Targeted Resequencing of the Pericentromere of Chromosome 2 Linked to Constitutional Delay of Growth and Puberty

Constitutional delay of growth and puberty (CDGP) is the most common cause of pubertal delay. CDGP is defined as the proportion of the normal population who experience pubertal onset at least 2 SD later than the population mean, representing 2.3% of all adolescents. While adolescents with CDGP spontaneously enter puberty, they are at risk for short stature, decreased bone mineral density, and psychosocial problems. Genetic factors contribute heavily to the timing of puberty, but the vast majority of CDGP cases remain biologically unexplained, and there is no definitive test to distinguish CDGP from pathological absence of puberty during adolescence. Recently, we published a study identifying significant linkage between a locus at the pericentromeric region of chromosome 2 (chr 2) and CDGP in Finnish families. To investigate this region for causal variation, we sequenced chr 2 between the genomic coordinates of 79–124 Mb (genome build GRCh37) in the proband and affected parent of the 13 families contributing most to this linkage signal. One gene, DNAH6, harbored 6 protein-altering low-frequency variants (< 6% in the Finnish population) in 10 of the CDGP probands. We sequenced an additional 135 unrelated Finnish CDGP subjects and utilized the unique Sequencing Initiative Suomi (SISu) population reference exome set to show that while 5 of these variants were present in the CDGP set, they were also present in the Finnish population at similar frequencies. Additional variants in the targeted region could not be prioritized for follow-up, possibly due to gaps in sequencing coverage or lack of functional knowledge of non-genic genomic regions. Thus, despite having a well-characterized sample collection from a genetically homogeneous population with a large population-based reference sequence dataset, we were unable to pinpoint variation in the linked region predisposing delayed puberty. This study highlights the difficulties of detecting genetic variants under linkage regions for complex traits and suggests that advancements in annotation of gene function and regulatory regions of the genome will be critical for solving the genetic background of complex phenotypes like CDGP.     

An evaluation of different target enrichment methods in pooled sequencing designs for complex disease association studies

Pooled sequencing can be a cost-effective approach to disease variant discovery, but its applicability in association studies remains unclear. We compare sequence enrichment methods coupled to next-generation sequencing in non-indexed pools of 1, 2, 10, 20 and 50 individuals and assess their ability to discover variants and to estimate their allele frequencies. We find that pooled resequencing is most usefully applied as a variant discovery tool due to limitations in estimating allele frequency with high enough accuracy for association studies, and that in-solution hybrid-capture performs best among the enrichment methods examined regardless of pool size.     

Variants in TF and HFE Explain ∼40% of Genetic Variation in Serum-Transferrin Levels

Only a small proportion of genetic variation in complex traits has been explained by SNPs from genome-wide association studies (GWASs). We report the results from two GWASs for serum markers of iron status (serum iron, serum transferrin, transferrin saturation with iron, and serum ferritin), which are important in iron overload (e.g., hemochromatosis) and deficiency (e.g., anemia) conditions. We performed two GWASs on samples of Australians of European descent. In the first GWAS, 411 adolescent twins and their siblings were genotyped with 100K SNPs. rs1830084, 10.8 kb 3′ of TF, was significantly associated with serum transferrin (p total association test = 1.0 × 10⁻⁹; p within-family test = 2.2 × 10⁻⁵). In the second GWAS on an independent sample of 459 female monozygotic (MZ) twin pairs genotyped with 300K SNPs, we found rs3811647 (within intron 11 of TF, HapMap CEU r² with rs1830084 = 0.86) was significantly associated with serum transferrin (p = 3.0 × 10⁻¹⁵). In the second GWAS, we found two additional and independent SNPs on TF (rs1799852 and rs2280673) and confirmed the known C282Y mutation in HFE to be independently associated with serum transferrin. The three variants in TF (rs3811647, rs1799852 and rs2280673) plus the HFE C282Y mutation explained ~40% of genetic variation in serum transferrin (p = 7.8 × 10⁻²⁵). These findings are potentially important for our understanding of iron metabolism and of regulation of hepatic protein secretion, and also strongly support the hypothesis that the genetic architecture of some endophenotypes may be simpler than that of disease.     

A susceptibility locus for migraine with aura, on chromosome 4q24

Migraine is a complex neurovascular disorder with substantial evidence supporting a genetic contribution. Prior attempts to localize susceptibility loci for common forms of migraine have not produced conclusive evidence of linkage or association. To date, no genomewide screen for migraine has been published. We report results from a genomewide screen of 50 multigenerational, clinically well-defined Finnish families showing intergenerational transmission of migraine with aura (MA). The families were screened using 350 polymorphic microsatellite markers, with an average intermarker distance of 11 cM. Significant evidence of linkage was found between the MA phenotype and marker D4S1647 on 4q24. Using parametric two-point linkage analysis and assuming a dominant mode of inheritance, we found for this marker a maximum LOD score of 4.20 under locus homogeneity (P=.000006) or locus heterogeneity (P=.000011). Multipoint parametric (HLOD = 4.45; P=.0000058) and nonparametric (NPL(all) = 3.43; P=.0007) analyses support linkage in this region. Statistically significant linkage was not observed in any other chromosomal region.     

The Contribution of GWAS Loci in Familial Dyslipidemias

Familial combined hyperlipidemia (FCH) is a complex and common familial dyslipidemia characterized by elevated total cholesterol and/or triglyceride levels with over five-fold risk of coronary heart disease. The genetic architecture and contribution of rare Mendelian and common variants to FCH susceptibility is unknown. In 53 Finnish FCH families, we genotyped and imputed nine million variants in 715 family members with DNA available. We studied the enrichment of variants previously implicated with monogenic dyslipidemias and/or lipid levels in the general population by comparing allele frequencies between the FCH families and population samples. We also constructed weighted polygenic scores using 212 lipid-associated SNPs and estimated the relative contributions of Mendelian variants and polygenic scores to the risk of FCH in the families. We identified, across the whole allele frequency spectrum, an enrichment of variants known to elevate, and a deficiency of variants known to lower LDL-C and/or TG levels among both probands and affected FCH individuals. The score based on TG associated SNPs was particularly high among affected individuals compared to non-affected family members. Out of 234 affected FCH individuals across the families, seven (3%) carried Mendelian variants and 83 (35%) showed high accumulation of either known LDL-C or TG elevating variants by having either polygenic score over the 90^th percentile in the population. The positive predictive value of high score was much higher for affected FCH individuals than for similar sporadic cases in the population. FCH is highly polygenic, supporting the hypothesis that variants across the whole allele frequency spectrum contribute to this complex familial trait. Polygenic SNP panels improve identification of individuals affected with FCH, but their clinical utility remains to be defined.     

Use of population isolates for mapping complex traits

Geneticists have repeatedly turned to population isolates for mapping and cloning Mendelian disease genes. Population isolates possess many advantages in this regard. Foremost among these is the tendency for affected individuals to share ancestral haplotypes derived from a handful of founders. These haplotype signatures have guided scientists in the fine mapping of scores of rare disease genes. The past successes with Mendelian disorders using population isolates have prompted unprecedented interest among medical researchers in both the public and private sectors. Despite the obvious genetic and environmental complications, geneticists have targeted several population isolates for mapping genes for complex diseases.     

Analysis of genetic variation in the GenomEUtwin project.

Multiallelic short tandem repeat polymorphisms, or microsatellites, are useful markers in genome wide scans to identify chromosomal regions containing genes underlying disease loci. The biallelic single nucleotide polymorphism (SNP) can be used to fine map previously identified large candidate regions or to test functional candidate genes by association analysis. In the GenomEUtwin project the population based impact of susceptibility genes for six multifactorial traits will be studied. A genome wide panel of informative human microsatellite markers will be analyzed by fluorescent capillary electrophoresis in well characterized twin and population samples. Contrary to microsatellites, selection of the most informative panels of SNPs is hampered by imperfect data on the allele frequencies and population distribution of SNPs markers in the databases. Therefore, selection of SNPs requires a substantial amount of bioinformatics, and, the SNPs need to be validated experimentally in the relevant populations prior to genotyping large sample sets. In the GenomEUtwin project, large scale genotyping of SNPs will be performed using the SNPstreamUHT and MassARRAY genotyping systems that are based on the primer extension reaction principle combined with fluorescent and mass spectrometric detection, respectively. Production of the genotyping data will be a joint effort by GenomEUtwin partners at the University of Helsinki, the National Public Health Institute in Helsinki, Finland and Uppsala University, Sweden. All genotyping data will be stored in a common database established specifically for the GenomEUtwin project, from where it can be accessed by the twin research centres that provided the samples for genotyping.