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Sporadic amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease, most likely results from complex genetic and environmental interactions. Although a number of association studies have been performed in an effort to find genetic components of sporadic ALS, most of them resulted in inconsistent findings due to a small number of genes investigated in relatively small sample sizes, while the replication of results was rarely attempted. Defects in retrograde axonal transport, vesicle trafficking and xenobiotic metabolism have been implicated in neurodegeneration and motor neuron death both in human disease and animal models. To assess the role of common genetic variation in these pathways in susceptibility to sporadic ALS, we performed a pathway-based candidate gene case-control association study with replication. Furthermore, we determined reliability of whole genome amplified DNA in a large-scale association study. In the first stage of the study, 1277 putative functional and tagging SNPs in 134 genes spanning 8.7 Mb were genotyped in 822 British sporadic ALS patients and 872 controls using whole genome amplified DNA. To detect variants with modest effect size and discriminate among false positive findings 19 SNPs showing a trend of association in the initial screen were genotyped in a replication sample of 580 German sporadic ALS patients and 361 controls. We did not detect strong evidence of association with any of the genes investigated in the discovery sample (lowest uncorrected P-value 0.00037, lowest permutation corrected P-value 0.353). None of the suggestive associations was replicated in a second sample, further excluding variants with moderate effect size. We conclude that common variation in the investigated pathways is unlikely to have a major effect on susceptibility to sporadic ALS. The genotyping efficiency was only slightly decreased ( approximately 1%) and genotyping quality was not affected using whole genome amplified DNA. It is reliable for large scale genotyping studies of diseases such as ALS, where DNA sample collections are limited because of low disease prevalence and short survival time. [ABSTRACT FROM AUTHOR]

We assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits that has been made available on the individuals in UKB, using a unified pipeline for PRS evaluation. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 76 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in a separate cohort (100,000 Genomes Project). The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit., Competing Interests: Peter Donnelly and Gil McVean are partners in Peptide Groove LLP. Deborah Thompson, Daniel Wells, Saskia Selzam, Iliana Peneva, Rachel Moore, Kevin Sharp, William Tarran, Edward Beard,Fernando Riveros-Mckay, Carla Giner-Delgado, Duncan Palmer, Priyanka Seth, James Harrison, Gil McVean, Vincent Plagnol, Peter Donnelly and Michael Weale are current or former employees of Genomics plc, and are or have been in possession of stock or stock options for Genomics plc. Peter Donnelly and Gil McVean are Founders and Directors of Genomics plc, and Peter Donnelly is the CEO of Genomics plc. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Thompson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Aims: A cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown., Methods: The Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone., Results: The distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28×10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1×10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores., Conclusions: In a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: The aim of the study was to assess the real-world feasibility, acceptability, and impact of an integrated risk tool for cardiovascular disease (CVD IRT, combining the standard QRISK®2 risk algorithm with a polygenic risk score), implemented within routine primary practice in the UK National Health Service., Methods and Results: The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) evaluated participants undergoing primary care health checks. Both QRISK2 and CVD IRT scores were returned to the healthcare providers (HCPs), who then communicated the results to participants. The primary outcome of the study was feasibility of CVD IRT implementation. Secondary outcomes included changes in CVD risk (QRISK2 vs. CVD IRT) and impact of the CVD IRT on clinical decision-making. A total of 832 eligible participants (median age 55 years, 62% females, 97.5% White ethnicity) were enrolled across 12 UK primary care practices. Cardiovascular disease IRT scores were obtained on 100% of the blood samples. Healthcare providers stated that the CVD IRT could be incorporated into routine primary care in a straightforward manner in 90.7% of reports. Participants stated they were 'likely' or 'very likely' to recommend the use of this test to their family or friends in 86.9% of reports. Participants stated that the test was personally useful (98.8%) and that the results were easy to understand (94.6%). When CVD IRT exceeded QRISK2, HCPs planned changes in management for 108/388 (27.8%) of participants and 47% (62/132) of participants with absolute risk score changes of >2%., Conclusion: Amongst HCPs and participants who agreed to the trial of genetic data for refinement of clinical risk prediction in primary care, we observed that CVD IRT implementation was feasible and well accepted. The CVD IRT results were associated with planned changes in prevention strategies., Competing Interests: Conflict of interests: A.F. is an Honorary Professor of Primary Care Cardiology, at Durham University; GP, GP Appraiser and GPSI Cardiology Darlington; Past President and current Education and Research Lead Primary Care Cardiovascular Society; NENC AHSN Lipid champion and Primary Care Clinical Lead for Lipids; NIHR NENC LCRN Clinical Research Network GP Engagement Lead; and Darlington PCN CVD and Research Lead. Some authors (A.F., E.L., J.W., F.J.K., and Y.S.) were study investigators who also contributed to clinical surveys and interviews. M.M. is a consultant to Genomics plc. S.H. has received NIHR-funded grants, is Deputy Implementation lead of NIHR ARC North East & North Cumbria, and is on the NIHR Editorial board for alerts. S.H. and H.R. are employees of NHS North of England Commissioning Support (NECS). E.A., S.G., C.G.-D., M.E.W., A.L., V.P., S.H., P.D., and R.C. are employees of Genomics plc., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed., (© 2023. Springer Nature Limited.)

Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data have only been obtained from a small proportion of present-day ethnolinguistic groups. By analyzing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria, and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example, correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relate to the expansions of Bantu language-speaking peoples.

The American College of Cardiology / American Heart Association pooled cohort equations tool (ASCVD-PCE) is currently recommended to assess 10-year risk for atherosclerotic cardiovascular disease (ASCVD). ASCVD-PCE does not currently include genetic risk factors. Polygenic risk scores (PRSs) have been shown to offer a powerful new approach to measuring genetic risk for common diseases, including ASCVD, and to enhance risk prediction when combined with ASCVD-PCE. Most work to date, including the assessment of tools, has focused on performance in individuals of European ancestries. Here we present evidence for the clinical validation of a new integrated risk tool (IRT), ASCVD-IRT, which combines ASCVD-PCE with PRS to predict 10-year risk of ASCVD across diverse ethnicity and ancestry groups. We demonstrate improved predictive performance of ASCVD-IRT over ASCVD-PCE, not only in individuals of self-reported White ethnicities (net reclassification improvement [NRI]; with 95% confidence interval = 2.7% [1.1 to 4.2]) but also Black / African American / Black Caribbean / Black African (NRI = 2.5% [0.6-4.3]) and South Asian (Indian, Bangladeshi or Pakistani) ethnicities (NRI = 8.7% [3.1 to 14.4]). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% [-1.4 to 16.5]), but PRS effect sizes in these ethnicities were significant and of comparable size to those seen in individuals of White ethnicities. Comparable results were obtained when individuals were analyzed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favor their generalization to all ethnicities and ancestries., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment., Methods: Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals., Results: The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period., Conclusions: An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Dystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in patients with dystonia. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systems-biology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships among dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from 10 human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinson's disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, 4 of 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrate significant enrichments of the heritability of major depressive disorder, obsessive-compulsive disorder and schizophrenia within the putamen and white matter modules, and a significant enrichment of the heritability of Parkinson's disease within the substantia nigra module. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/.

Selection of antihypertensive treatment according to self-defined ethnicity is recommended by some guidelines but might be better guided by individual genotype rather than ethnicity or race. We compared the extent to which variation in blood pressure response across different ethnicities may be explained by genetic factors: genetically defined ancestry and gene variants at loci known to be associated with blood pressure. We analyzed data from 5 trials in which genotyping had been performed (n=4696) and in which treatment responses to β-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blocker, thiazide or thiazide-like diuretic and calcium channel blocker were available. Genetically defined ancestry for proportion of African ancestry was computed using the 1000 genomes population database as a reference. Differences in response to the thiazide diuretic hydrochlorothiazide, the β-blockers atenolol and metoprolol, the angiotensin-converting enzyme inhibitor lisinopril, and the angiotensin receptor blocker candesartan were more closely associated to genetically defined ancestry than self-defined ethnicity in admixed subjects. A relatively small number of gene variants related to loci associated with drug-signaling pathways (KCNK3, SULT1C3, AMH, PDE3A, PLCE1, PRKAG2) with large effect size (-3.5 to +3.5 mm Hg difference in response per allele) and differing allele frequencies in black versus white individuals explained a large proportion of the difference in response to candesartan and hydrochlorothiazide between these groups. These findings suggest that a genomic precision medicine approach can be used to individualize antihypertensive treatment within and across populations without recourse to surrogates of genetic structure such as self-defined ethnicity.

Genetic variation across the human leukocyte antigen loci is known to influence renal-transplant outcome. However, the impact of genetic variation beyond the human leukocyte antigen loci is less clear. We tested the association of common genetic variation and clinical characteristics, from both the donor and recipient, with posttransplant eGFR at different time-points, out to 5 years posttransplantation. We conducted GWAS meta-analyses across 10 844 donors and recipients from five European ancestry cohorts. We also analyzed the impact of polygenic risk scores (PRS), calculated using genetic variants associated with nontransplant eGFR, on posttransplant eGFR. PRS calculated using the recipient genotype alone, as well as combined donor and recipient genotypes were significantly associated with eGFR at 1-year posttransplant. Thirty-two percent of the variability in eGFR at 1-year posttransplant was explained by our model containing clinical covariates (including weights for death/graft-failure), principal components and combined donor-recipient PRS, with 0.3% contributed by the PRS. No individual genetic variant was significantly associated with eGFR posttransplant in the GWAS. This is the first study to examine PRS, composed of variants that impact kidney function in the general population, in a posttransplant context. Despite PRS being a significant predictor of eGFR posttransplant, the effect size of common genetic factors is limited compared to clinical variables., (© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Mesial temporal lobe epilepsy with hippocampal sclerosis represents the most common epilepsy syndrome in adult patients with medically intractable partial epilepsy. Mesial temporal lobe epilepsy is usually regarded as a polygenic and complex disorder, still poorly understood but probably caused and perpetuated by dysregulation of numerous biological networks and cellular functions. The study of gene expression changes by single nucleotide polymorphisms in regulatory elements (expression quantitative trait loci, eQTLs) has been shown to be a powerful complementary approach to the detection and understanding of risk loci by genome-wide association studies. We performed a whole (gene and exon-level) transcriptome analysis on cortical tissue samples (Brodmann areas 20 and 21) from 86 patients with mesial temporal lobe epilepsy with hippocampal sclerosis and 75 neurologically healthy controls. Genome-wide genotyping data from the same individuals (patients and controls) were analysed and paired with the transcriptome data. We report potential epilepsy-risk eQTLs, some of which are specific to tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis. We also found large transcriptional and splicing deregulation in mesial temporal lobe epilepsy with hippocampal sclerosis tissue as well as gene networks involving neuronal and glial mechanisms that provide new insights into the cause and maintenance of the seizures. These data (available via the 'Seizubraineac' web-tool resource, www.seizubraineac.org) will facilitate the identification of new therapeutic targets and biomarkers as well as genetic risk variants that could influence epilepsy and pharmacoresistance., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Genome-wide association studies (GWASs) have allowed researchers to identify thousands of single nucleotide polymorphisms (SNPs) and other variants associated with particular complex traits. Previous studies have reported differences in the strength and even the direction of GWAS signals across different populations. These differences could be due to a combination of (1) lack of power, (2) allele frequency differences, (3) linkage disequilibrium (LD) differences, and (4) true differences in causal variant effect sizes. To determine whether properties (1)-(3) on their own might be sufficient to explain the patterns previously noted in strong GWAS signals, we simulated case-control data of European, Asian and African ancestry, applying realistic allele frequencies and LD from 1000 Genomes data but enforcing equal causal effect sizes across populations. Much of the observed differences in strong GWAS signals could indeed be accounted for by allele frequency and LD differences, enhanced by the Euro-centric SNP bias and lower SNP coverage found in older GWAS panels. While we cannot rule out a role for true transethnic effect size differences, our results suggest that strong causal effects may be largely shared among human populations, motivating the use of transethnic data for fine-mapping., (© 2018 John Wiley & Sons Ltd/University College London.)

Improvements in immunosuppression have modified short-term survival of deceased-donor allografts, but not their rate of long-term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long-term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large-scale genome-wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased-donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short-term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application., (© 2018 The Authors. American Journal of Transplantation published by Wiley Periodicals, Inc. on behalf of The American Society of Transplantation and the American Society of Transplant Surgeons.)

Pharmacogenetics (PGx) has the potential to personalize pharmaceutical treatments. Many relevant gene-drug associations have been discovered, but PGx-guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard health-care. We reviewed economic evaluations for PGx associations listed in the US Food and Drug Administration (FDA) Table of Pharmacogenomic Biomarkers in Drug Labeling. We determined the proportion of evaluations that found PGx-guided treatment to be cost-effective or dominant over the alternative strategies, and estimated the impact on this proportion of removing the cost of genetic testing. Of the 137 PGx associations in the FDA table, 44 economic evaluations, relating to 10 drugs, were identified. Of these evaluations, 57% drew conclusions in favour of PGx testing, of which 30% were cost-effective and 27% were dominant (cost-saving). If genetic information was freely available, 75% of economic evaluations would support PGx-guided treatment, of which 25% would be cost-effective and 50% would be dominant. Thus, PGx-guided treatment can be a cost-effective and even a cost-saving strategy. Having genetic information readily available in the clinical health record is a realistic future prospect, and would make more genetic tests economically worthwhile.

Zoroastrianism is one of the oldest extant religions in the world, originating in Persia (present-day Iran) during the second millennium BCE. Historical records indicate that migrants from Persia brought Zoroastrianism to India, but there is debate over the timing of these migrations. Here we present genome-wide autosomal, Y chromosome, and mitochondrial DNA data from Iranian and Indian Zoroastrians and neighboring modern-day Indian and Iranian populations and conduct a comprehensive genome-wide genetic analysis in these groups. Using powerful haplotype-based techniques, we find that Zoroastrians in Iran and India have increased genetic homogeneity relative to other sampled groups in their respective countries, consistent with their current practices of endogamy. Despite this, we infer that Indian Zoroastrians (Parsis) intermixed with local groups sometime after their arrival in India, dating this mixture to 690-1390 CE and providing strong evidence that Iranian Zoroastrian ancestry was maintained primarily through the male line. By making use of the rich information in DNA from ancient human remains, we also highlight admixture in the ancestors of Iranian Zoroastrians dated to 570 BCE-746 CE, older than admixture seen in any other sampled Iranian group, consistent with a long-standing isolation of Zoroastrians from outside groups. Finally, we report results, and challenges, from a genome-wide scan to identify genomic regions showing signatures of positive selection in present-day Zoroastrians that might correlate to the prevalence of particular diseases among these communities., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry. We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn )., Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices., Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (r g =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρ genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

Objectives: We assessed the current genetic evidence for the involvement of various cell types and tissue types in the etiology of neurodegenerative diseases, especially in relation to the neuroinflammatory hypothesis of neurodegenerative diseases., Methods: We obtained large-scale genome-wide association study (GWAS) summary statistics from Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). We used multiple sclerosis (MS), an autoimmune disease of the central nervous system, as a positive control. We applied stratified LD score regression to determine if functional marks for cell type and tissue activity, and gene-set lists were enriched for genetic heritability. We compared our results to those from two gene-set enrichment methods (Ingenuity Pathway Analysis and enrichr)., Results: There were no significant heritability enrichments for annotations marking genes active within brain regions, but there were significant heritability enrichments for annotations marking genes active within cell types that form part of both the innate and adaptive immune systems. We found this for MS (as expected) and also for AD and PD. The strongest signals were from the adaptive immune system (e.g., T cells) for PD, and from both the adaptive (e.g., T cells) and innate (e.g., CD14: a marker for monocytes, and CD15: a marker for neutrophils) immune systems for AD. Annotations from the liver were also significant for AD. Pathway analysis provided complementary results., Interpretation: For AD and PD, we found significant enrichment of heritability in annotations marking gene activity in immune cells.

The importance of the innate immune system, including complement, in causing transplant injury and augmenting adaptive immune responses is increasingly recognized. Therefore variability in graft outcome may in part be due to genetic polymorphism in genes encoding proteins of the immune system. This study assessed the relationship between single nucleotide polymorphisms (SNPs) in complement genes and outcome after transplantation. Analysis was performed on two patient cohorts of 650 and 520 transplant recipients. 505 tagged SNPs in 47 genes were typed in both donor and recipient. The relationships between SNPs and graft survival, serum creatinine, delayed graft function and acute rejection were analyzed. One recipient SNP in the gene encoding mannose binding lectin was associated with graft outcome after correction for analysis of multiple SNPs (p=6.41 × 10(-5)). When further correction was applied to account for analysis of the effect of SNPs in both donor and recipient this lost significance. Despite association p values of <0.001 no SNP was significantly associated with clinical phenotypes after Bonferroni correction. In conclusion, the variability seen in transplant outcome in this patient cohort cannot be explained by variation in complement genes. If causal genetic effects exist in these genes, they are too small to be detected by this study., (Copyright © 2015 Elsevier GmbH. All rights reserved.)

Background: In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products., Results: Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses., Conclusions: This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work.

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/., (© 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.)

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

Background: In silico models have recently been created in order to predict which genetic variants are more likely to contribute to the risk of a complex trait given their functional characteristics. However, there has been no comprehensive review as to which type of predictive accuracy measures and data visualization techniques are most useful for assessing these models., Methods: We assessed the performance of the models for predicting risk using various methodologies, some of which include: receiver operating characteristic (ROC) curves, histograms of classification probability, and the novel use of the quantile-quantile plot. These measures have variable interpretability depending on factors such as whether the dataset is balanced in terms of numbers of genetic variants classified as risk variants versus those that are not., Results: We conclude that the area under the curve (AUC) is a suitable starting place, and for models with similar AUCs, violin plots are particularly useful for examining the distribution of the risk scores.

It is generally believed that splicing removes introns as single units from precursor messenger RNA transcripts. However, some long Drosophila melanogaster introns contain a cryptic site, known as a recursive splice site (RS-site), that enables a multi-step process of intron removal termed recursive splicing. The extent to which recursive splicing occurs in other species and its mechanistic basis have not been examined. Here we identify highly conserved RS-sites in genes expressed in the mammalian brain that encode proteins functioning in neuronal development. Moreover, the RS-sites are found in some of the longest introns across vertebrates. We find that vertebrate recursive splicing requires initial definition of an 'RS-exon' that follows the RS-site. The RS-exon is then excluded from the dominant mRNA isoform owing to competition with a reconstituted 5' splice site formed at the RS-site after the first splicing step. Conversely, the RS-exon is included when preceded by cryptic promoters or exons that fail to reconstitute an efficient 5' splice site. Most RS-exons contain a premature stop codon such that their inclusion can decrease mRNA stability. Thus, by establishing a binary splicing switch, RS-sites demarcate different mRNA isoforms emerging from long genes by coupling cryptic elements with inclusion of RS-exons.

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question. Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants. Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn's disease and 480 controls. 80 variants with evidence of association in the sequencing experiment or with potential functional significance were selected for follow up genotyping in 6,507 IBD cases and 3,064 population controls. The top 5 disease associated variants were genotyped in an extension panel of 3,662 IBD cases and 3,639 controls, and tested for association in a combined analysis of 10,147 IBD cases and 7,008 controls. A rare coding variant p.G454C in the BTNL2 gene within the major histocompatibility complex was significantly associated with increased risk for IBD (p = 9.65x10-10, OR = 2.3[95% CI = 1.75-3.04]), but was independent of the known common associated CD and UC variants at this locus. Rare (<1%) and low frequency (1-5%) variants in 3 additional genes showed suggestive association (p<0.005) with either an increased risk (ARIH2 c.338-6C>T) or decreased risk (IL12B p.V298F, and NICN p.H191R) of IBD. These results provide additional insights into the involvement of the inhibition of T cell activation in the development of both sub-phenotypes of inflammatory bowel disease. We suggest that although rare coding variants may make a modest overall contribution to complex disease susceptibility, they can inform our understanding of the molecular pathways that contribute to pathogenesis.

Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

Background: Any reduction in myocardial oxygen delivery relative to its demands can impair cardiac contractile performance. Understanding the mitochondrial metabolic response to hypoxia is key to understanding ischemia tolerance in the myocardium. We used a novel combination of 2 genome-scale methods to study key processes underlying human myocardial hypoxia tolerance. In particular, we hypothesized that computational modeling and evolution would identify similar genes as critical to human myocardial hypoxia tolerance., Methods and Results: We analyzed a reconstruction of the cardiac mitochondrial metabolic network using constraint-based methods, under conditions of simulated hypoxia. We used flux balance analysis, random sampling, and principal component analysis to explore feasible steady-state solutions. Hypoxia blunted maximal ATP (-17%) and heme (-75%) synthesis and shrank the feasible solution space. Tricarboxylic acid and urea cycle fluxes were also reduced in hypoxia, but phospholipid synthesis was increased. Using mathematical optimization methods, we identified reactions that would be critical to hypoxia tolerance in the human heart. We used data regarding single-nucleotide polymorphism frequency and distribution in the genomes of Tibetans (whose ancestors have resided in persistent high-altitude hypoxia for several millennia). Six reactions were identified by both methods as being critical to mitochondrial ATP production in hypoxia: phosphofructokinase, phosphoglucokinase, complex II, complex IV, aconitase, and fumarase., Conclusions: Mathematical optimization and evolution converged on similar genes as critical to human myocardial hypoxia tolerance. Our approach is unique and completely novel and demonstrates that genome-scale modeling and genomics can be used in tandem to provide new insights into cardiovascular genetics., (© 2014 American Heart Association, Inc.)

Progressive supranuclear palsy is a rare parkinsonian disorder with characteristic neurofibrillary pathology consisting of hyperphosphorylated tau protein. Common variation defining the microtubule associated protein tau gene (MAPT) H1 haplotype strongly contributes to disease risk. A recent genome-wide association study (GWAS) revealed 3 novel risk loci on chromosomes 1, 2, and 3 that primarily implicate STX6, EIF2AK3, and MOBP, respectively. Genetic associations, however, rarely lead to direct identification of the relevant functional allele. More often, they are in linkage disequilibrium with the causative polymorphism(s) that could be a coding change or affect gene expression regulatory motifs. To identify any such changes, we sequenced all coding exons of those genes directly implicated by the associations in progressive supranuclear palsy cases and analyzed regional gene expression data from control brains to identify expression quantitative trait loci within 1 Mb of the risk loci. Although we did not find any coding variants underlying the associations, GWAS-associated single-nucleotide polymorphisms at these loci are in complete linkage disequilibrium with haplotypes that completely overlap with the respective genes. Although implication of EIF2AK3 and MOBP could not be fully assessed, we show that the GWAS single-nucleotide polymorphism rs1411478 (STX6) is a strong expression quantitative trait locus with significantly lower expression of STX6 in white matter in carriers of the risk allele., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals ("hits") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.

Background: Multiple sclerosis (MS) is a common disease of the central nervous system and a major cause of disability amongst young adults. Genome-wide association studies have identified many novel susceptibility loci including rs2248359. We hypothesized that genotypes of this locus could increase the risk of MS by regulating expression of neighboring gene, CYP24A1 which encodes the enzyme responsible for initiating degradation of 1,25-dihydroxyvitamin D3., Methods: We investigated this hypothesis using paired gene expression and genotyping data from three independent datasets of neurologically healthy adults of European descent. The UK Brain Expression Consortium (UKBEC) consists of post-mortem samples across 10 brain regions originating from 134 individuals (1231 samples total). The North American Brain Expression Consortium (NABEC) consists of cerebellum and frontal cortex samples from 304 individuals (605 samples total). The brain dataset from Heinzen and colleagues consists of prefrontal cortex samples from 93 individuals. Additionally, we used gene network analysis to analyze UKBEC expression data to understand CYP24A1 function in human brain., Findings: The risk allele, rs2248359-C, is strongly associated with increased expression of CYP24A1 in frontal cortex ( p -value=1.45×10 -13 ), but not white matter. This association was replicated using data from NABEC ( p -value=7.2×10 -6 ) and Heinzen and colleagues ( p -value=1.2×10 -4 ). Network analysis shows a significant enrichment of terms related to immune response in eight out of the 10 brain regions., Interpretation: The known MS risk allele rs2248359-C increases CYP24A1 expression in human brain providing a genetic link between MS and vitamin D metabolism, and predicting that the physiologically active form of vitamin D3 is protective. Vitamin D3's involvement in MS may relate to its immunomodulatory functions in human brain., Funding: Medical Research Council UK; King Faisal Specialist Hospital and Research Centre, Saudi Arabia; Intramural Research Program of the National Institute on Aging, National Institutes of Health, USA.

Rare variants in TREM2 cause susceptibility to late-onset Alzheimer's disease. Here we use microarray-based expression data generated from 101 neuropathologically normal individuals and covering 10 brain regions, including the hippocampus, to understand TREM2 biology in human brain. Using network analysis, we detect a highly preserved TREM2-containing module in human brain, show that it relates to microglia, and demonstrate that TREM2 is a hub gene in 5 brain regions, including the hippocampus, suggesting that it can drive module function. Using enrichment analysis we show significant overrepresentation of genes implicated in the adaptive and innate immune system. Inspection of genes with the highest connectivity to TREM2 suggests that it plays a key role in mediating changes in the microglial cytoskeleton necessary not only for phagocytosis, but also migration. Most importantly, we show that the TREM2-containing module is significantly enriched for genes genetically implicated in Alzheimer's disease, multiple sclerosis, and motor neuron disease, implying that these diseases share common pathways centered on microglia and that among the genes identified are possible new disease-relevant genes., (Copyright © 2013 Elsevier Inc. All rights reserved.)