Haploinsufficiency underlies the neurodevelopmental consequences of SLC6A1 variants.

Heterozygous variants in SLC6A1, encoding the GAT-1 GABA transporter, are associated with seizures, developmental delay, and autism. The majority of affected individuals carry missense variants, many of which are recurrent germline de novo mutations, raising the possibility of gain-of-function or dominant-negative effects. To understand the functional consequences, we performed an in vitro GABA uptake assay for 213 unique variants, including 24 control variants. De novo variants consistently resulted in a decrease in GABA uptake, in keeping with haploinsufficiency underlying all neurodevelopmental phenotypes. Where present, ClinVar pathogenicity reports correlated well with GABA uptake data; the functional data can inform future reports for the remaining 72% of unscored variants. Surface localization was assessed for 86 variants; two-thirds of loss-of-function missense variants prevented GAT-1 from being present on the membrane while GAT-1 was on the surface but with reduced activity for the remaining third. Surprisingly, recurrent de novo missense variants showed moderate loss-of-function effects that reduced GABA uptake with no evidence for dominant-negative or gain-of-function effects. Using linear regression across multiple missense severity scores to extrapolate the functional data to all potential SLC6A1 missense variants, we observe an abundance of GAT-1 residues that are sensitive to substitution. The extent of this missense vulnerability accounts for the clinically observed missense enrichment; overlap with hypermutable CpG sites accounts for the recurrent missense variants. Strategies to increase the expression of the wild-type SLC6A1 allele are likely to be beneficial across neurodevelopmental disorders, though the developmental stage and extent of required rescue remain unknown.

Genetic Inhibition of APOL1 Pore-Forming Function Prevents APOL1-Mediated Kidney Disease.

SIGNIFICANCE STATEMENT: African Americans are at increased risk of CKD in part due to high-risk (HR) variants in the apolipoprotein L1 ( APOL1 ) gene, termed G1/G2. A different APOL1 variant, p.N264K , reduced the risk of CKD and ESKD among carriers of APOL1 HR variants to levels comparable with individuals with APOL1 low-risk variants in an analysis of 121,492 participants of African ancestry from the Million Veteran Program (MVP). Functional genetic studies in cell models showed that APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR mutations. Pharmacologic inhibitors that mimic this mutation blocking APOL1 -mediated pore formation may be able to prevent and/or treat APOL1 -associated kidney disease. BACKGROUND: African Americans are at increased risk for nondiabetic CKD in part due to HR variants in the APOL1 gene. METHODS: We tested whether a different APOL1 variant, p.N264K , modified the association between APOL1 HR genotypes (two copies of G1/G2) and CKD in a cross-sectional analysis of 121,492 participants of African ancestry from the MVP. We replicated our findings in the Vanderbilt University Biobank ( n =14,386) and National Institutes of Health All of Us ( n =14,704). Primary outcome was CKD and secondary outcome was ESKD among nondiabetic patients. Primary analysis compared APOL1 HR genotypes with and without p.N264K . Secondary analyses included APOL1 low-risk genotypes and tested for interaction. In MVP, we performed sequential logistic regression models adjusting for demographics, comorbidities, medications, and ten principal components of ancestry. Functional genomic studies expressed APOL1 HR variants with and without APOL1 p.N264K in cell models. RESULTS: In the MVP cohort, 15,604 (12.8%) had two APOL1 HR variants, of which 582 (0.5%) also had APOL1 p.N264K . In MVP, 18,831 (15%) had CKD, 4177 (3%) had ESKD, and 34% had diabetes. MVP APOL1 HR, without p.N264K , was associated with increased odds of CKD (odds ratio [OR], 1.72; 95% confidence interval [CI], 1.60 to 1.85) and ESKD (OR, 3.94; 95% CI, 3.52 to 4.41). In MVP, APOL1 p.N264K mitigated the renal risk of APOL1 HR, in CKD (OR, 0.43; 95% CI, 0.28 to 0.65) and ESKD (OR, 0.19; CI 0.07 to 0.51). In the replication cohorts meta-analysis, APOL1 p.N264K mitigated the renal risk of APOL1 HR in CKD (OR, 0.40; 95% CI, 0.18 to 0.92) and ESKD (OR, 0.19; 95% CI, 0.05 to 0.79). In the mechanistic studies, APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR variants. CONCLUSIONS: APOL1 p.N264K is associated with reduced risk of CKD and ESKD among carriers of APOL1 HR to levels comparable with individuals with APOL1 low-risk genotypes.

A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes.

BACKGROUND: Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking. METHODS: We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease. RESULTS: We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed. CONCLUSIONS: We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .

Multivariate genome-wide association study models to improve prediction of Crohn's disease risk and identification of potential novel variants.

BACKGROUND: Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that affects the gastrointestinal tract with diverse symptoms. At present, genome-wide association studies (GWAS) has discovered more than 140 genetic loci associated with CD from several datasets. Using the usual univariate GWAS methods, researchers have discovered common variants with small effects. Univariate methods assume independence among the variants that miss subtle combinatorial signals. Multivariate approaches have improved risk prediction and have complemented univariate methods for elucidating the etiology of complex traits and potential novel associations. However, the current multivariate models for CD have been assessed for three datasets (published from 2006 to 2008) under unrelated methodological settings showing a broad performance spectrum. Notably, these multivariate studies do not analyze potential novel variants. Here, we aimed to perform a robust multivariate analysis of a CD dataset different from the one commonly used, and we used the information yielded by the models to identify whether the generated models could provide additional information about the potential novel variants of CD. METHODS: Therefore, we compared different multivariate methods and models, LASSO (least absolute shrinkage and selection operator), XGBoost, random forest (RF), Bootstrap stage-wise model selection (BSWiMS), and LDpred, using a strict random subsampling approach to predict the CD risk using a recent GWAS dataset, United Kingdom IBD IBD Genetics Consortium (UKIBDGC), made available in 2017, that had not been used for CD prediction studies. In addition, we assessed the effect of common strategies by increasing and decreasing the number of single-nucleotide polymorphism (SNP) markers (using genotype imputation and linkage disequilibrium (LD)-clumping). RESULTS: We found that the LDpred model without any imputation was the best model among all the tested models for predicting the CD risk (area under the receiver operating characteristic curve (AUROC) = 0.667 ± 0.024) in this dataset. We validated the best models using a second dataset (National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) IBD Genetics Consortium, which was previously used in CD prediction studies) in which LDpred was also the best method with a similar performance (AUROC = 0.634 ± 0.009). Based on the importance of the variants yielded by the multivariate models, we identified an unnoticed region within chromosome 6, tagged by SNP rs4945943; this region was close to the gene MARCKS, which appeared to contribute to CD risk. CONCLUSIONS: This research is the first multivariate prediction analysis applied to the UKIBDGC dataset. Our robust multivariate setting analysis enabled us to identify a potential variant that contributed to the CD risk. Multivariate methods are valuable tools for identifying genes that contribute to disease risk.

Identifying therapeutic drug targets using bidirectional effect genes.

Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10-8). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes.

Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer's disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Detecting Clustered Independent Rare Variant Associations Using Genetic Algorithms.

The availability of an increasing collection of sequencing data provides the opportunity to study genetic variation with an unprecedented level of detail. There is much interest in uncovering the role of rare variants and their contribution to disease. However, detecting associations of rare variants with small minor allele frequencies (MAF) and modest effects remains a challenge for rare variant association methods. Due to this low signal-to-noise ratio, most methods are underpowered to detect associations even when conducting rare variant association tests at the gene level. We present a new method for detecting rare variant associations. The algorithm consists of two steps. In the first step, a genetic algorithm searches for a promising genomic region containing a collection of genes with causal rare variants. In the second step, a genetic algorithm aims at removing false positives from the located genomic region. We tested the proposed method with a collection of datasets obtained from real exome data. The proposed method possesses sufficient power for detecting associations of rare variants with complex phenotypes. This method can be used for studying the contribution of rare variants with complex disease, particularly in cases where single-variant or gene-based tests are underpowered.

Differences in the Presentation and Progression of Parkinson's Disease by Sex.

BACKGROUND: Previous studies reported various symptoms of Parkinson's disease (PD) associated with sex. Some were conflicting or confirmed in only one study. OBJECTIVES: We examined sex associations to PD phenotypes cross-sectionally and longitudinally in large-scale data. METHODS: We tested 40 clinical phenotypes, using longitudinal, clinic-based patient cohorts, consisting of 5946 patients, with a median follow-up of 3.1 years. For continuous outcomes, we used linear regressions at baseline to test sex-associated differences in presentation, and linear mixed-effects models to test sex-associated differences in progression. For binomial outcomes, we used logistic regression models at baseline and Cox regression models for survival analyses. We adjusted for age, disease duration, and medication use. In the secondary analyses, data from 17 719 PD patients and 7588 non-PD participants from an online-only, self-assessment PD cohort were cross-sectionally evaluated to determine whether the sex-associated differences identified in the primary analyses were consistent and unique to PD. RESULTS: Female PD patients had a higher risk of developing dyskinesia early during the follow-up period, with a slower progression in activities of daily living difficulties, and a lower risk of developing cognitive impairments compared with male patients. The findings in the longitudinal, clinic-based cohorts were mostly consistent with the results of the online-only cohort. CONCLUSIONS: We observed sex-associated contributions to PD heterogeneity. These results highlight the necessity of future research to determine the underlying mechanisms and importance of personalized clinical management. © 2020 International Parkinson and Movement Disorder Society.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Harnessing peripheral DNA methylation differences in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease.

BACKGROUND: Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer's disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants. RESULTS: In this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10-5 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1) validated using the genotype tissue expression project portal (GTex). CONCLUSIONS: Our work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD.

Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data.

BACKGROUND: Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. RESULTS: We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. CONCLUSIONS: Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

BACKGROUND: Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. OBJECTIVES: To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. METHODS: We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes. RESULTS: Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. CONCLUSIONS: We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Genetic risk of Parkinson disease and progression:: An analysis of 13 longitudinal cohorts.

OBJECTIVE: To determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression. METHODS: We evaluated the association between 31 risk variants and variables measuring disease progression. A total of 23,423 visits by 4,307 patients of European ancestry from 13 longitudinal cohorts in Europe, North America, and Australia were analyzed. RESULTS: We confirmed the importance of GBA on phenotypes. GBA variants were associated with the development of daytime sleepiness (p.N370S: hazard ratio [HR] 3.28 [1.69-6.34]) and possible REM sleep behavior (p.T408M: odds ratio 6.48 [2.04-20.60]). We also replicated previously reported associations of GBA variants with motor/cognitive declines. The other genotype-phenotype associations include an intergenic variant near LRRK2 and the faster development of motor symptom (Hoehn and Yahr scale 3.0 HR 1.33 [1.16-1.52] for the C allele of rs76904798) and an intronic variant in PMVK and the development of wearing-off effects (HR 1.66 [1.19-2.31] for the C allele of rs114138760). Age at onset was associated with TMEM175 variant p.M393T (-0.72 [-1.21 to -0.23] in years), the C allele of rs199347 (intronic region of GPNMB, 0.70 [0.27-1.14]), and G allele of rs1106180 (intronic region of CCDC62, 0.62 [0.21-1.03]). CONCLUSIONS: This study provides evidence that alleles associated with Parkinson disease risk, in particular GBA variants, also contribute to the heterogeneity of multiple motor and nonmotor aspects. Accounting for genetic variability will be a useful factor in understanding disease course and in minimizing heterogeneity in clinical trials.

BIN1 favors the spreading of Tau via extracellular vesicles.

Despite Bridging INtegrator 1 (BIN1) being the second most statistically-significant locus associated to Late Onset Alzheimer's Disease, its role in disease pathogenesis remains to be clarified. As reports suggest a link between BIN1, Tau and extracellular vesicles, we investigated whether BIN1 could affect Tau spreading via exosomes secretion. We observed that BIN1-associated Tau-containing extracellular vesicles purified from cerebrospinal fluid of AD-affected individuals are seeding-competent. We showed that BIN1 over-expression promotes the release of Tau via extracellular vesicles in vitro as well as exacerbation of Tau pathology in vivo in PS19 mice. Genetic deletion of Bin1 from microglia resulted in reduction of Tau secretion via extracellular vesicles in vitro, and in decrease of Tau spreading in vivo in male, but not female, mice, in the context of PS19 background. Interestingly, ablation of Bin1 in microglia of male mice resulted in significant reduction in the expression of heat-shock proteins, previously implicated in Tau proteostasis. These observations suggest that BIN1 could contribute to the progression of AD-related Tau pathology by altering Tau clearance and promoting release of Tau-enriched extracellular vesicles by microglia.

Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry.

Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility. © 2019 American Society for Bone and Mineral Research.

Disentangling the genetics of lean mass.

Background: Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass. Objectives: To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci. Methods: We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms). Results: Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as "sumo wrestler" loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed "body builder" loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in "body builder" loci were associated with metabolic protection. Conclusions: In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.

Phenome-wide association studies across large population cohorts support drug target validation.

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.

Translating Human Genetics into Novel Drug Targets.

The most important promise of the human genome sequencing project is the identification of the genetic cause of devastating human diseases and the subsequent deliver of novel drug therapies to treat these diseases with high unmet medical need. In the last 10 years we have successfully identified hundreds of genetic loci associated with many traits and diseases. The translation of these findings into novel therapies is not straightforward and poses challenges that are usually overlooked in traditional gene mapping. This chapter describes some of the most common challenges and opportunities to use human genetics to identify and validate novel drug targets.