Recurrence risk modelling of the genetic susceptibility to ankylosing spondylitis

Objectives - It has long been suspected that susceptibility to ankylosing spondylitis (AS) is influenced by genes lying distant to the major histocompatibility complex. This study compares genetic models of AS to assess the most likely mode of inheritance, using recurrence risk ratios in relatives of affected subjects. Methods - Recurrence risk ratios in different degrees of relatives were determined using published data from studies specifically designed to address the question. The methods of Risch were used to determine the expected recurrence risk ratios in different degrees of relatives, assuming equal first degree relative recurrence risk between models. Goodness of fit was determined by χ2 comparison of the expected number of affected subjects with the observed number, given equal numbers of each type of relative studied. Results - The recurrence risks in different degrees of relatives were: monozygotic (MZ) twins 63% (17/27), first degree relatives 8.2% (441/5390), second degree relatives 1.0% (8/834), and third degree relatives 0.7% (7/997). Parent-child recurrence risk (7.9%, 37/466) was not significantly different from the sibling recurrence risk (8.2%, 404/4924), excluding a significant dominance genetic component to susceptibility. Poor fitting models included single gene, genetic heterogeneity, additive, two locus multiplicative, and one locus and residual polygenes (χ2 > 32 (two degrees of freedom), p < 10-6 for all models). The best fitting model studied was a five locus model with multiplicative interaction between loci (χ2 = 1.4 (two degrees of freedom), p = 0.5). Oligogenic multiplicative models were the best fitting over a range of population prevalences and first degree recurrence risk rates. Conclusions - This study suggests that of the genetic models tested, the most likely model operating in AS is an oligogenic model with predominantly multiplicative interaction between loci.

Genetic associations and functional characterization of M1 aminopeptidases and immune-mediated diseases

Endosplasmic reticulum aminopeptidase 1 (ERAP1), endoplasmic reticulum aminopeptidase 2 (ERAP2) and puromycin-sensitive aminopeptidase (NPEPPS) are key zinc metallopeptidases that belong to the oxytocinase subfamily of M1 aminopeptidase family. NPEPPS catalyzes the processing of proteosome-derived peptide repertoire followed by trimming of antigenic peptides by ERAP1 and ERAP2 for presentation on major histocompatibility complex (MHC) Class I molecules. A series of genome-wide association studies have demonstrated associations of these aminopeptidases with a range of immune-mediated diseases such as ankylosing spondylitis, psoriasis, Behçet's disease, inflammatory bowel disease and type I diabetes, and significantly, genetic interaction between some aminopeptidases and HLA Class I loci with which these diseases are strongly associated. In this review, we highlight the current state of understanding of the genetic associations of this class of genes, their functional role in disease, and potential as therapeutic targets.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 x 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 x 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.

ssSNPer: identifying statistically similar SNPs to aid interpretation of genetic association studies

ssSNPer is a novel user-friendly web interface that provides easy determination of the number and location of untested HapMap SNPs, in the region surrounding a tested HapMap SNP, which are statistically similar and would thus produce comparable and perhaps more significant association results. Identification of ssSNPs can have crucial implications for the interpretation of the initial association results and the design of follow-up studies. AVAILABILITY: http://fraser.qimr.edu.au/general/daleN/ssSNPer/

Latent class and genetic analysis does not support migraine with aura and migraine without aura as separate entities

Latent class and genetic analyses were used to identify subgroups of migraine sufferers in a community sample of 6,265 Australian twins (55% female) aged 25-36 who had completed an interview based on International Headache Society (IHS) criteria. Consistent with prevalence rates from other population-based studies, 703 (20%) female and 250 (9%) male twins satisfied the IHS criteria for migraine without aura (MO), and of these, 432 (13%) female and 166 (6%) male twins satisfied the criteria for migraine with aura (MA) as indicated by visual symptoms. Latent class analysis (LCA) of IHS symptoms identified three major symptomatic classes, representing 1) a mild form of recurrent nonmigrainous headache, 2) a moderately severe form of migraine, typically without visual aura symptoms (although 40% of individuals in this class were positive for aura), and 3) a severe form of migraine typically with visual aura symptoms (although 24% of individuals were negative for aura). Using the LCA classification, many more individuals were considered affected to some degree than when using IHS criteria (35% vs. 13%). Furthermore, genetic model fitting indicated a greater genetic contribution to migraine using the LCA classification (heritability, h(2)=0.40; 95% CI, 0.29-0.46) compared with the IHS classification (h(2)=0.36; 95% CI, 0.22-0.42). Exploratory latent class modeling, fitting up to 10 classes, did not identify classes corresponding to either the IHS MO or MA classification. Our data indicate the existence of a continuum of severity, with MA more severe but not etiologically distinct from MO. In searching for predisposing genes, we should therefore expect to find some genes that may underlie all major recurrent headache subtypes, with modifying genetic or environmental factors that may lead to differential expression of the liability for migraine.

Prediction of multilocus identity-by-descent

Previous studies have enabled exact prediction of probabilities of identity-by-descent (IBD) in randommating populations for a few loci (up to four or so), with extension to more using approximate regression methods. Here we present a precise predictor of multiple-locus IBD using simple formulas based on exact results for two loci. In particular, the probability of non-IBD X ABC at each of ordered loci A, B, and C can be well approximated by XABC = XABXBC/XB and generalizes to X123. . .k = X12X23. . .Xk-1,k/ Xk-2, where X is the probability of non-IBD at each locus. Predictions from this chain rule are very precise with population bottlenecks and migration, but are rather poorer in the presence of mutation. From these coefficients, the probabilities of multilocus IBD and non-IBD can also be computed for genomic regions as functions of population size, time, and map distances. An approximate but simple recurrence formula is also developed, which generally is less accurate than the chain rule but is more robust with mutation. Used together with the chain rule it leads to explicit equations for non-IBD in a region. The results can be applied to detection of quantitative trait loci (QTL) by computing the probability of IBD at candidate loci in terms of identity-by-state at neighboring markers.

Prediction of IBD based on population history for fine gene mapping

A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBDL), among individuals without pedigree, given information on surrounding markers and population history. These IBDL probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBD L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.

Mapping of multiple quantitative trait loci affecting bovine spongiform encephalopathy

A whole-genome scan was conducted to map quantitative trait loci (QTL) for BSE resistance or susceptibility. Cows from four half-sib families were included and 173 microsatellite markers were used to construct a 2835-cM (Kosambi) linkage map covering 29 autosomes and the pseudoautosomal region of the sex chromosome. Interval mapping by linear regression was applied and extended to a multiple-QTL analysis approach that used identified QTL on other chromosomes as cofactors to increase mapping power. In the multiple-QTL analysis, two genome-wide significant QTL (BTA17 and X/Y ps) and four genome-wide suggestive QTL (BTA1, 6, 13, and 19) were revealed. The QTL identified here using linkage analysis do not overlap with regions previously identified using TDT analysis. One factor that may explain the disparity between the results is that a more extensive data set was used in the present study. Furthermore, methodological differences between TDT and linkage analyses may affect the power of these approaches.

Candidate gene analysis for quantitative traits using the transmission disequilibrium test : the example of the melanocortin 4-receptor in pigs

Population-wide associations between loci due to linkage disequilibrium can be used to map quantitative trait loci (QTL) with high resolution. However, spurious associations between markers and QTL can also arise as a consequence of population stratification. Statistical methods that cannot differentiate between loci associations due to linkage disequilibria from those caused in other ways can render false-positive results. The transmission-disequilibrium test (TDT) is a robust test for detecting QTL. The TDT exploits within-family associations that are not affected by population stratification. However, some TDTs are formulated in a rigid-form, with reduced potential applications. In this study we generalize TDT using mixed linear models to allow greater statistical flexibility. Allelic effects are estimated with two independent parameters: one exploiting the robust within-family information and the other the potentially biased between-family information. A significant difference between these two parameters can be used as evidence for spurious association. This methodology was then used to test the effects of the fourth melanocortin receptor (MC4R) on production traits in the pig. The new analyses supported the previously reported results; i.e., the studied polymorphism is either causal of in very strong linkage disequilibrium with the causal mutation, and provided no evidence for spurious association.

RNA interference-inducing hairpin RNAs in plants act through the viral defence pathway

RNA interference (RNAi) is widely used to silence genes in plants and animals. It operates through the degradation of target mRNA by endonuclease complexes guided by approximately 21 nucleotide (nt) short interfering RNAs (siRNAs). A similar process regulates the expression of some developmental genes through approximately 21 nt microRNAs. Plants have four types of Dicer-like (DCL) enzyme, each producing small RNAs with different functions. Here, we show that DCL2, DCL3 and DCL4 in Arabidopsis process both replicating viral RNAs and RNAi-inducing hairpin RNAs (hpRNAs) into 22-, 24- and 21 nt siRNAs, respectively, and that loss of both DCL2 and DCL4 activities is required to negate RNAi and to release the plant's repression of viral replication. We also show that hpRNAs, similar to viral infection, can engender long-distance silencing signals and that hpRNA-induced silencing is suppressed by the expression of a virus-derived suppressor protein. These findings indicate that hpRNA-mediated RNAi in plants operates through the viral defence pathway.

Is disease severity in ankylosing spondylitis genetically determined?

Objective. To assess the role of genes and the environment in determining the severity of ankylosing spondylitis. Methods: One hundred seventy-three families with >1 case of ankylosing spondylitis were recruited (120 affected sibling pairs, 26 affected parent-child pairs, 20 families with both first- and second-degree relatives affected, and 7 families with only second-degree relatives affected), comprising a total of 384 affected individuals. Disease severity was assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and functional impairment was determined using the Bath Ankylosing Spondylitis Functional Index (BASFI). Disease duration and age at onset were also studied. Variance-components modeling was used to determine the genetic and environmental components Contributing to familiality of the traits examined, and complex segregation analysis was performed to assess different disease models. Results. Both the disease activity and functional capacity as assessed by the BASDAI and the BASFI, respectively, were found to be highly familial (BASDAI familiality 0.51 [P = 10-4], BASFI familiality 0,68 [P = 3 × 10-7]). No significant shared environmental component was demonstrated to be associated with either the BASDAI or the BASFI. Including age at disease onset and duration of disease as covariates made no difference in the heritability assessments. A strong correlation was noted between the BASDAI and the BASFI (genetic correlation 0.9), suggesting the presence of shared determinants of these 2 measures. However, there was significant residual heritability for each measure independent of the other (BASFI residual heritability 0.48, BASDAI 0,36), perhaps indicating that not all genes influencing disease activity influence chronicity. No significant heritability of age at disease onset was found (heritability 0.18; P = 0.2). Segregation studies suggested the presence of a single major gene influencing the BASDAI and the BASFI. Conclusion. This study demonstrates a major genetic contribution to disease severity in ankylosing spondylitis. As with susceptibility to ankylosing spondylitis, shared environmental factors play little role in determining the disease severity.

The effect of LRP5 polymorphisms on bone mineral density is apparent in childhood

Bone mass acquired during childhood is the primary determinant of adult bone mineral density (BMD) and osteoporosis risk. Bone accrual is subject to genetic influences. Activating and inactivating LRP5 gene mutations elicit extreme bone phenotypes, while more common LRP5 polymorphisms are associated with normal variation of BMD. Our aim was to test the hypothesis that LRP5 gene polymorphisms influence bone mass acquisition during childhood. The association between LRP5 gene polymorphisms and bone size and mineralization was examined in 819 unrelated British Caucasian children (n = 429 boys) aged 9 years. Height, weight, pubertal status (where available), total-body and spinal bone area, bone mineral content (BMC), BMD, and area-adjusted BMC (aBMC) were assessed. Dual-energy X-ray absorptiometry (DXA)-gene associations were assessed by linear regression, with adjustment for age, gender, pubertal status, and body size parameters. There were 140, 79, 12, and 2 girls who achieved Tanner stages I-IV, respectively, and 179 and 32 boys who achieved Tanner stages I and II, respectively. The rs2306862 (N740N) coding polymorphism in exon 10 of the LRP5 gene was associated with spinal BMD and aBMC (each P = 0.01) and total-body BMD and aBMC (P = 0.04 and 0.03, respectively). Adjusting for pubertal stage strengthened associations between this polymorphism and spinal BMD and aBMC (P = 0.01 and 0.002, respectively). Individuals homozygous for the T allele had greater spinal BMD and aBMC scores than those homozygous for the C allele. A dose effect was apparent as the mean spinal BMD and aBMC of heterozygous TC individuals were intermediate between those of their TT and CC counterparts. The N740N polymorphism in exon 10 of LRP5 was associated with spinal BMD and aBMC in pre- and early pubertal children. These results indicate that LRP5 influences volumetric bone density in childhood, possibly through effects on trabecular bone formation.

Common variants at the MHC locus and at chromosome 16q24.1 predispose to Barrett's Esophagus

Barrett's esophagus is an increasingly common disease that is strongly associated with reflux of stomach acid and usually a hiatus hernia, and it strongly predisposes to esophageal adenocarcinoma (EAC), a tumor with a very poor prognosis. We report the first genome-wide association study on Barrett's esophagus, comprising 1,852 UK cases and 5,172 UK controls in the discovery stage and 5,986 cases and 12,825 controls in the replication stage. Variants at two loci were associated with disease risk: chromosome 6p21, rs9257809 (P combined = 4.09 × 10-9; odds ratio (OR) = 1.21, 95% confidence interval (CI) =1.13-1.28), within the major histocompatibility complex locus, and chromosome 16q24, rs9936833 (P combined = 2.74 × 10-10; OR = 1.14, 95% CI = 1.10-1.19), for which the closest protein-coding gene is FOXF1, which is implicated in esophageal development and structure. We found evidence that many common variants of small effect contribute to genetic susceptibility to Barrett's esophagus and that SNP alleles predisposing to obesity also increase risk for Barrett's esophagus. © 2012 Nature America, Inc. All rights reserved.