Chad Haynes

Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes.

Recent proteome-wide screening approaches have provided a wealth of information about interacting proteins in various organisms. To test for a potential association between protein connectivity and the amount of predicted structural disorder, the disorder propensities of proteins with various numbers of interacting partners from four eukaryotic organisms (Caenorhabditis elegans, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens) were investigated. The results of PONDR VL-XT disorder analysis show that for all four studied organisms, hub proteins, defined here as those that interact with ≥10 partners, are significantly more disordered than end proteins, defined here as those that interact with just one partner. The proportion of predicted disordered residues, the average disorder score, and the number of predicted disordered regions of various lengths were higher overall in hubs than in ends. A binary classification of hubs and ends into ordered and disordered subclasses using the consensus prediction method showed a significant enrichment of wholly disordered proteins and a significant depletion of wholly ordered proteins in hubs relative to ends in worm, fly, and human. The functional annotation of yeast hubs and ends using GO categories and the correlation of these annotations with disorder predictions demonstrate that proteins with regulation, transcription, and development annotations are enriched in disorder, whereas proteins with catalytic activity, transport, and membrane localization annotations are depleted in disorder. The results of this study demonstrate that intrinsic structural disorder is a distinctive and common characteristic of eukaryotic hub proteins, and that disorder may serve as a determinant of protein interactivity.

Identification, analysis, and prediction of protein ubiquitination sites

Ubiquitination plays an important role in many cellular processes and is implicated in many diseases. Experimental identification of ubiquitination sites is challenging due to rapid turnover of ubiquitinated proteins and the large size of the ubiquitin modifier. We identified 141 new ubiquitination sites using a combination of liquid chromatography, mass spectrometry, and mutant yeast strains. Investigation of the sequence biases and structural preferences around known ubiquitination sites indicated that their properties were similar to those of intrinsically disordered protein regions. Using a combined set of new and previously known ubiquitination sites, we developed a random forest predictor of ubiquitination sites, UbPred. The class‐balanced accuracy of UbPred reached 72%, with the area under the ROC curve at 80%. The application of UbPred showed that high confidence Rsp5 ubiquitin ligase substrates and proteins with very short half‐lives were significantly enriched in the number of predicted ubiquitination sites. Proteome‐wide prediction of ubiquitination sites in Saccharomyces cerevisiae indicated that highly ubiquitinated substrates were prevalent among transcription/enzyme regulators and proteins involved in cell cycle control. In the human proteome, cytoskeletal, cell cycle, regulatory, and cancer‐associated proteins display higher extent of ubiquitination than proteins from other functional categories. We show that gain and loss of predicted ubiquitination sites may likely represent a molecular mechanism behind a number of disease‐associatedmutations. UbPred is available at http://www.ubpred.org. Proteins 2010.

Genomewide Scan for Linkage Reveals Evidence of Several Susceptibility Loci for Alopecia Areata

Alopecia areata (AA) is a genetically determined, immune-mediated disorder of the hair follicle that affects 1%-2% of the U.S. population. It is defined by a spectrum of severity that ranges from patchy localized hair loss on the scalp to the complete absence of hair everywhere on the body. In an effort to define the genetic basis of AA, we performed a genomewide search for linkage in 20 families with AA consisting of 102 affected and 118 unaffected individuals from the United States and Israel. Our analysis revealed evidence of at least four susceptibility loci on chromosomes 6, 10, 16 and 18, by use of several different statistical approaches. Fine-mapping analysis with additional families yielded a maximum multipoint LOD score of 3.93 on chromosome 18, a two-point affected sib pair (ASP) LOD score of 3.11 on chromosome 16, several ASP LOD scores >2.00 on chromosome 6q, and a haplotype-based relative risk LOD of 2.00 on chromosome 6p (in the major histocompatibility complex locus). Our findings confirm previous studies of association of the human leukocyte antigen locus with human AA, as well as the C3H-HeJ mouse model for AA. Interestingly, the major loci on chromosomes 16 and 18 coincide with loci for psoriasis reported elsewhere. These results suggest that these regions may harbor gene(s) involved in a number of different skin and hair disorders.

Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies

BackgroundPhenotype error causes reduction in power to detect genetic association. We present a quantification of phenotype error, also known as diagnostic error, on power and sample size calculations for case-control genetic association studies between a marker locus and a disease phenotype. We consider the classic Pearson chi-square test for independence as our test of genetic association. To determine asymptotic power analytically, we compute the distributions non-centrality parameter, which is a function of the case and control sample sizes, genotype frequencies, disease prevalence, and phenotype misclassification probabilities. We derive the non-centrality parameter in the presence of phenotype errors and equivalent formulas for misclassification cost (the percentage increase in minimum sample size needed to maintain constant asymptotic power at a fixed significance level for each percentage increase in a given misclassification parameter). We use a linear Taylor Series approximation for the cost of phenotype misclassification to determine lower bounds for the relative costs of misclassifying a true affected (respectively, unaffected) as a control (respectively, case). Power is verified by computer simulation.ResultsOur major findings are that: (i) the median absolute difference between analytic power with our method and simulation power was 0.001 and the absolute difference was no larger than 0.011; (ii) as the disease prevalence approaches 0, the cost of misclassifying a unaffected as a case becomes infinitely large while the cost of misclassifying an affected as a control approaches 0.ConclusionOur work enables researchers to specifically quantify power loss and minimum sample size requirements in the presence of phenotype errors, thereby allowing for more realistic study design. For most diseases of current interest, verifying that cases are correctly classified is of paramount importance.

Serine/arginine-rich splicing factors belong to a class of intrinsically disordered proteins

Serine/arginine-rich (SR) splicing factors play an important role in constitutive and alternative splicing as well as during several steps of RNA metabolism. Despite the wealth of functional information about SR proteins accumulated to-date, structural knowledge about the members of this family is very limited. To gain a better insight into structure-function relationships of SR proteins, we performed extensive sequence analysis of SR protein family members and combined it with ordered/disordered structure predictions. We found that SR proteins have properties characteristic of intrinsically disordered (ID) proteins. The amino acid composition and sequence complexity of SR proteins were very similar to those of the disordered protein regions. More detailed analysis showed that the SR proteins, and their RS domains in particular, are enriched in the disorder-promoting residues and are depleted in the order-promoting residues as compared to the entire human proteome. Moreover, disorder predictions indicated that RS domains of SR proteins were completely unstructured. Two different classification methods, the charge-hydropathy measure and the cumulative distribution function (CDF) of the disorder scores, were in agreement with each other, and they both strongly predicted members of the SR protein family to be disordered. This study emphasizes the importance of the disordered structure for several functions of SR proteins, such as for spliceosome assembly and for interaction with multiple partners. In addition, it demonstrates the usefulness of order/disorder predictions for inferring protein structure from sequence.

Disease-Associated Mutations Disrupt Functionally Important Regions of Intrinsic Protein Disorder

The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7–2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.

A transmission disequilibrium test for general pedigrees that is robust to the presence of random genotyping errors and any number of untyped parents

Two issues regarding the robustness of the original transmission disequilibrium test (TDT) developed by Spielman et al are: (i) missing parental genotype data and (ii) the presence of undetected genotype errors. While extensions of the TDT that are robust to items (i) and (ii) have been developed, there is to date no single TDT statistic that is robust to both for general pedigrees. We present here a likelihood method, the TDTae, which is robust to these issues in general pedigrees. The TDTae assumes a more general disease model than the traditional TDT, which assumes a multiplicative inheritance model for genotypic relative risk. Our model is based on Weinbergs work. To assess robustness, we perform simulations. Also, we apply our method to two data sets from actual diseases: psoriasis and sitosterolemia. Maximization under alternative and null hypotheses is performed using Powells method. Results of our simulations indicate that our method maintains correct type I error rates at the 1, 5, and 10% levels of significance. Furthermore, a Kolmorogov–Smirnoff Goodness of Fit test suggests that the data are drawn from a central χ2 with 2 df, the correct asymptotic null distribution. The psoriasis results suggest two loci as being significantly linked to the disease, even in the presence of genotyping errors and missing data, and the sitosterolemia results show a P-value of 1.5 × 10−9 for the marker locus nearest to the sitosterolemia disease genes. We have developed software to perform TDTae calculations, which may be accessed from our ftp site.

Genome-wide autozygosity mapping in human populations

Individuals are frequently observed to have long segments of uninterrupted sequences of homozygous markers. One of the major mechanisms that gives rise to such long homozygous segments is consanguineous marriages, where parents pass shared chromosomal segments to their child. Such chromosomal segments are also known as autozygous segments. The clinical evidence that progeny from inbred individuals may have reduced health and fitness because of homozygosity of recessive alleles is well known. As the length of such homozygous segments depends on the degree of parental consanguinity, it would be logical to observe shorter homozygous segments in more outbred populations. However, a recent study identified long homozygous regions, thus likely to be autozygous segments in the HapMap populations. While an abundance of homozygous segments may significantly reduce the ability to fine map disease genes using association studies, detecting tracts of extended homozygosity related to disease status seems the natural next step in genome‐wide association studies beyond allele, genotype and haplotype association analyses. In this study, we propose a new algorithm to map disease‐related segments based on autozygosity using case‐control data. The underlying rationale for the proposed method is that shared autozygosity regions that differ between diseased and healthy individuals may harbor mutations underlying diseases. Specifically, our algorithm uses a sliding‐window framework and employs a logarithm of the odds score measure of autozygosity coupled with permutation‐based methods to identify disease‐related regions. We illustrate the advantage of the algorithm with its application to a genome‐wide association study on Parkinsons disease. Genet. Epidemiol. 2008.

Quantifying the Percent Increase in Minimum Sample Size for SNP Genotyping Errors in Genetic Model-Based Association Studies

Kang et al. [Genet Epidemiol 2004;26:132–141] addressed the question of which genotype misclassification errors are most costly, in terms of minimum percentage increase in sample size necessary (%MSSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association in a genetic model-free setting. They answered the question for single nucleotide polymorphisms (SNPs) using the 2 × 3 χ2 test of independence. We address the same question here for a genetic model-based framework. The genetic model parameters considered are: disease model (dominant, recessive), genotypic relative risk, SNP (marker) and disease allele frequency, and linkage disequilibrium. %MSSN coefficients of each of the six possible error rates are determined by expanding the non-centrality parameter of the asymptotic distribution of the 2 × 3 χ2 test under a specified alternative hypothesis to approximate %MSSN using a linear Taylor series in the error rates. In this work we assume errors misclassifying one homozygote as another homozygote are 0, since these errors are thought to rarely occur in practice. Our findings are that there are settings of the genetic model parameters that lead to large total %MSSN for both dominant and recessive models. As SNP minor allele approaches 0, total %MSSN increases without bound, independent of other genetic model parameters. In general, %MSSN is a complex function of the genetic model parameters. Use of SNPs with small minor allele frequency requires careful attention to frequency of genotyping errors to insure that power specifications are met. Software to perform these calculations for study design is available, and an example of its use to study a disease is given.

The Effects of SNP Genotyping Errors on the Power of The Cochran-Armitage Linear Trend Test for Case/Control Association Studies

The questions addressed in this paper are: What single nucleotide polymorphism (SNP) genotyping errors are most costly, in terms of minimum sample size necessary (MSSN) to maintain constant asymptotic power and significance level, when performing case‐control studies of genetic association applying the Cochran‐Armitage trend test? And which trend test or χ2 test is more powerful under standard genetic models with genotyping errors? Our strategy is to expand the non‐centrality parameter of the asymptotic distribution of the trend test to approximate the MSSN using a Taylor series linear in the genotyping error rates. We apply our strategy to example scenarios that assume recessive, dominant, additive, or over‐dominant disease models.