Publications by authors named "Nathalie Chami"

11 Publications

  • Page 1 of 1

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes.

Nat Commun 2021 06 9;12(1):3505. Epub 2021 Jun 9.

Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.
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http://dx.doi.org/10.1038/s41467-021-23556-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190084PMC
June 2021

Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.

Am J Hum Genet 2021 05 21;108(5):874-893. Epub 2021 Apr 21.

Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.
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http://dx.doi.org/10.1016/j.ajhg.2021.04.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206199PMC
May 2021

Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities.

Nat Metab 2021 02 22;3(2):228-243. Epub 2021 Feb 22.

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA.

Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin-glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.
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http://dx.doi.org/10.1038/s42255-021-00346-2DOI Listing
February 2021

The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population.

PLoS Med 2020 07 21;17(7):e1003196. Epub 2020 Jul 21.

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

Background: Melanocortin 4 receptor (MC4R) deficiency, caused by mutations in MC4R, is the most common cause of monogenic forms of obesity. However, these mutations have often been identified in small-scale, case-focused studies. Here, we assess the penetrance of previously reported MC4R mutations at a population level. Furthermore, we examine why some carriers of pathogenic mutations remain of normal weight, to gain insight into the mechanisms that control body weight.

Methods And Findings: We identified 59 known obesity-increasing mutations in MC4R from the Human Gene Mutation Database (HGMD) and Clinvar. We assessed their penetrance and effect on obesity (body mass index [BMI] ≥ 30 kg/m2) in >450,000 individuals (age 40-69 years) of the UK Biobank, a population-based cohort study. Of these 59 mutations, only 11 had moderate-to-high penetrance and increased the odds of obesity by more than 2-fold. We subsequently focused on these 11 mutations and examined differences between carriers of normal weight and carriers with obesity. Twenty-eight of the 182 carriers of these 11 mutations were of normal weight. Body composition of carriers of normal weight was similar to noncarriers of normal weight, whereas among individuals with obesity, carriers had a somewhat higher BMI than noncarriers (1.44 ± 0.07 standard deviation scores [SDSs] ± standard error [SE] versus 1.29 ± 0.001, P = 0.03), because of greater lean mass (1.44 ± 0.09 versus 1.15 ± 0.002, P = 0.002). Carriers of normal weight more often reported that, already at age 10 years, their body size was below average or average (72%) compared with carriers with obesity (48%) (P = 0.01). To assess the polygenic contribution to body weight in carriers of normal weight and carriers with obesity, we calculated a genome-wide polygenic risk score for BMI (PRSBMI). The PRSBMI of carriers of normal weight (PRSBMI = -0.64 ± 0.18) was significantly lower than of carriers with obesity (0.40 ± 0.11; P = 1.7 × 10-6), and tended to be lower than that of noncarriers of normal weight (-0.29 ± 0.003; P = 0.05). Among carriers, those with a low PRSBMI (bottom quartile) have an approximately 5-kg/m2 lower BMI (approximately 14 kg of body weight for a 1.7-m-tall person) than those with a high PRS (top quartile). Because the UK Biobank population is healthier than the general population in the United Kingdom, penetrance may have been somewhat underestimated.

Conclusions: We showed that large-scale data are needed to validate the impact of mutations observed in small-scale and case-focused studies. Furthermore, we observed that despite the key role of MC4R in obesity, the effects of pathogenic MC4R mutations may be countered, at least in part, by a low polygenic risk potentially representing other innate mechanisms implicated in body weight regulation.
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http://dx.doi.org/10.1371/journal.pmed.1003196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373259PMC
July 2020

Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.

Am J Hum Genet 2016 Jul 23;99(1):22-39. Epub 2016 Jun 23.

Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA.

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005433PMC
July 2016

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.

Am J Hum Genet 2016 Jul 23;99(1):40-55. Epub 2016 Jun 23.

Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland; University of Tampere, School of Medicine, Tampere 33514, Finland.

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005441PMC
July 2016

Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits.

Am J Hum Genet 2016 Jul 23;99(1):8-21. Epub 2016 Jun 23.

Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.

Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified 16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] = 3.3%, p = 2 × 10(-10) for hemoglobin [HGB]) and HNF4A (rs1800961, MAF = 2.4%, p < 3 × 10(-8) for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF = 8.7%, p = 7 × 10(-11)) and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts. We also identified a rare missense variant in ALAS2 (rs201062903, MAF = 0.2%) associated with lower mean corpuscular volume and mean corpuscular hemoglobin (p < 8 × 10(-9)). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic hemolytic anemia, associated with HGB and HCT (SKAT p < 8 × 10(-7)). These rare, low-frequency, and common RBC variants showed pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.
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http://dx.doi.org/10.1016/j.ajhg.2016.05.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005438PMC
July 2016

Nonsense mutations in BAG3 are associated with early-onset dilated cardiomyopathy in French Canadians.

Can J Cardiol 2014 Dec 2;30(12):1655-61. Epub 2014 Oct 2.

Centre de Recherche, Montreal Heart Institute, Montréal, Québec, Canada; Départment de Médecine, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada. Electronic address:

Background: Dilated cardiomyopathy (DCM) is a major cause of heart failure that may require heart transplantation. Approximately one third of DCM cases are familial. Next-generation DNA sequencing of large panels of candidate genes (ie, targeted sequencing) or of the whole exome can rapidly and economically identify pathogenic mutations in familial DCM.

Methods: We recruited 64 individuals from 26 DCM families followed at the Montreal Heart Institute Cardiovascular Genetic Center and sequenced the whole exome of 44 patients and 2 controls. Both affected and unaffected family members underwent genotyping for segregation analysis.

Results: We found 2 truncating mutations in BAG3 in 4 DCM families (15%) and confirmed segregation with disease status by linkage (log of the odds [LOD] score = 3.8). BAG3 nonsense mutations conferred a worse prognosis as evidenced by a younger age of clinical onset (37 vs 48 years for carriers and noncarriers respectively; P = 0.037). We also found truncating mutations in TTN in 5 families (19%). Finally, we identified potential pathogenic mutations for 9 DCM families in 6 candidate genes (DSP, LMNA, MYH7, MYPN, RBM20, and TNNT2). We still need to confirm several of these mutations by segregation analysis.

Conclusions: Screening an extended panel of 41 candidate genes allowed us to identify probable pathogenic mutations in 69% of families with DCM in our cohort of mostly French-Canadian patients. We confirmed the prevalence of TTN nonsense mutations in DCM. Furthermore, to our knowledge, we are the first to present an association between nonsense mutations in BAG3 and early-onset DCM.
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http://dx.doi.org/10.1016/j.cjca.2014.09.030DOI Listing
December 2014

Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits.

Nat Genet 2014 Jun 28;46(6):629-34. Epub 2014 Apr 28.

1] Montreal Heart Institute, Montreal, Quebec, Canada. [2] Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada. [3] Faculty of Pharmacy, Université de Montréal, Montreal, Québec, Canada. [4].

Hematological traits are important clinical parameters. To test the effects of rare and low-frequency coding variants on hematological traits, we analyzed hemoglobin concentration, hematocrit levels, white blood cell (WBC) counts and platelet counts in 31,340 individuals genotyped on an exome array. We identified several missense variants in CXCR2 associated with reduced WBC count (gene-based P = 2.6 × 10(-13)). In a separate family-based resequencing study, we identified a CXCR2 frameshift mutation in a pedigree with congenital neutropenia that abolished ligand-induced CXCR2 signal transduction and chemotaxis. We also identified missense or splice-site variants in key hematopoiesis regulators (EPO, TFR2, HBB, TUBB1 and SH2B3) associated with blood cell traits. Finally, we were able to detect associations between a rare somatic JAK2 mutation (encoding p.Val617Phe) and platelet count (P = 3.9 × 10(-22)) as well as hemoglobin concentration (P = 0.002), hematocrit levels (P = 9.5 × 10(-7)) and WBC count (P = 3.1 × 10(-5)). In conclusion, exome arrays complement genome-wide association studies in identifying new variants that contribute to complex human traits.
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http://dx.doi.org/10.1038/ng.2962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4050975PMC
June 2014

Lessons and Implications from Genome-Wide Association Studies (GWAS) Findings of Blood Cell Phenotypes.

Genes (Basel) 2014 Jan 27;5(1):51-64. Epub 2014 Jan 27.

Montreal Heart Institute, Faculté de Médecine, Université de Montréal, 5000 Bélanger Street, Montréal, QC H1T 1C8, Canada.

Genome-wide association studies (GWAS) have identified reproducible genetic associations with hundreds of human diseases and traits. The vast majority of these associated single nucleotide polymorphisms (SNPs) are non-coding, highlighting the challenge in moving from genetic findings to mechanistic and functional insights. Nevertheless, large-scale (epi)genomic studies and bioinformatic analyses strongly suggest that GWAS hits are not randomly distributed in the genome but rather pinpoint specific biological pathways important for disease development or phenotypic variation. In this review, we focus on GWAS discoveries for the three main blood cell types: red blood cells, white blood cells and platelets. We summarize the knowledge gained from GWAS of these phenotypes and discuss their possible clinical implications for common (e.g., anemia) and rare (e.g., myeloproliferative neoplasms) human blood-related diseases. Finally, we argue that blood phenotypes are ideal to study the genetics of complex human traits because they are fully amenable to experimental testing.
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http://dx.doi.org/10.3390/genes5010051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978511PMC
January 2014
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