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Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers.

Amy E Taylor | Richard W Morris | Meg E Fluharty | Johan H Bjorngaard | Bjørn Olav Åsvold | Maiken E Gabrielsen | Archie Campbell | Riccardo Marioni | Meena Kumari | Jenni Hällfors | Satu Männistö | Pedro Marques-Vidal | Marika Kaakinen | Alana Cavadino | Iris Postmus | Lise Lotte N Husemoen | Tea Skaaby | Tarunveer S Ahluwalia | Jorien L Treur | Gonneke Willemsen | Caroline Dale | S Goya Wannamethee | Jari Lahti | Aarno Palotie | Katri Räikkönen | Aliaksei Kisialiou | Alex McConnachie | Sandosh Padmanabhan | Andrew Wong | Christine Dalgård | Lavinia Paternoster | Yoav Ben-Shlomo | Jessica Tyrrell | John Horwood | David M Fergusson | Martin A Kennedy | Tim Frayling | Ellen A Nohr | Lene Christiansen | Kirsten Ohm Kyvik | Diana Kuh | Graham Watt | Johan Eriksson | Peter H Whincup | Jacqueline M Vink | Dorret I Boomsma | George Davey Smith | Debbie Lawlor | Allan Linneberg | Ian Ford | J Wouter Jukema | Christine Power | Elina Hyppönen | Marjo-Riitta Jarvelin | Martin Preisig | Katja Borodulin | Jaakko Kaprio | Mika Kivimaki | Blair H Smith | Caroline Hayward | Pål R Romundstad | Thorkild I A Sørensen | Marcus R Munafò | Naveed Sattar
PLoS genetics | 2014

We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.

Pubmed ID: 25474695 RIS Download

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Associated grants

  • Agency: Medical Research Council, United Kingdom
    Id: MR/K013351/1
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_12013/6
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_12013/1
  • Agency: Medical Research Council, United Kingdom
    Id: G1001799
  • Agency: Wellcome Trust, United Kingdom
    Id: 102215
  • Agency: Medical Research Council, United Kingdom
    Id: MC_PC_15018
  • Agency: Medical Research Council, United Kingdom
    Id: MR/J01351X/1
  • Agency: British Heart Foundation, United Kingdom
    Id: PG/13/66/30442
  • Agency: NCATS NIH HHS, United States
    Id: UL1 TR001425
  • Agency: NIDA NIH HHS, United States
    Id: R01 DA018673
  • Agency: British Heart Foundation, United Kingdom
    Id: RG/13/16/30528
  • Agency: Medical Research Council, United Kingdom
    Id: MR/K023195/1
  • Agency: Chief Scientist Office, United Kingdom
    Id: CZD/16/6/4
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_12019/1

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International HapMap Project (tool)

RRID:SCR_002846

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

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QUANTO (tool)

RRID:SCR_009084

Software program that computes sample size or power for association studies of genes, environmental factors, gene-environment interaction, or gene-gene interaction. Available study designs for a disease (binary) outcome include the unmatched case-control, matched case-control, case-sibling, case-parent, and case-only designs. Study designs for a quantitative tra it include independent individuals and case parent designs. Quanto is a 32-bit Windows application requiring Windows 95, 98, NT, 2000, ME or XP to run. The graphical user interface allows th e user to easily change the model and view the results without having to edit an input file and rerun the program for every model. The results of a session are stored to a log file. This log can be printed or saved to a file for reviewing at a later date. An option is included to create a text file of the log that can be imported into other documents. (entry from Genetic Analysis Software)

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