Harald H H Göring

Polymorphisms in the tyrosine kinase 2 and interferon regulatory factor 5 genes are associated with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex systemic autoimmune disease caused by both genetic and environmental factors. Genome scans in families with SLE point to multiple potential chromosomal regions that harbor SLE susceptibility genes, and association studies in different populations have suggested several susceptibility alleles for SLE. Increased production of type I interferon (IFN) and expression of IFN-inducible genes is commonly observed in SLE and may be pivotal in the molecular pathogenesis of the disease. We analyzed 44 single-nucleotide polymorphisms (SNPs) in 13 genes from the type I IFN pathway in 679 Swedish, Finnish, and Icelandic patients with SLE, in 798 unaffected family members, and in 438 unrelated control individuals for joint linkage and association with SLE. In two of the genes--the tyrosine kinase 2 (TYK2) and IFN regulatory factor 5 (IRF5) genes--we identified SNPs that displayed strong signals in joint analysis of linkage and association (unadjusted P<10(-7)) with SLE. TYK2 binds to the type I IFN receptor complex and IRF5 is a regulator of type I IFN gene expression. Thus, our results support a disease mechanism in SLE that involves key components of the type I IFN system.

Large Upward Bias in Estimation of Locus-Specific Effects from Genomewide Scans

The primary goal of a genomewide scan is to estimate the genomic locations of genes influencing a trait of interest. It is sometimes said that a secondary goal is to estimate the phenotypic effects of each identified locus. Here, it is shown that these two objectives cannot be met reliably by use of a single data set of a currently realistic size. Simulation and analytical results, based on variance-components linkage analysis as an example, demonstrate that estimates of locus-specific effect size at genomewide LOD score peaks tend to be grossly inflated and can even be virtually independent of the true effect size, even for studies on large samples when the true effect size is small. However, the bias diminishes asymptotically. The explanation for the bias is that the LOD score is a function of the locus-specific effect-size estimate, such that there is a high correlation between the observed statistical significance and the effect-size estimate. When the LOD score is maximized over the many pointwise tests being conducted throughout the genome, the locus-specific effect-size estimate is therefore effectively maximized as well. We argue that attempts at bias correction give unsatisfactory results, and that pointwise estimation in an independent data set may be the only way of obtaining reliable estimates of locus-specific effect-and then only if one does not condition on statistical significance being obtained. We further show that the same factors causing this bias are responsible for frequent failures to replicate initial claims of linkage or association for complex traits, even when the initial localization is, in fact, correct. The findings of this study have wide-ranging implications, as they apply to all statistical methods of gene localization. It is hoped that, by keeping this bias in mind, we will more realistically interpret and extrapolate from the results of genomewide scans.

Gene Mapping in the 20th and 21st Centuries: Statistical Methods, Data Analysis, and Experimental Design

Abstract In the 20th century geneticists began to unravel some of the simpler aspects of the etiology of inherited diseases in humans. The theory of linkage analysis was developed and applied long before the advent of molecular biology, but only the technological advances of the second half of the 20th century made large-scale gene mapping with a dense genome-spanning set of markers a reality. More recently, the primary topic of interest has shifted from simple Mendelian diseases, for which genotypes of some gene are the cause of disease, to more complex diseases, for which genotypes of some set of genes together with environmental factors merely alter the probability that an individual gets the disease, although individual factors are typically insufficient to cause the disease outright. To this end, a great deal of dogma has evolved about the best way to skin this cat, although to date success has been minimal with any approach. We postulate that the main reason for this is a lack of attention to experimental design. Once the data have been ascertained, the most powerful statistical methods will not be able to salvage an inappropriately designed study (Andersen 1990). Each phenotype and/or population mandates its own individually tailored study design to maximize the chances of successful gene mapping. We suggest that careful consideration of the available data from real genotype-phenotype correlation studies (as opposed to oversimplified theoretically tractable models), and the practical feasibility of different ascertainment schemes dictate how one should proceed. In this review we review the theory and practice of gene mapping at the close of the 20th century, showing that most methods of linkage and linkage disequilibrium analysis are similar in a fundamental sense, with the differences being related more to study design and ascertainment than to technical details of the underlying statistical analysis. To this end, we propose a new focus in the field of statistical genetics that more explicitly highlights the primacy of study design as the means to increase power for gene mapping.

Global patterns of cis variation in human cells revealed by high-density allelic expression analysis.

Cis-acting variants altering gene expression are a source of phenotypic differences. The cis-acting components of expression variation can be identified through the mapping of differences in allelic expression (AE), which is the measure of relative expression between two allelic transcripts. We generated a map of AE associated SNPs using quantitative measurements of AE on Illumina Human1M BeadChips. In 53 lymphoblastoid cell lines derived from donors of European descent, we identified common cis variants affecting 30% (2935/9751) of the measured RefSeq transcripts at 0.001 permutation significance. The pervasive influence of cis-regulatory variants, which explain 50% of population variation in AE, extend to full-length transcripts and their isoforms as well as to unannotated transcripts. These strong effects facilitate fine mapping of cis-regulatory SNPs, as demonstrated by dissection of heritable control of transcripts in the systemic lupus erythematosus–associated C8orf13-BLK region in chromosome 8. The dense collection of associations will facilitate large-scale isolation of cis-regulatory SNPs.

A risk haplotype of STAT4 for systemic lupus erythematosus is over-expressed, correlates with anti-dsDNA and shows additive effects with two risk alleles of IRF5

Systemic lupus erythematosus (SLE) is the prototype autoimmune disease where genes regulated by type I interferon (IFN) are over-expressed and contribute to the disease pathogenesis. Because signal transducer and activator of transcription 4 (STAT4) plays a key role in the type I IFN receptor signaling, we performed a candidate gene study of a comprehensive set of single nucleotide polymorphism (SNPs) in STAT4 in Swedish patients with SLE. We found that 10 out of 53 analyzed SNPs in STAT4 were associated with SLE, with the strongest signal of association (P = 7.1 × 10−8) for two perfectly linked SNPs rs10181656 and rs7582694. The risk alleles of these 10 SNPs form a common risk haplotype for SLE (P = 1.7 × 10−5). According to conditional logistic regression analysis the SNP rs10181656 or rs7582694 accounts for all of the observed association signal. By quantitative analysis of the allelic expression of STAT4 we found that the risk allele of STAT4 was over-expressed in primary human cells of mesenchymal origin, but not in B-cells, and that the risk allele of STAT4 was over-expressed (P = 8.4 × 10−5) in cells carrying the risk haplotype for SLE compared with cells with a non-risk haplotype. The risk allele of the SNP rs7582694 in STAT4 correlated to production of anti-dsDNA (double-stranded DNA) antibodies and displayed a multiplicatively increased, 1.82-fold risk of SLE with two independent risk alleles of the IRF5 (interferon regulatory factor 5) gene.

A Spectrum of ABCC6 Mutations Is Responsible for Pseudoxanthoma Elasticum

To better understand the pathogenetics of pseudoxanthoma elasticum (PXE), we performed a mutational analysis of ATP-binding cassette subfamily C member 6 (ABCC6) in 122 unrelated patients with PXE, the largest cohort of patients yet studied. Thirty-six mutations were characterized, and, among these, 28 were novel variants (for a total of 43 PXE mutations known to date). Twenty-one alleles were missense variants, six were small insertions or deletions, five were nonsense, two were alleles likely to result in aberrant mRNA splicing, and two were large deletions involving ABCC6. Although most mutations appeared to be unique variants, two disease-causing alleles occurred frequently in apparently unrelated individuals. R1141X was found in our patient cohort at a frequency of 18.8% and was preponderant in European patients. ABCC6del23-29 occurred at a frequency of 12.9% and was prevalent in patients from the United States. These results suggested that R1141X and ABCC6del23-29 might have been derived regionally from founder alleles. Putative disease-causing mutations were identified in approximately 64% of the 244 chromosomes studied, and 85.2% of the 122 patients were found to have at least one disease-causing allele. Our results suggest that a fraction of the undetected mutant alleles could be either genomic rearrangements or mutations occurring in noncoding regions of the ABCC6 gene. The distribution pattern of ABCC6 mutations revealed a cluster of disease-causing variants within exons encoding a large C-terminal cytoplasmic loop and in the C-terminal nucleotide-binding domain (NBD2). We discuss the potential structural and functional significance of this mutation pattern within the context of the complex relationship between the PXE phenotype and the function of ABCC6.

High dimensional endophenotype ranking in the search for major depression risk genes

BACKGROUND Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness. METHODS Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees. RESULTS Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk. CONCLUSIONS The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.

Chemerin, a novel adipokine in the regulation of angiogenesis

CONTEXT Chemerin is a new adipokine associated with obesity and the metabolic syndrome. Gene expression levels of chemerin were elevated in the adipose depots of obese compared with lean animals and was markedly elevated during differentiation of fibroblasts into mature adipocytes. OBJECTIVE The objective of the study was to identify factors that affect the regulation and potential function of chemerin using a genetics approach. DESIGN, SETTING, PATIENTS, AND INTERVENTION Plasma chemerin levels were measured in subjects from the San Antonio Family Heart Study, a large family-based genetic epidemiological study including 1354 Mexican-American individuals. Individuals were randomly sampled without regard to phenotype or disease status. MAIN OUTCOME MEASURES A genome-wide association analysis using 542,944 single-nucleotide polymorphisms in a subset of 523 of the same subjects was undertaken. The effect of chemerin on angiogenesis was measured using human endothelial cells and interstitial cells in coculture in a specially formulated medium. RESULTS Serum chemerin levels were found to be highly heritable (h(2) = 0.25; P = 1.4 x 10(-9)). The single-nucleotide polymorphism showing strongest evidence of association (rs347344; P = 1.4 x 10(-6)) was located within the gene encoding epithelial growth factor-like repeats and discoidin I-like domains 3, which has a known role in angiogenesis. Functional angiogenesis assays in human endothelial cells confirmed that chemerin significantly mediated the formation of blood vessels to a similar extent as vascular endothelial growth factor. CONCLUSION Here we demonstrate for the first time that plasma chemerin levels are significantly heritable and identified a novel role for chemerin as a stimulator of angiogenesis.

Haplotypes of Transcription Factor 7–Like 2 (TCF7L2) Gene and Its Upstream Region Are Associated With Type 2 Diabetes and Age of Onset in Mexican Americans

TCF7L2 acts as both a repressor and transactivator of genes, as directed by the Wnt signaling pathway. Recently, several highly correlated sequence variants located within a haplotype block of the TCF7L2 gene were observed to associate with type 2 diabetes in three Caucasian cohorts. We previously reported linkage of type 2 diabetes in the San Antonio Family Diabetes Study (SAFADS) cohort consisting of extended pedigrees of Mexican Americans to the region of chromosome 10q harboring TCF7L2. We therefore genotyped 11 single nucleotide polymorphisms (SNPs) from nine haplotype blocks across the gene in 545 SAFADS subjects (178 diabetic) to investigate their role in diabetes pathogenesis. We observed nominal association between four SNPs (rs10885390, rs7903146, rs12255372, and rs3814573) in three haplotype blocks and type 2 diabetes, age at diagnosis, and 2-h glucose levels (P = 0.001–0.055). Furthermore, we identified a common protective haplotype defined by these four SNPs that was significantly associated with type 2 diabetes and age at diagnosis (P = 4.2 × 10−5, relative risk [RR] 0.69; P = 6.7 × 10−6, respectively) and a haplotype that confers diabetes risk that contains the rare alleles at SNPs rs10885390 and rs12255372 (P = 0.02, RR 1.64). These data provide evidence that variation in the TCF7L2 genomic region may affect risk for type 2 diabetes in Mexican Americans, but the attributable risk may be lower than in Caucasian populations.

Linkage Analysis in the Presence of Errors III: Marker Loci and Their Map as Nuisance Parameters

In linkage and linkage disequilibrium (LD) analysis of complex multifactorial phenotypes, various types of errors can greatly reduce the chance of successful gene localization. The power of such studies-even in the absence of errors-is quite low, and, accordingly, their robustness to errors can be poor, especially in multipoint analysis. For this reason, it is important to deal with the ramifications of errors up front, as part of the analytical strategy. In this study, errors in the characterization of marker-locus parameters-including allele frequencies, haplotype frequencies (i.e., LD between marker loci), recombination fractions, and locus order-are dealt with through the use of profile likelihoods maximized over such nuisance parameters. It is shown that the common practice of assuming fixed, erroneous values for such parameters can reduce the power and/or increase the probability of obtaining false positive results in a study. The effects of errors in assumed parameter values are generally more severe when a larger number of less informative marker loci, like the highly-touted single nucleotide polymorphisms (SNPs), are analyzed jointly than when fewer but more informative marker loci, such as microsatellites, are used. Rather than fixing inaccurate values for these parameters a priori, we propose to treat them as nuisance parameters through the use of profile likelihoods. It is demonstrated that the power of linkage and/or LD analysis can be increased through application of this technique in situations where parameter values cannot be specified with a high degree of certainty.