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Background: Alirocumab, an antibody that blocks PCSK9 (proprotein convertase subtilisin/kexin type 9), was associated with reduced major adverse cardiovascular events (MACE) and death in the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab). In this study, higher baseline levels of low-density lipoprotein cholesterol (LDL-C) predicted greater benefit from alirocumab treatment. Recent studies indicate high polygenic risk scores (PRS) for coronary artery disease (CAD) identify individuals at higher risk who derive increased benefit from statins. We performed post hoc analyses to determine whether high PRS for CAD identifies higher-risk individuals, independent of baseline LDL-C and other known risk factors, who might derive greater benefit from alirocumab treatment. Methods: ODYSSEY OUTCOMES was a randomized, double-blind, placebo-controlled trial comparing alirocumab or placebo in 18 924 patients with acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin treatment. The primary endpoint (MACE) comprised death of CAD, nonfatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. A genome-wide PRS for CAD comprising 6 579 025 genetic variants was evaluated in 11 953 patients with available DNA samples. Analysis of MACE risk was performed in placebo-treated patients, whereas treatment benefit analysis was performed in all patients. Results: The incidence of MACE in the placebo group was related to PRS for CAD: 17.0% for high PRS patients (>90th percentile) and 11.4% for lower PRS patients (≤90th percentile; P <0.001); this PRS relationship was not explained by baseline LDL-C or other established risk factors. Both the absolute and relative reduction of MACE by alirocumab compared with placebo was greater in high versus low PRS patients. There was an absolute reduction by alirocumab in high versus low PRS groups of 6.0% and 1.5%, respectively, and a relative risk reduction by alirocumab of 37% in the high PRS group (hazard ratio, 0.63 [95% CI, 0.46–0.86]; P =0.004) versus a 13% reduction in the low PRS group (hazard ratio, 0.87 [95% CI, 0.78–0.98]; P =0.022; interaction P =0.04). Conclusions: A high PRS for CAD is associated with elevated risk for recurrent MACE after acute coronary syndrome and a larger absolute and relative risk reduction with alirocumab treatment, providing an independent tool for risk stratification and precision medicine.

Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes 1 – 5 . Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75 , which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes 6 , our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2–2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10−8) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10−13), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.

LMX1B encodes a homeodomain-containing transcription factor that is essential during development. Mutations in LMX1B cause nail-patella syndrome, characterized by dysplasia of the patellae, nails, and elbows and FSGS with specific ultrastructural lesions of the glomerular basement membrane (GBM). By linkage analysis and exome sequencing, we unexpectedly identified an LMX1B mutation segregating with disease in a pedigree of five patients with autosomal dominant FSGS but without either extrarenal features or ultrastructural abnormalities of the GBM suggestive of nail-patella-like renal disease. Subsequently, we screened 73 additional unrelated families with FSGS and found mutations involving the same amino acid (R246) in 2 families. An LMX1B in silico homology model suggested that the mutated residue plays an important role in strengthening the interaction between the LMX1B homeodomain and DNA; both identified mutations would be expected to diminish such interactions. In summary, these results suggest that isolated FSGS could result from mutations in genes that are also involved in syndromic forms of FSGS. This highlights the need to include these genes in all diagnostic approaches to FSGS that involve next-generation sequencing.

Rationale: To date, most studies aimed at discovering genetic factors influencing treatment response in asthma have focused on biologic candidate genes. Genome-wide association studies (GWAS) can rapidly identify novel pharmacogenetic loci. Objectives: To investigate if GWAS can identify novel pharmacogenetic loci in asthma. Methods: Using phenotypic and GWAS genotype data available through the NHLBI-funded Single-nucleotide polymorphism Health association-Asthma Resource Project, we analyzed differences in FEV 1 in response to inhaled corticosteroids in 418 white subjects with asthma. Of the 444,088 single nucleotide polymorphisms (SNPs) analyzed, the lowest 50 SNPs by P value were genotyped in an independent clinical trial population of 407 subjects with asthma. Measurements and Main Results: The lowest P value for the GWAS analysis was 2.09 3 10 26. Of the 47 SNPs successfully genotyped in the replication population, three were associated under the same genetic model in the same direction, including two of the top four SNPs ranked by P value. Combined P values for these SNPs were 1.06 3 10 25 for rs3127412 and 6.13 3 10 26 for rs6456042. Although these two were not located within a gene, they were tightly correlated with three variants mapping to potentially functional regions within the T gene. After genotyping, each T gene variant was also associated with lung function response to inhaled corticosteroids in each of the trials associated with rs3127412 and rs6456042 in the initial GWAS analysis. On average, there was a twofold to threefold difference in FEV 1 response for those subjects homozygous for the wild-type versus mutant alleles for each T gene SNP. Conclusions: Genome-wide association has identified the T gene as a novel pharmacogenetic locus for inhaled corticosteroid response in asthma.Keywords: polymorphism; genome; pharmacogenomics; glucocorticoid Approximately 300 million individuals worldwide carry a diagnosis of asthma (1). Asthma is a genetic disease, known for more than three centuries to cluster in families. Based on twin studies, the broad sense heritability estimates (proportion of the total variance of a trait due to genetic causes) of an asthma diagnosis range from approximately 36-75%. For asthma control, the most widely prescribed medications are inhaled corticosteroids (ICS). Endogenous corticosteroid level and exogenous therapeutic Supported by NIH U01 HL65899 and R01 HL092197. SHARP was funded by NIH U10 HL74231, U01 HL65899, U54LM8748, U01 HL75232, U01 HL75408, U01 HL75409, U01 HL75415, U01 HL75416, U01 HL75417, U01 HL75419, U01 HL75420, U10 HL64287, U10 HL64288, U10 HL64295, U10 HL64305, U10 HL64307, U01 HL64313, U10 HL51831, U10 HL51834, U10 HL51843, U10 HL51810, U10 HL51823, U10 HL51845, and U10 HL56443. The full SHARP acknowledgment can be found in the online supplement. The NHLBI SHARe (SNP Health Association Resource) genotyping services were provided by Affymetrix, Inc. under US Federal Government contract number N02-HL-6-4278 from the NHLBI. The NIH GWAS Repository of ...

BackgroundPersonalized health-care promises tailored health-care solutions to individual patients based on their genetic background and/or environmental exposure history. To date, disease prediction has been based on a few environmental factors and/or single nucleotide polymorphisms (SNPs), while complex diseases are usually affected by many genetic and environmental factors with each factor contributing a small portion to the outcome. We hypothesized that the use of random forests classifiers to select SNPs would result in an improved predictive model of asthma exacerbations. We tested this hypothesis in a population of childhood asthmatics.MethodsIn this study, using emergency room visits or hospitalizations as the definition of a severe asthma exacerbation, we first identified a list of top Genome Wide Association Study (GWAS) SNPs ranked by Random Forests (RF) importance score for the CAMP (Childhood Asthma Management Program) population of 127 exacerbation cases and 290 non-exacerbation controls. We predict severe asthma exacerbations using the top 10 to 320 SNPs together with age, sex, pre-bronchodilator FEV1 percentage predicted, and treatment group.ResultsTesting in an independent set of the CAMP population shows that severe asthma exacerbations can be predicted with an Area Under the Curve (AUC) = 0.66 with 160-320 SNPs in comparison to an AUC score of 0.57 with 10 SNPs. Using the clinical traits alone yielded AUC score of 0.54, suggesting the phenotype is affected by genetic as well as environmental factors.ConclusionsOur study shows that a random forests algorithm can effectively extract and use the information contained in a small number of samples. Random forests, and other machine learning tools, can be used with GWAS studies to integrate large numbers of predictors simultaneously.

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