William S. Bush

Evidence for polygenic susceptibility to multiple sclerosis?The shape of things to come

It is well established that the risk of developing multiple sclerosis is substantially increased in the relatives of affected individuals and that most of this increase is genetically determined. The observed pattern of familial recurrence risk has long suggested that multiple variants are involved, but it has proven difficult to identify individual risk variants and little has been established about the genetic architecture underlying susceptibility. By using data from two independent genome-wide association studies (GWAS), we demonstrate that a substantial proportion of the thousands of variants that individually fail to show statistically significant evidence of association have allele frequencies in cases that are skewed away from the null distribution through the effects of multiple as-yet-unidentified risk loci. The collective effect of 12,627 SNPs with Cochran-Mantel-Haenszel test (p < 0.2) in our discovery GWAS set optimally explains approximately 3% of the variance in MS risk in our independent target GWAS set, estimated by Nagelkerkes pseudo-R(2). This model has a highly significant fit (p = 9.90E-19). These results statistically demonstrate a polygenic component to MS susceptibility and suggest that the risk alleles identified to date represent just the tip of an iceberg of risk variants likely to include hundreds of modest effects and possibly thousands of very small effects.

Biofilter: A Knowledge-Integration System for the Multi-Locus Analysis of Genome-Wide Association Studies

Genome-wide association studies provide an unprecedented opportunity to identify combinations of genetic variants that contribute to disease susceptibility. The combinatorial problem of jointly analyzing the millions of genetic variations accessible by high-throughput genotyping technologies is a difficult challenge. One approach to reducing the search space of this variable selection problem is to assess specific combinations of genetic variations based on prior statistical and biological knowledge. In this work, we provide a systematic approach to integrate multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. This approach yields a collection of models which can be tested statistically in genome-wide data, along with an ordinal quantity describing the number of data sources that support any given model. Using this knowledge-driven approach reduces the computational and statistical burden of large-scale interaction analysis while simultaneously providing a biological foundation for the relevance of any significant statistical result that is found.

Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records.

IMPORTANCEnLarge-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records (EMRs) may provide a resource to assess the clinical relevance of rare variants.nnnOBJECTIVEnTo determine the clinical phenotypes from EMRs for individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes.nnnDESIGN, SETTING, AND PARTICIPANTSnThis prospective cohort study included 2022 individuals recruited for nonantiarrhythmic drug exposure phenotypes from October 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmacogenomics project from 7 US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada syndromes, were assessed for potential pathogenicity by 3 laboratories with ion channel expertise and by comparison with the ClinVar database. Relevant phenotypes were determined from EMRs, with data available from 2002 (or earlier for some sites) through September 10, 2014.nnnEXPOSURESnOne or more variants designated as pathogenic in SCN5A or KCNH2.nnnMAIN OUTCOMES AND MEASURESnArrhythmia or electrocardiographic (ECG) phenotypes defined by International Classification of Diseases, Ninth Revision (ICD-9) codes, ECG data, and manual EMR review.nnnRESULTSnAmong 2022 study participants (median age, 61 years [interquartile range, 56-65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen κu2009=u20090.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, -5% to +13%; Pu2009=u2009.35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, -10 milliseconds; 95% CI, -16 to +3 milliseconds; Pu2009=u2009.17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds.nnnCONCLUSIONS AND RELEVANCEnAmong laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.

The phenotypic legacy of admixture between modern humans and Neandertals

The legacy of human-Neandertal interbreeding Non-African humans are estimated to have inherited on average 1.5 to 4% of their genomes from Neandertals. However, how this genetic legacy affects human traits is unknown. Simonti et al. combined genotyping data with electronic health records. Individual Neandertal alleles were correlated with clinically relevant phenotypes in individuals of European descent. These archaic genetic variants were associated with medical conditions affecting the skin, the blood, and the risk of depression. Science, this issue p. 737 Genotype-phenotype association analysis of Neandertal alleles in modern humans identifies clinical effects. Many modern human genomes retain DNA inherited from interbreeding with archaic hominins, such as Neandertals, yet the influence of this admixture on human traits is largely unknown. We analyzed the contribution of common Neandertal variants to over 1000 electronic health record (EHR)–derived phenotypes in ~28,000 adults of European ancestry. We discovered and replicated associations of Neandertal alleles with neurological, psychiatric, immunological, and dermatological phenotypes. Neandertal alleles together explained a significant fraction of the variation in risk for depression and skin lesions resulting from sun exposure (actinic keratosis), and individual Neandertal alleles were significantly associated with specific human phenotypes, including hypercoagulation and tobacco use. Our results establish that archaic admixture influences disease risk in modern humans, provide hypotheses about the effects of hundreds of Neandertal haplotypes, and demonstrate the utility of EHR data in evolutionary analyses.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome–phenome relationship.

IL12A, MPHOSPH9/CDK2AP1 and RGS1 are novel multiple sclerosis susceptibility loci

A recent meta-analysis identified seven single-nucleotide polymorphisms (SNPs) with suggestive evidence of association with multiple sclerosis (MS). We report an analysis of these polymorphisms in a replication study that includes 8,085 cases and 7,777 controls. A meta-analysis across the replication collections and a joint analysis with the discovery data set were performed. The possible functional consequences of the validated susceptibility loci were explored using RNA expression data. For all of the tested SNPs, the effect observed in the replication phase involved the same allele and the same direction of effect observed in the discovery phase. Three loci exceeded genome-wide significance in the joint analysis: RGS1 (P value=3.55 × 10−9), IL12A (P=3.08 × 10−8) and MPHOSPH9/CDK2AP1 (P=3.96 × 10−8). The RGS1 risk allele is shared with celiac disease (CD), and the IL12A risk allele seems to be protective for celiac disease. Within the MPHOSPH9/CDK2AP1 locus, the risk allele correlates with diminished RNA expression of the cell cycle regulator CDK2AP1; this effect is seen in both lymphoblastic cell lines (P=1.18 × 10−5) and in peripheral blood mononuclear cells from subjects with MS (P=0.01). Thus, we report three new MS susceptibility loci, including a novel inflammatory disease locus that could affect autoreactive cell proliferation.

Interrogating the complex role of chromosome 16p13.13 in multiple sclerosis susceptibility: independent genetic signals in the CIITA–CLEC16A–SOCS1 gene complex

Multiple sclerosis (MS) is a neurodegenerative, autoimmune disease of the central nervous system, and numerous studies have shown that MS has a strong genetic component. Independent studies to identify MS-associated genes have often indicated multiple signals in physically close genomic regions, although by their proximity it is not always clear if these data indicate redundant or truly independent genetic signals. Recently, three MS study samples were genotyped in parallel using an Illumina Custom BeadChip. These revealed multiple significantly associated single-nucleotide polymorphisms within a 600 kb stretch on chromosome 16p13. Here we present a detailed analysis of variants in this region that clarifies the independent nature of these signals. The linkage disequilibrium patterns in the region and logistic regression analysis of the associations suggest that this region likely harbors three independent MS disease loci. Further, we examined cis-expression QTLs, histone modifications and CCCTC-binding factor (CTCF) binding data in the region. We also tested for correlated expression of the genes from the region using whole-genome expression array data from lymphoblastoid cell lines. Three of the genes show expression correlations across loci. Furthermore, in the GM12878 lymphoblastoid cell line, these three genes are in a continuous region devoid of H3K27 methylation, suggesting an open chromatin configuration. This region likely only contributes minimal risk to MS; however, investigation of this region will undoubtedly provide insight into the functional mechanisms of these genes. These data highlight the importance of taking a closer look at the expression and function of chromosome 16p13 in the pathogenesis of MS.

Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium.

Late-onset Alzheimer disease (AD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance, and gene-gene interactions; however, the investigation of interactions in recent genome-wide association studies has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across 13 data sets from the Alzheimer Disease Genetics Consortium. Fifteen single nucleotide polymorphism (SNP)-SNP pairs within 3 gene-gene combinations were identified: SIRT1xa0× ABCB1, PSAPxa0× PEBP4, and GRIN2Bxa0× ADRA1A. In addition, we extend a previously identified interaction from an endophenotype analysis between RYR3xa0× CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this article highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis.

Is Isolated Low High-Density Lipoprotein Cholesterol a Cardiovascular Disease Risk Factor?

Background—Although the inverse association between high-density lipoprotein cholesterol (HDL-C) and risk of cardiovascular disease (CVD) has been long established, it remains unclear whether low HDL-C remains a CVD risk factor when levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) are not elevated. This is a timely issue because recent studies have questioned whether HDL-C is truly an independent predictor of CVD. Methods and Results—3590 men and women from the Framingham Heart Study offspring cohort without known CVD were followed between 1987 and 2011. Low HDL-C (<40 mg/dL in men and <50 mg/dL in women) was defined as isolated if TG and LDL-C were both low (<100 mg/dL). We also examined higher thresholds for TG (150 mg/dL) and LDL-C (130 mg/dL) and compared low versus high HDL-C phenotypes using logistic regression analysis to assess association with CVD. Compared with isolated low HDL-C, CVD risks were higher when low HDL-C was accompanied by LDL-C ≥100 mg/dL and TG <100 mg/dL (odds ratio 1.3 [1.0, 1.6]), TG ≥100 mg/dL and LDL-C <100 mg/dL (odds ratio 1.3 [1.1, 1.5]), or TG and LDL-C ≥100 mg/dL (odds ratio 1.6, [1.2, 2.2]), after adjustment for covariates. When low HDL-C was analyzed with higher thresholds for TG (≥150 mg/dL) and LDL-C (≥130 mg/dL), results were essentially the same. In contrast, compared with isolated low HDL-C, high HDL-C was associated with 20% to 40% lower CVD risk except when TG and LDL-C were elevated. Conclusions—CVD risk as a function of HDL-C phenotypes is modulated by other components of the lipid panel.