[statisticalgenetics] Today, 12pm, QBI L7 Seminar room, Jian Yang's talk on complex traits using whole genome sequence data

  • From: Guo-Bo Chen <guobo.chen@xxxxxxxxx>
  • To: Statistical Genetics <statisticalgenetics@xxxxxxxxxxxxx>
  • Date: Tue, 2 Jun 2015 23:33:56 +0000

QBI Neuroscience Seminar
Time: 12:00 - 1:00PM
Location: Level 7 Auditorium, QBI
Speaker: A/Professor Jian Yang
Title: Estimating genetic variation for human complex traits and common
diseases using whole genome sequence data
Abstract: Understanding the extent to which variation in DNA sequence explains
variation in phenotype is of fundamental importance to genetics, and
quantifying the relative contributions of common and rare variants to
phenotypic variation is of great importance for experimental designs that aim
to disentangle the genetic architecture of complex traits (e.g. obesity and
schizophrenia). We developed a new method to estimate heritability for complex
traits from unrelated individuals using whole-genome sequencing (WGS) data. We
demonstrate by simulations based on real WGS data that ~97% and ~72% of
variation at common and rare sequence variants, respectively, can be captured
by SNP array data imputed to a sequenced reference panel. Using our method, we
estimate from 44,126 unrelated individuals of European descent that all ~17M
imputed variants explain 56% of variance for height (a model trait) and 27% for
obesity (as measured by body mass index, BMI). We find evidence that both
height and BMI have been under natural selection. We conclude that narrow-sense
heritability is likely to be between 60 and 70% for height and between 30 and
40% for BMI. Therefore, missing heritability is small for both these traits.
For further gene and variant discovery of complex traits, a design with SNP
arrays followed by sequence imputation is more cost-effective than WGS at
current prices. Our results have implications for gene discovery for common
diseases, including neurological and psychiatric disorders.

Tel. +61.7.3346.6408
The Queensland Brain Institute, QBI Building (#79)
The University of Queensland, St Lucia, QLD 4072, Australia

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