Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene-environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.
Somatic mutations in the phosphatidylinositol/AKT/mTOR pathway cause segmental overgrowth disorders. Diagnostic descriptors associated with PIK3CA mutations include fibroadipose overgrowth (FAO), Hemihyperplasia multiple Lipomatosis (HHML), Congenital Lipomatous Overgrowth, Vascular malformations, Epidermal nevi, Scoliosis/skeletal and spinal (CLOVES) syndrome, macrodactyly, and the megalencephaly syndrome, Megalencephaly-Capillary malformation (MCAP) syndrome. We set out to refine the understanding of the clinical spectrum and natural history of these phenotypes, and now describe 35 patients with segmental overgrowth and somatic PIK3CA mutations. The phenotypic data show that these previously described disease entities have considerable overlap, and represent a spectrum. While this spectrum overlaps with Proteus syndrome (sporadic, mosaic, and progressive) it can be distinguished by the absence of cerebriform connective tissue nevi and a distinct natural history. Vascular malformations were found in 15/35 (43%) and epidermal nevi in 4/35 (11%) patients, lower than in Proteus syndrome. Unlike Proteus syndrome, 31/35 (89%) patients with PIK3CA mutations had congenital overgrowth, and in 35/35 patients this was asymmetric and disproportionate. Overgrowth was mild with little postnatal progression in most, while in others it was severe and progressive requiring multiple surgeries. Novel findings include: adipose dysregulation present in all patients, unilateral overgrowth that is predominantly left-sided, overgrowth that affects the lower extremities more than the upper extremities and progresses in a distal to proximal pattern, and in the most severely affected patients is associated with marked paucity of adipose tissue in unaffected areas. While the current data are consistent with some genotype–phenotype correlation, this cannot yet be confirmed. © The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals, Inc.
Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (λ) (ranging from 1.00 to 4.21), fixation index (Fst) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS).
Although high-density SNP genotyping platforms generate a momentum for detailed genome-wide association (GWA) studies, an offshoot is a new insight into population genetics. Here, we present an example in one of the best-known founder populations by scrutinizing ten distinct Finnish early- and late-settlement subpopulations. By determining genetic distances, homozygosity, and patterns of linkage disequilibrium, we demonstrate that population substructure, and even individual ancestry, is detectable at a very high resolution and supports the concept of multiple historical bottlenecks resulting from consecutive founder effects. Given that genetic studies are currently aiming at identifying smaller and smaller genetic effects, recognizing and controlling for population substructure even at this fine level becomes imperative to avoid confounding and spurious associations. This study provides an example of the power of GWA data sets to demonstrate stratification caused by population history even within a seemingly homogeneous population, like the Finns. Further, the results provide interesting lessons concerning the impact of population history on the genome landscape of humans, as well as approaches to identify rare variants enriched in these subpopulations.
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