The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Neuroimaging and genetics: Exploring, searching, and finding

This issue on the genetics of brain imaging phenotypes is a celebration of the happy marriage between two of science's highly interesting fields: neuroscience and genetics. The articles collected here are ample evidence that a good deal of synergy exists in this marriage. A wide selection of papers is presented that provide many different perspectives on how genes cause variation in brain structure and function, which in turn influence behavioral phenotypes (including psychopathology). They are examples of the many different methodologies in contemporary genetics and neuroscience research. Genetic methodology includes genome-wide association (GWA), candidate-gene association, and twin studies. Sources of data on brain phenotypes include cortical gray matter (GM) structural/volumetric measures from magnetic resonance imaging (MRI); white matter (WM) measures from diffusion tensor imaging (DTI), such as fractional anisotropy; functional- (activity-) based measures from electroencephalography (EEG), and functional MRI (fMRI). Together, they reflect a combination of scientific fields that have seen great technological advances, whether it is the single-nucleotide polymorphism (SNP) array in genetics, the increasingly high-resolution MRI imaging, or high angular resolution diffusion imaging technique for measuring WM connective properties.

Myriad genetics: Patent law and genetic testing

This paper considers the legal challenges to the legal validity of the patents held by Myriad Genetics in respect of genetic testing for breast cancer and ovarian cancer. It argues that broad-based patents on gene sequences and medical diagnostics will have a harmful effect upon access to patient care, genetic research, and the administration of public health care.

Correction: Genome-wide association studies of asthma in population-based cohorts confirm known and suggested loci and identify an additional association near HLA

Read the original article via 'related work' link below

Genomewide screens in ankylosing spondylitis

Efforts to identify genes other than HLA-B27 in AS have been driven by the strength of the evidence from genetic epidemiology studies indicating that HLA-B27, although a major gene in AS, is clearly not the only significant gene operating. This is the case for both genetic determinants of disease-susceptibility and phenotypic characteristics such as disease severity and associated disease features. In this chapter the genetic epidemiology of AS and the gene-mapping studies performed to date will be reviewed and the future direction of research in this field discussed.

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci.

The interleukin 1 gene cluster contains a major susceptibility locus for ankylosing spondylitis

Ankylosing spondylitis (AS) is a common and highly heritable inflammatory arthropathy. Although the gene HLA-B27 is almost essential for the inheritance of the condition, it alone is not sufficient to explain the pattern of familial recurrence of the disease. We have previously demonstrated suggestive linkage of AS to chromosome 2q13, a region containing the interleukin 1 (IL-1) family gene cluster, which includes several strong candidates for involvement in the disease. In the current study, we describe strong association and transmission of IL-1 family gene cluster single-nucleotide polymorphisms and haplotypes with AS.

Matters Arising: Zhu et al, "A novel gene variation of TNFα associated with ankylosing spondylitis: A reconfirmed study"

In a recently published study1 involving 79 ankylosing spondylitis (AS) patients and 132 unrelated healthy blood donors, Zhu et al report association of a tumour necrosis factor α (TNFα) single nucleotide polymorphism (SNP), rs1799724, with AS.1 I have many concerns with this paper and its conclusions...

Ankylosing spondylitis in West Africans - evidence for a non-HLA-B27 protective effect

Objective - To determine the prevalence of ankylosing spondylitis in the Fula ethnic group in The Gambia, and relate the disease prevalence to the B27 frequency and subtype distribution of that population. Methods - 215 first degree relatives of 48 B27 positive Fula twin pairs, and 900 adult Fula males were screened for ankylosing spondylitis by clinical and, where appropriate, radiographic means. The B27 prevalence was determined by PCR/sequence specific oligonucleotides on finger prick samples from 100 unrelated Fula, and B27 subtype distribution by SSCP on unrelated B27 positive individuals. This data were then compared with the prevalence of ankylosing spondylitis among B27 positive Caucasians. Results - No case of ankylosing spondylitis was seen. Six per cent of Fula are B27 positive, of which 32% are B*2703 and 68% B*2705. Assuming the penetrance of ankylosing spondylitis in B27 positive Fula is the same as in B27 positive Caucasians, the probability of not observing any cases of ankylosing spondylitis among the Fula examined is remote (P = 6.7 x 10-6). Similarly, the chance of not seeing any cases among those expected to be either B*2705 or B*2703 was small (P = 3.2 x 10-4 for B*2705, and P = 0.02 for B*2703). Conclusions - The risk of developing ankylosing spondylitis in B27 positive Fula is lower than in B27 positive Caucasians. This is not explained by the B27 subtype distribution among Fula, and suggests the presence of some non-B27 protective factor reducing the prevalence of ankylosing spondylitis in this population.

Ankylosing spondylitis is associated with the anthrax toxin receptor 2 gene (ANTXR2)

Objectives ANTXR2 variants have been associated with ankylosing spondylitis (AS) in two previous genome-wide association studies (GWAS) (p∼9×10-8). However, a genome-wide significant association (p<5×10-8) was not observed. We conducted a more comprehensive analysis of ANTXR2 in an independent UK sample to confirm and refine this association. Methods A replication study was carried out with 2978 cases and 8365 controls. Then, these were combined with non-overlapping samples from the two previous GWAS in a meta-analysis. Human leukocyte antigen (HLA)-B27 stratification was also performed to test for ANTXR2-HLA-B27 interaction. Results Out of nine single nucleotide polymorphisms (SNP) in the study, five SNPs were nominally associated (p<0.05) with AS in the replication dataset. In the meta-analysis, eight SNPs showed evidence of association, the strongest being with rs12504282 (OR=0.88, p=6.7×10-9). Seven of these SNPs showed evidence for association in the HLA-B27-positive subgroup, but none was associated with HLA-B27-negative AS. However, no statistically significant interaction was detected between HLA-B27 and ANTXR2 variants. Conclusions ANTXR2 variants are clearly associated with AS. The top SNPs from two previous GWAS (rs4333130 and rs4389526) and this study (rs12504282) are in strong linkage disequilibrium (r2≥0.76). All are located near a putative regulatory region. Further studies are required to clarify the role played by these ANTXR2 variants in AS.

Author's reply to: Ankylosing spondylitis: evidence for a non-HLA-B*27 protective effect, Sjef Van Der Linden, Desieree Van Der Heijde

As mentioned in the letter by van der Linden and van der Heijde, Jurgen Braun’s excellent recent paper describing a survey of blood donors by questionnaire, clinical, and magnetic resonance imaging examinations revealed a prevalence of ankylosing spondylitis in B27 positive blood donors (6.4%)1-1 very similar to that reported by Gran et al(6.7%).1-2 It is probable that some of the differences in reported prevalence of ankylosing spondylitis by the various studies are because of methodological differences.

A genome-wide screen for susceptibility loci in ankylosing spondylitis

Objective. To localize the regions containing genes that determine susceptibility to ankylosing spondylitis (AS). Methods. One hundred five white British families with 121 affected sibling pairs with AS were recruited, largely from the Royal National Hospital for Rheumatic Diseases AS database. A genome-wide linkage screen was undertaken using 254 highly polymorphic microsatellite markers from the Medical Research Council (UK) (MRC) set. The major histocompatibility complex (MHC) region was studied more intensively using 5 microsatellites lying within the HLA class III region and HLA-DRB1 typing. The Analyze package was used for 2-point analysis, and GeneHunter for multipoint analysis. Results. When only the MRC set was considered, 11 markers in 7 regions achieved a P value of ≤0.01. The maximum logarithm of odds score obtained was 3.8 (P = 1.4 x 10-5) using marker D6S273, which lies in the HLA class III region. A further marker used in mapping of the MHC class III region achieved a LOD score of 8.1 (P = 1 x 10-9). Nine of 118 affected sibling pairs (7.6%) did not share parental haplotypes identical by descent across the MHC, suggesting that only 31% of the susceptibility to AS is coded by genes linked to the MHC. The maximum non-MHC LOD score obtained was 2.6 (P = 0.0003) for marker D16S422. Conclusion. The results of this study confirm the strong linkage of the MHC with AS, and provide suggestive evidence regarding the presence and location of non-MHC genes influencing susceptibility to the disease.

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 X-chromosome and susceptibility to ankylosing spondylitis

Objective. Ankylosing spondylitis (AS) affects 0.25-1.0% of the population, and its etiology is incompletely understood. Susceptibility to this highly familial disease (λ(s) = 58) is primarily genetically determined. There is a significant sex bias in AS, and there are differences in recurrence risk to the offspring of affected mothers and fathers, suggesting that there may be an X-linked recessive effect. We undertook an X- chromosome linkage study to determine any contribution of the X-chromosome to AS susceptibility. Methods. A linkage study of the X-chromosome using 234 affected sibling pairs was performed to investigate this hypothesis. Results. No linkage of the X-chromosome with susceptibility to AS was found. Model- free multipoint linkage analysis strongly excluded any significant genetic contribution (λ ≥1.5) to AS susceptibility encoded on the X-chromosome (logarithm of odds [LOD] <-2.0). Smaller genetic effects (A ≥1.3) were also found to be unlikely (LOD <-1.0). Conclusion. The sex bias in AS is not explained by X-chromosome-encoded genetic effects. The disease model best explaining the sex bias in occurrence and transmission of AS is a polygenic model with a higher susceptibility threshold in females.

Whole-genome linkage analysis of rheumatoid arthritis susceptibility loci in 252 affected sibling pairs in the United Kingdom

Objective. To undertake a systematic wholegenome screen to identify regions exhibiting genetic linkage to rheumatoid arthritis (RA). Methods. Two hundred fifty-two RA-affected sibling pairs from 182 UK families were genotyped using 365 highly informative microsatellite markers. Microsatellite genotyping was performed using fluorescent polymerase chain reaction primers and semiautomated DNA sequencing technology. Linkage analysis was undertaken using MAPMAKER/SIBS for single-point and multipoint analysis. Results. Significant linkage (maximum logarithm of odds score 4.7 [P = 0.000003] at marker D6S276, 1 cM from HLA-DRB1) was identified around the major histocompatibility complex (MHC) region on chromosome 6. Suggestive linkage (P < 7.4 × 10-4) was identified on chromosome 6q by single- and multipoint analysis. Ten other sites of nominal linkage (P < 0.05) were identified on chromosomes 3p, 4q, 7p, 2 regions of 10q, 2 regions of 14q, 16p, 21q, and Xq by single-point analysis and on 3 sites (1q, 14q, and 14q) by multipoint analysis. Conclusion. Linkage to the MHC region was confirmed. Eleven non-HLA regions demonstrated evidence of suggestive or nominal linkage, but none reached the genome-wide threshold for significant linkage (P = 2.2 × 10-5). Results of previous genome screens have suggested that 6 of these regions may be involved in RA susceptibility.