METHODS: This was a prospective cohort study. Multivariable logistic regression was used to evaluate the effect of early vs. late FGR, placental biomarkers and fetoplacental Dopplers on Maternal Vascular Malperfusion (MVM) which was the commonest placental abnormality identified.
RESULTS: There were 161 (53.5 %) early FGR and 140 (46.5 %) late FGR cases. MVM abnormalities were present in 154 (51.2 %), VUE in 45 (14.6 %), FVM in 16 (5.3 %), DVM in 14 (4.7 %) and CHI in 4 (1.3 %) cases. The odds of MVM were higher in early compared to late FGR cohort (OR 1.89, 95%CI 1.14, 3.14, p = 0.01). Low maternal PlGF levels <100 ng/L (OR 2.34, 95%CI 1.27,4.31, p = 0.01), high sFlt-1 level (OR 2.13, 95%CI 1.35, 3.36, p = 0.001) or elevated sFlt-1/PlGF ratio (OR 3.48, 95%CI 1.36, 8.91, p = 0.01) were all associated with MVM. Increased UA PI > 95th centile (OR 2.91, 95%CI 1.71, 4.95, p=<0.001) and mean UtA PI z-score (OR 1.74, 95%CI 1.15, 2.64, p = 0.01) were associated with higher odds of MVM. Rates of severe non-neurological morbidity were highest in the MVM, FVM, and CHI cohorts (44.8 %, 50 %, and 50 % respectively).
CONCLUSION: MVM was the commonest placental abnormality in FGR, particularly in early-onset disease. Low maternal PlGF levels, high sFlt-1 levels, elevated sFlt-1/PlGF ratio, and abnormal fetoplacental Dopplers were also significantly associated with MVM. MVM, FVM, and CHI abnormalities were associated with lower median birthweight, higher rates of preterm birth, operative birth for non-reassuring fetal status, and severe neonatal non-neurological morbidity.
METHOD: A literature review was carried out, power and other issues discussed, and planned studies assessed.
RESULTS: Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.
CONCLUSIONS: GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.