD, making it potentially worthwhile also for clinical care

Chances and challenges of machine learning based disease classification in genetic association studies illustrated on age-related macular degeneration

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning-based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1,CFH) from artifacts (nearHERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof-of-concept that a GWAS using machine learning-derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms