Observational studies have demonstrated an association between decreased vitamin D level and risk of multiple sclerosis (MS); however, it remains unclear whether this relationship is causal. We ...undertook a Mendelian randomization (MR) study to evaluate whether genetically lowered vitamin D level influences the risk of MS.
We identified single nucleotide polymorphisms (SNPs) associated with 25-hydroxyvitamin D (25OHD) level from SUNLIGHT, the largest (n = 33,996) genome-wide association study to date for vitamin D. Four SNPs were genome-wide significant for 25OHD level (p-values ranging from 6 × 10-10 to 2 × 10-109), and all four SNPs lay in, or near, genes strongly implicated in separate mechanisms influencing 25OHD. We then ascertained their effect on 25OHD level in 2,347 participants from a population-based cohort, the Canadian Multicentre Osteoporosis Study, and tested the extent to which the 25OHD-decreasing alleles explained variation in 25OHD level. We found that the count of 25OHD-decreasing alleles across these four SNPs was strongly associated with lower 25OHD level (n = 2,347, F-test statistic = 49.7, p = 2.4 × 10-12). Next, we conducted an MR study to describe the effect of genetically lowered 25OHD on the odds of MS in the International Multiple Sclerosis Genetics Consortium study, the largest genetic association study to date for MS (including up to 14,498 cases and 24,091 healthy controls). Alleles were weighted by their relative effect on 25OHD level, and sensitivity analyses were performed to test MR assumptions. MR analyses found that each genetically determined one-standard-deviation decrease in log-transformed 25OHD level conferred a 2.0-fold increase in the odds of MS (95% CI: 1.7-2.5; p = 7.7 × 10-12; I2 = 63%, 95% CI: 0%-88%). This result persisted in sensitivity analyses excluding SNPs possibly influenced by population stratification or pleiotropy (odds ratio OR = 1.7, 95% CI: 1.3-2.2; p = 2.3 × 10-5; I2 = 47%, 95% CI: 0%-85%) and including only SNPs involved in 25OHD synthesis or metabolism (ORsynthesis = 2.1, 95% CI: 1.6-2.6, p = 1 × 10-9; ORmetabolism = 1.9, 95% CI: 1.3-2.7, p = 0.002). While these sensitivity analyses decreased the possibility that pleiotropy may have biased the results, residual pleiotropy is difficult to exclude entirely.
A genetically lowered 25OHD level is strongly associated with increased susceptibility to MS. Whether vitamin D sufficiency can delay, or prevent, MS onset merits further investigation in long-term randomized controlled trials.
Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, ...their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.
Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be ...screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program.
A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk.
Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation ...array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case-control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.
Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on ...cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches.
We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case-control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls.
We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.
Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to ...identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes.
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified ...associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
Dysembryoplastic neuroepithelial tumor (DNET) is a benign brain tumor associated with intractable drug-resistant epilepsy. In order to identify underlying genetic alterations and molecular ...mechanisms, we examined three family members affected by multinodular DNETs as well as 100 sporadic tumors from 96 patients, which had been referred to us as DNETs. We performed whole-exome sequencing on 46 tumors and targeted sequencing for hotspot
FGFR1
mutations and BRAF p.V600E was used on the remaining samples. FISH, copy number variation assays and Sanger sequencing were used to validate the findings. By whole-exome sequencing of the familial cases, we identified a novel germline
FGFR1
mutation, p.R661P. Somatic activating
FGFR1
mutations (p.N546K or p.K656E) were observed in the tumor samples and further evidence for functional relevance was obtained by in silico modeling. The FGFR1 p.K656E mutation was confirmed to be
in cis
with the germline p.R661P variant. In 43 sporadic cases, in which the diagnosis of DNET could be confirmed on central blinded neuropathology review, FGFR1 alterations were also frequent and mainly comprised intragenic tyrosine kinase
FGFR1
duplication and multiple mutants
in cis
(25/43; 58.1 %) while BRAF p.V600E alterations were absent (0/43). In contrast, in 53 cases, in which the diagnosis of DNET was not confirmed,
FGFR1
alterations were less common (10/53; 19 %;
p
< 0.0001) and hotspot BRAF p.V600E (12/53; 22.6 %) (
p
< 0.001) prevailed. We observed overexpression of phospho-ERK in FGFR1 p.R661P and p.N546K mutant expressing HEK293 cells as well as
FGFR1
mutated tumor samples, supporting enhanced MAP kinase pathway activation under these conditions. In conclusion, constitutional and somatic
FGFR1
alterations and MAP kinase pathway activation are key events in the pathogenesis of DNET. These findings point the way towards existing targeted therapies.
The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to ...address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.
Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently ...developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility.
The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains.
The "probability of being loss-of-function intolerant" (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills.
Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.
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