Aims: To clarify the genetic component of white matter disease and outcome in preterm infants, by exploring genetic variation and correlation with imaging endophenotype and neurodevelopmental phenotype.

Hypothesis: Genetic variation in preterm infants correlates with white matter damage and developmental outcome at age 2.

Exposure variable: Preterm birth less than 37 weeks Gestational Age

Outcome variables:

1. Whole organism - Bayley III neurodevelopmental assessment at age 2 years

2. Endophenotype - White matter damage assessed by quantitative MRI at term equivalent age (Tract Based Spatial Statistics TBSS and Deformation Based Morphometry DBM)

Confounding variables: Sex, birth weight, IUGR, days of assisted ventilation, antenatal steroids, culture positive sepsis, chronic lung disease

Background: More than 1 in 10 babies worldwide are born prematurely, and 40-50% of children with birthweight less than 1000g have neurodevelopmental problems (Marlow et al. 2005). Rates of prematurity are increasing worldwide (WHO 2012) and the predominant type of pathology now consists of diffuse white matter injury rather than florid parenchymal lesions such as cystic periventricular leukomalacia.

Infection, inflammation, chronic lung disease, gender and intra-uterine growth restriction have all been shown to impact brain structure and outcome but this does not fully account for the range of neurological outcomes for preterm babies with similar clinical features in similar environments. Individual susceptibility to injury might have a role to play, and be modulated by genetic factors. The most severe functional end-point of the various brain parenchyma pathologies remains cerebral palsy, a multi-factorial heterogeneous phenotype with a stable incidence despite changes in obstetric and neonatal practices (Stanley et al 2000). A genetic component for cerebral palsy has been suggested by previous case-control studies (Wu et al 2009, Hollegaard et al 2012) but studies tend to report conflicting results and have to contend with small sample sizes and other methodological issues.

The ALSPAC dataset will allow us to overcome some of these hurdles by starting with a large cohort and further increasing power by focusing on biological pathways within those data rather than searching for individually significant SNPs. The documented clinical variables and outcome measures in the database provide the opportunity to account for confounders and assess the clinical significance of resulting gene candidates. Using our bioinformatics expertise we hope to apply novel pathway analysis methods to genome-wide data in order to uncover biological patterns. We aim to combine prior knowledge from three sources in order to select a final group of gene candidates and focus validation:

1. GWAS data (ALSPAC)

2. Biologically driven (neonatal mouse subplate co-expression patterns)

3. Literature evidence

MRI is a safe and non-invasive method of obtaining an intermediate measure of the brain substrate, and our perinatal neuroimaging group has extensive experience in this field. The data obtained by advanced neuroinformatics methods such as TBSS and DBM are quantitative endophenotypes that are known to relate to outcome in the preterm population (Boardman et al 2010, van Kooij et al 2012). We will use advanced bioinformatics strategies to integrate the genetic and imaging datasets, both large amounts of data that can be more informative in conjunction than separately. This will then guide validation in our cohort by correlation of gene variation and linked quantitative imaging, thus opening a window on genetic effects on brain structure and function.

References

Marlow N, Wolke D, Bracewell MA, Samara M, N Engl J Med, 2005;352(1):9-19

Born Too Soon: The Global Action Report on Preterm Birth, WHO 2012-08-13

Stanley F, Blair E, Alberman E. MacKeith Press; 2000

Wu D, Yan-Feng Z, Xiao-Yan X, Li Y, Gong-Chun Z, Xi-Song B, Jiu-Lai T, Dev Med Child Neurol, 2011;53: 217-225

Hollegaard MV, Skogstrand K, Thorsen P, Norgaard-Pedersen B, Hougaard DM, Grove J, Hum Mutat, 2012; Epub ahead of print

van Kooij BJ, de Vries LS, Ball G, van Haastert, Benders MJ, Groenendaal F, Counsell SJ, AJNR Am J Neuroradiol, 2012; 33(1):188-94

Boardman JP, Craven C, Valappil S, Counsell SJ, Dyet LE, Rueckert D, Aljabar P, Rutherford MA, Chew AT, Allsop JM, Cowan F, Edwards AD, Neuroimage, 2010; 52(2): 409-14