Albert Smith

Albert Smith

orcid

Research Focus & Metrics

Academic Career Length

40Years (1987-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 February 2015 in Nature
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Journal Article
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ALAdam E Locke
Adam E Locke
BKBratati Kahali
Bratati Kahali
SBSonja I Berndt
Sonja I Berndt
AJAnne E Justice
Anne E Justice
TPTune H Pers
Tune H Pers
ESElizabeth K. Speliotes
Elizabeth K. Speliotes

27 October 2013 in Nature Genetics
Verified
Journal Article
Closed access
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E(European Alzheimer'S Disease Initiative (Eadi)
European Alzheimer'S Disease Initiative (Eadi)
JLJean-Charles Lambert
Jean-Charles Lambert
G(Genetic And Environmental Risk In Alzheimer'S Disease (Gerad)
Genetic And Environmental Risk In Alzheimer'S Disease (Gerad)
A(Alzheimer's Disease Genetic Consortium (ADGC)
Alzheimer's Disease Genetic Consortium (ADGC)
C(Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)
Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)
PAPhilippe Amouyel
Philippe Amouyel

5 August 2010 in Nature
Verified
Journal Article
Open access
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TTTanya M. Teslovich
Tanya M. Teslovich
KMKiran Musunuru
Kiran Musunuru
ASAlbert Smith
Albert Smith
AEAndrew C. Edmondson
Andrew C. Edmondson
ISIoannis M. Stylianou
Ioannis M. Stylianou
SKSekar Kathiresan
Sekar Kathiresan
Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 × 10−8), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes—GALNT2, PPP1R3B and TTC39B—with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.

28 February 2019 in Nature Genetics
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Journal Article
Closed access
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BKBrian W. Kunkle
Brian W. Kunkle
T(The European Alzheimer’s Disease Initiative (Eadi)
The European Alzheimer’s Disease Initiative (Eadi)
BGBenjamin Grenier-Boley
Benjamin Grenier-Boley
RSRebecca Sims
Rebecca Sims
JBJoshua C. Bis
Joshua C. Bis
MPMargaret A. Pericak-Vance
Margaret A. Pericak-Vance

10 October 2010 in Nature Genetics
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Journal Article
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ESElizabeth K Speliotes
Elizabeth K Speliotes
MAMagic
Magic
CWCristen J Willer
Cristen J Willer
SBSonja I Berndt
Sonja I Berndt
KMKeri L Monda
Keri L Monda
OCon behalf of Procardis Consortium
on behalf of Procardis Consortium
Ruth Loos and colleagues report results of a large genome-wide association study for body mass index. They identify 18 new loci associated with this trait, some of which map near key hypothalamic regulators of energy balance. Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10−8), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

10 February 2021 in Nature
Verified
Journal Article
Open access
PDF file
DTDaniel Taliun
Daniel Taliun
DHDaniel N Harris
Daniel N Harris
MKMichael D Kessler
Michael D Kessler
JCJedidiah Carlson
Jedidiah Carlson
ZSZachary A Szpiech
Zachary A Szpiech
GAGonçalo R Abecasis
Gonçalo R Abecasis
The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.