Genome-wide Association Datasets Research Articles

The causal relationship between serum metabolites and acne vulgaris: a Mendelian randomization study

In individuals with acne vulgaris, alterations occur in serum metabolite composition, yet the exact causal link between these metabolites and acne development remains elusive. Using genome-wide association datasets, we performed bidirectional Mendelian randomization (MR) to investigate the potential causal relationship between 309 serum metabolites and acne vulgaris. We performed sensitivity analysis to evaluate the presence of heterogeneity and pleiotropy. Forward MR analysis found 14 serum metabolites significantly associated with acne vulgaris, and reverse MR analysis found no significant association between acne vulgaris and these serum metabolites. Through validation using data from the FinnGen database of acne vulgaris studies, we found a conclusive and significant correlation between stearoylcarnitine and acne vulgaris. This provides new evidence in the search for new targets for the treatment of acne vulgaris.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

PTPN23[Thr] variant reduces susceptibility and tumorigenesis in esophageal squamous cell carcinoma through dephosphorylation of EGFR

Post-translational modifications (PTMs) have emerged as pivotal regulators of the development of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we conducted a comprehensive analysis of PTM-related genetic variants associated with ESCC risk using large-scale genome-wide and exome-wide association datasets. We observed significant enrichment of PTM-related variants in the ESCC risk loci and identified five variants that were significantly associated with ESCC risk. Among them, rs6780013 in PTPN23 exhibited the highest level of significance in ESCC susceptibility in 9,728 ESCC cases and 10,977 controls (odds ratio [OR] = 0.85, 95 % confidence interval [CI] = 0.81– 0.89, P = 9.77 × 10−14). Further functional investigations revealed that PTPN23[Thr] variant binds to EGFR and modulates its phosphorylation at Thr699. PTPN23[Thr] variant substantially inhibited ESCC cell proliferation both in vitro and in vivo. Our findings underscore the critical role of PTPN23[Thr]-EGFR interaction in ESCC development, providing more insights into the pathogenesis of this cancer.

A new method for multiancestry polygenic prediction improves performance across diverse populations.

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

Association between polygenic risk for Alzheimer’s disease and brain structure in children and adults

BackgroundThe correlations between genetic risk for Alzheimer’s disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages.MethodsThis study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life.ResultsCompared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD).ConclusionsIn conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.

Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Bone Mineral Density.

Mounting evidence has supported osteoporosis (OP) as a metabolic disorder. Recent metabolomics studies have discovered numerous metabolites related to bone mineral density (BMD). However, the causal effects of metabolites on BMD at distinct sites remained underexplored. Leveraging genome-wide association datasets, we conducted two-sample Mendelian randomization (MR) analyses to investigate the causal relationship between 486 blood metabolites and bone mineral density at five skeletal sites including heel (H), total body (TB), lumbar spine (LS), femoral neck (FN), and ultra-distal forearm (FA). Sensitivity analyses were performed to test the presence of the heterogeneity and the pleiotropy. To exclude the influences of reverse causation, genetic correlation, and linkage disequilibrium (LD), we further performed reverse MR, linkage disequilibrium regression score (LDSC), and colocalization analyses. In the primary MR analyses, 22, 10, 3, 7, and 2 metabolite associations were established respectively for H-BMD, TB-BMD, LS-BMD, FN-BMD, and FA-BMD at the nominal significance level (IVW, P < 0.05) and passing sensitivity analyses. Among these, one metabolite, androsterone sulfate showed a strong effect on four out of five BMD phenotypes (Odds ratio [OR] for H-BMD = 1.045 [1.020, 1.071]; Odds ratio [OR] for TB-BMD = 1.061 [1.017, 1.107]; Odds ratio [OR] for LS-BMD = 1.088 [1.023, 1.159]; Odds ratio [OR] for FN-BMD = 1.114 [1.054, 1.177]). Reverse MR analysis provided no evidence for the causal effects of BMD measurements on these metabolites. Colocalization analysis have found that several metabolite associations might be driven by shared genetic variants such as mannose for TB-BMD. This study identified some metabolites causally related to BMD at distinct sites and several key metabolic pathways, which shed light on predictive biomarkers and drug targets for OP.

COVID-19 and non-alcoholic fatty liver disease: Biological insights from multi-omics data.

We explored the shared pathophysiological mechanisms between COVID-19 and non-alcoholic fatty liver disease (NAFLD) by integrating multi-omics data. We studied common genetic risk factors and underlying biological processes using functional enrichment analysis. To understand the sex-specific pathways involved in the clinical course of SARS-CoV-2 infection, we processed sex-stratified data from COVID-19 genome-wide association datasets. We further explored the transcriptional signature of the liver cells in healthy and COVID-19 tissue specimens. We also integrated genetic and metabolomic information. We found that COVID-19 and NAFLD share biological disease mechanisms, including pathways that regulate the inflammatory and lipopolysaccharide response. Single-cell transcriptomics revealed enrichment of complement-related pathways in Kupffer cells, syndecan-mediated signalling in plasma cells, and epithelial-to-mesenchymal transition in hepatic stellate cells. The strategy of pathway-level analysis of genomic and metabolomic data uncovered l-lactic acid, Krebs cycle intermediate compounds, arachidonic acid and cortisol among the most prominent shared metabolites.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

SummarySince 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales—from genes to genomic regions and pathways—have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans.

Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.

Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study.

Bipolar disorder is a complex psychiatric trait that is also recognized as a high substantial heritability from a worldwide distribution. The success in identifying susceptibility loci for bipolar disorder (BPD) has been limited due to its complex genetic architecture. Growing evidence from association studies including genome-wide association (GWA) studies points to the need of improved analytic strategies to pinpoint the missing heritability for BPD. More importantly, many studies indicate that BPD has a strong association with dementia. We conducted advanced pathway analytics strategies to investigate synergistic effects of multilocus within biologically functional pathways, and further demonstrated functional effects among proteins in subnetworks to examine mechanisms underlying the complex nature of bipolarity using a GWA dataset for BPD. We allowed bipolar susceptible loci to play a role that takes larger weights in pathway-based analytic approaches. Having significantly informative genes identified from enriched pathways, we further built function-specific subnetworks of protein interactions using MetaCore. The gene-wise scores (i.e., minimum p-value) were corrected for the gene-length, and the results were corrected for multiple tests using Benjamini and Hochberg’s method. We found 87 enriched pathways that are significant for BPD; of which 36 pathways were reported. Most of them are involved with several metabolic processes, neural systems, immune system, molecular transport, cellular communication, and signal transduction. Three significant and function-related subnetworks with multiple hotspots were reported to link with several Gene Ontology processes for BPD. Our comprehensive pathway-network frameworks demonstrated that the use of prior knowledge is promising to facilitate our understanding between complex psychiatric disorders (e.g., BPD) and dementia for the access to the connection and clinical implications, along with the development and progression of dementia.

Grey and white matter microstructure is associated with polygenic risk for schizophrenia

Recent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition. We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macrostructural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed-effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures, and 14 white matter tracts. Other microstructural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate, and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia. Risk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical, and white matter microstructure may be linked to the genetic mechanisms of schizophrenia.

Abstract 460: Cyclin Dependent Kinase Inhibitor A1 Identified as a Potential Risk Locus for Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is characterized by heterogeneous phenotypic expression, natural history, and genetic profile with numerous causative mutations. The relationship between genotype and phenotype in HCM is incompletely understood. Identifying genetic modifiers may expand understanding of the signaling pathways that are responsible for phenotypic expression. UK Biobank comprises clinical and genetic data for greater than 500,000 individuals. We compared control individuals to those with a diagnosis of HCM (identified via ICD-10 code I42.1) in a 20-to-1 fashion to identify genetic differences. Data were compared using Mixed Model Analysis for Pedigrees/Populations (MMAP) mixed-model regression. Variants were assessed using bioinformatics tools including linkage disequilibrium and Eigen scoring. Results were then compared to previously published genome-wide association datasets. Mixed-model regression identified 5 variants of cyclin-dependent kinase inhibitor 1A ( CDKN1A ) as statistically significant with one intronic variant (rs3176326) likely affecting a gene promoter region with an Eigen-PC score of 7.779. This variant was found to have a p-value of 5.08x10 -8 when contrasted to controls with a minor allele frequency of 0.199 in the affected individuals. The 4 other variants were in linkage disequilibrium (r 2 0.90-0.99) with rs3176326. The odds ratio of HCM with rs3176326 is 1.81.To our knowledge, CDKN1A has not previously been associated with HCM. By utilizing bioinformatic tools we were able to identify 5 variants that may be associated with the risk for HCM. One locus affects a promotor region and resulted in a notably high Eigan-PC score suggestive that it has an impact on HCM. When comparing these findings with previous genome-wide association studies utilizing the UK Biobank, we find that CDKN1A has a possible association with HCM, though those results were not statistically significant. CDKN1A has been previously associated with the p53 DNA repair and cell senescence pathway. Animal and human studies have implicated it in the development of fibrosis. A recent genome-wide association metanalysis identified it as a risk locus for heart failure. These findings support our conclusion that CDKN1A is a risk locus for HCM.

Birth Weight and Stroke in Adult Life: Genetic Correlation and Causal Inference With Genome-Wide Association Data Sets.

ObjectivePrior studies have shown that there is an inverse association between birth weight and stroke in adulthood; however, whether such association is causal remains yet known and those studies cannot distinguish between the direct fetal effect and the indirect maternal effect. The aim of the study is to untangle such relationship using novel statistical genetic approaches.MethodsWe first utilized linkage disequilibrium score regression (LDSC) and Genetic analysis incorporating Pleiotropy and Annotation (GPA) to estimate the overall genetic correlation between birth weight and stroke. Then, with a set of valid birth-weight instruments which had adjusted fetal and maternal effects, we performed a two-sample Mendelian randomization (MR) to evaluate its causal effect on stroke based summary statistics from large scale genome-wide association study (GWAS) (n = 264,498 for birth weight and 446,696 for stroke). We further validated the MR results with extensive sensitivity analyses.ResultsBoth LDSC and GPA demonstrated significant evidence of shared maternal genetic foundation between birth weight and stroke, with the genetic correlation estimated to −0.176. However, no fetal genetic correlation between birth weight and stroke was detected. Furthermore, the inverse variance weighted MR demonstrated the maternally causal effect of birth weight on stroke was 1.12 (95% confidence interval [CI] 1.00–1.27). The maternal ORs of birth weight on three subtypes of stroke including cardioembolic stroke (CES), large artery stroke (LAS) and small vessel stroke (SVS) were 1.16 (95% CI 0.93–1.43), 1.50 (95% CI 1.14–1.96) and 1.47 (95% CI 1.15–1.87), respectively. In contrast, no fetal causal associations were found between birth weight and stroke or the subtypes. Those results were robust against extensive sensitivity analyses, with Egger regression ruling out the possibility of pleiotropy and multivariable MR excluding the likelihood of confounding or mediation effects of other risk factors of stroke.ConclusionThis study provides empirically supportive evidence on the fetal developmental origins of stroke and its subtypes. However, further investigation is warranted to understand the pathophysiological role of low birth weight in developing stroke.

Tropomyosin 1 genetically constrains in vitro hematopoiesis

BackgroundIdentifying causal variants and genes from human genetic studies of hematopoietic traits is important to enumerate basic regulatory mechanisms underlying these traits, and could ultimately augment translational efforts to generate platelets and/or red blood cells in vitro. To identify putative causal genes from these data, we performed computational modeling using available genome-wide association datasets for platelet and red blood cell traits.ResultsOur model identified a joint collection of genomic features enriched at established trait associations and plausible candidate variants. Additional studies associating variation at these loci with change in gene expression highlighted Tropomyosin 1 (TPM1) among our top-ranked candidate genes. CRISPR/Cas9-mediated TPM1 knockout in human induced pluripotent stem cells (iPSCs) enhanced hematopoietic progenitor development, increasing total megakaryocyte and erythroid cell yields.ConclusionsOur findings may help explain human genetic associations and identify a novel genetic strategy to enhance in vitro hematopoiesis. A similar trait-specific gene prioritization strategy could be employed to help streamline functional validation experiments for virtually any human trait.

Differences in telomere length between patients with bipolar disorder and controls are influenced by lithium treatment.

Aim: To assess the role of lithium treatment in the relationship between bipolar disorder (BD) and leukocyte telomere length (LTL). Materials& methods: We compared LTL between 131 patients with BD, with or without a history of lithium treatment, and 336 controls. We tested the association between genetically determined LTL and BD in two large genome-wide association datasets. Results: Patients with BD with a history lithium treatment showed longer LTL compared with never-treated patients (p=0.015), and similar LTL compared with controls. Patients never treated with lithium showed shorter LTL compared with controls (p=0.029). Mendelian randomization analysis showed no association between BD and genetically determined LTL. Conclusion: Our data support previous findings showing that long-term lithium treatment might protect against telomere shortening.

Tropomyosin 1 Genetically Constrains in Vitro Megakaryopoiesis

Platelet size and count are heritable, quantifiable outcomes of hematopoiesis. A better understanding of genetic mechanisms that regulate hematopoiesis could yield insights into blood disorders and augment clinical translational efforts to generate platelets in vitro. For example, induced pluripotent stem cell-derived blood cells could someday ameliorate adverse effects and increase transfusable platelet supplies. However, these methods remain inefficient. Genome wide association studies (GWAS) have linked hundreds of DNA loci with altered human platelet traits, but little is known about how these GWAS loci mechanistically impact hematopoiesis, megakaryocyte or platelet biology. Using publicly available genome-wide association and epigenetic data sets, we applied a machine-learning framework to identify epigenetic features enriched at established platelet trait association sites. From these results, we derived a quantitative prediction model that identified hematopoiesis-, megakaryocyte-, and platelet-relevant genomic loci and related genes more accurately than any prior computational method. In addition to specifying exact genetic variants known to regulate platelet traits and function, our model highlighted several novel variants in established platelet trait variation GWAS loci that were predicted to disrupt key transcription factor binding sites. Among loci nominated by our statistical model was a variant (rs11071720) that alters Tropomyosin 1 (TPM1) gene expression. TPM1 regulates cytoskeletal biology in many cell types, and cytoskeletal functions critically impact hematopoiesis and terminal blood cell biology. We used CRISPR/Cas9-mediated genome editing to create TPM1-knockout human induced pluripotent stem cells. TPM1KO cells were healthy and showed normal developmental progression through primitive streak, mesoderm, and early primitive hematopoietic differentiation programs. Unexpectedly, TPM1KO cells demonstrated enhanced formation of hemogenic endothelium and hematopoietic progenitor cells, increasing total megakaryocyte yield by more than 2-fold. TPM1KO megakaryocytes demonstrated normal morphology, gene expression patterns and functional responses to platelet agonists. Our findings help explain human genetics associations and identify a novel strategy to enhance in vitro hematopoiesis. More broadly, our now-validated machine learning-based approach might specify additional functional loci underlying hematopoiesis, megakaryopoiesis, or thrombopoiesis. Disclosures No relevant conflicts of interest to declare.

Gene-based analysis in HRC imputed genome wide association data identifies three novel genes for Alzheimer's disease.

Late onset Alzheimer’s disease is the most common form of dementia for which about 30 susceptibility loci have been reported. The aim of the current study is to identify novel genes associated with Alzheimer’s disease using the largest up-to-date reference single nucleotide polymorphism (SNP) panel, the most accurate imputation software and a novel gene-based analysis approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer’s Project Consortium, comprising over 7 million genotypes from 17,008 Alzheimer’s cases and 37,154 controls. In addition to earlier reported genes, we detected three novel gene-wide significant loci PPARGC1A (p = 2.2 × 10−6), RORA (p = 7.4 × 10−7) and ZNF423 (p = 2.1 × 10−6). PPARGC1A and RORA are involved in circadian rhythm; circadian disturbances are one of the earliest symptoms of Alzheimer’s disease. PPARGC1A is additionally linked to energy metabolism and the generation of amyloid beta plaques. RORA is involved in a variety of functions apart from circadian rhythm, such as cholesterol metabolism and inflammation. The ZNF423 gene resides in an Alzheimer’s disease-specific protein network and is likely involved with centrosomes and DNA damage repair.

Robust Reference Powered Association Test of Genome-Wide Association Studies.

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test1) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.