A covering method for detecting genetic associations between rare variants and common phenotypes

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.     

Investigation and functional characterization of rare genetic variants in the adipose triglyceride lipase in a large healthy working population

Recent studies demonstrated a strong influence of rare genetic variants on several lipid-related traits. However, their impact on free fatty acid (FFA) plasma concentrations, as well as the role of rare variants in a general population, has not yet been thoroughly addressed. The adipose triglyceride lipase (ATGL) is encoded by the PNPLA2 gene and catalyzes the rate-limiting step of lipolysis. It represents a prominent candidate gene affecting FFA concentrations. We therefore screened the full genomic region of ATGL for mutations in 1,473 randomly selected individuals from the SAPHIR (Salzburg Atherosclerosis Prevention program in subjects at High Individual Risk) Study using a combined Ecotilling and sequencing approach and functionally investigated all detected protein variants by in-vitro studies. We observed 55 novel mostly rare genetic variants in this general population sample. Biochemical evaluation of all non-synonymous variants demonstrated the presence of several mutated but mostly still functional ATGL alleles with largely varying residual lipolytic activity. About one-quarter (3 out of 13) of the investigated variants presented a marked decrease or total loss of catalytic function. Genetic association studies using both continuous and dichotomous approaches showed a shift towards lower plasma FFA concentrations for rare variant carriers and an accumulation of variants in the lower 10%-quantile of the FFA distribution. However, the generally rather small effects suggest either only a secondary role of rare ATGL variants on the FFA levels in the SAPHIR population or a recessive action of ATGL variants. In contrast to these rather small effects, we describe here also the first patient with "neutral lipid storage disease with myopathy" (NLSDM) with a point mutation in the catalytic dyad, but otherwise intact protein.     

Genome-wide searching of rare genetic variants in WTCCC data

Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn’s disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r ² < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants.     

Improved detection of rare genetic variants for diseases

Technology advances have promoted gene-based sequencing studies with the aim of identifying rare mutations responsible for complex diseases. A complication in these types of association studies is that the vast majority of non-synonymous mutations are believed to be neutral to phenotypes. It is thus critical to distinguish potential causative variants from neutral variation before performing association tests. In this study, we used existing predicting algorithms to predict functional amino acid substitutions, and incorporated that information into association tests. Using simulations, we comprehensively studied the effects of several influential factors, including the sensitivity and specificity of functional variant predictions, number of variants, and proportion of causative variants, on the performance of association tests. Our results showed that incorporating information regarding functional variants obtained from existing prediction algorithms improves statistical power under certain conditions, particularly when the proportion of causative variants is moderate. The application of the proposed tests to a real sequencing study confirms our conclusions. Our work may help investigators who are planning to pursue gene-based sequencing studies.     

Two-stage extreme phenotype sequencing design for discovering and testing common and rare genetic variants: efficiency and power

Next-generation sequencing technology provides an unprecedented opportunity to identify rare susceptibility variants. It is not yet financially feasible to perform whole-genome sequencing on a large number of subjects, and a two-stage design has been advocated to be a practical option. In stage I, variants are discovered by sequencing the whole genomes of a small number of carefully selected individuals. In stage II, the discovered variants of a large number of individuals are genotyped to assess associations. Individuals with extreme phenotypes are typically selected in stage I. Using simulated data for unrelated individuals, we explore two important aspects of this two-stage design: the efficiency of discovering common and rare single-nucleotide polymorphisms (SNPs) in stage I and the impact of incomplete SNP discovery in stage I on the power of testing associations in stage II. We applied a sum test and a sum of squared score test for gene-based association analyses evaluating the power of the two-stage design. We obtained the following results from extensive simulation studies and analysis of the GAW17 dataset. When individuals with trait values more extreme than the 99.7-99th quantile were included in stage I, the two-stage design could achieve the same power as or even higher than the one-stage design if the rare causal variants had large effect sizes. In such design, fewer than half of the total SNPs including more than half of the causal SNPs were discovered, which included nearly all SNPs with minor allele frequencies (MAFs) ≥5%, more than half of the SNPs with MAFs between 1% and 5%, and fewer than half of the SNPs with MAFs <1%. Although a one-stage design may be preferable to identify multiple rare variants having small to moderate effect sizes, our observations support using the two-stage design as a cost-effective option for next-generation sequencing studies.     

Comprehensive analysis of NRG1 common and rare variants in Hirschsprung patients

Hirschsprung disease (HSCR, OMIM 142623) is a developmental disorder characterized by the absence of ganglion cells along variable lengths of the distal gastrointestinal tract, which results in tonic contraction of the aganglionic gut segment and functional intestinal obstruction. The RET proto-oncogene is the major gene for HSCR with differential contributions of its rare and common, coding and noncoding mutations to the multifactorial nature of this pathology. Many other genes have been described to be associated with the pathology, as NRG1 gene (8p12), encoding neuregulin 1, which is implicated in the development of the enteric nervous system (ENS), and seems to contribute by both common and rare variants. Here we present the results of a comprehensive analysis of the NRG1 gene in the context of the disease in a series of 207 Spanish HSCR patients, by both mutational screening of its coding sequence and evaluation of 3 common tag SNPs as low penetrance susceptibility factors, finding some potentially damaging variants which we have functionally characterized. All of them were found to be associated with a significant reduction of the normal NRG1 protein levels. The fact that those mutations analyzed alter NRG1 protein would suggest that they would be related with HSCR disease not only in Chinese but also in a Caucasian population, which reinforces the implication of NRG1 gene in this pathology.     

Copy number variants and common disorders: filling the gaps and exploring complexity in genome-wide association studies

Genome-wide association scans (GWASs) using single nucleotide polymorphisms (SNPs) have been completed successfully for several common disorders and have detected over 30 new associations. Considering the large sample sizes and genome-wide SNP coverage of the scans, one might have expected many of the common variants underpinning the genetic component of various disorders to have been identified by now. However, these studies have not evaluated the contribution of other forms of genetic variation, such as structural variation, mainly in the form of copy number variants (CNVs). Known CNVs account for over 15% of the assembled human genome sequence. Since CNVs are not easily tagged by SNPs, might have a wide range of copy number variability, and often fall in genomic regions not well covered by whole-genome arrays or not genotyped by the HapMap project, current GWASs have largely missed the contribution of CNVs to complex disorders. In fact, some CNVs have already been reported to show association with several complex disorders using candidate gene/region approaches, underpinning the importance of regions not investigated in current GWASs. This reveals the need for new generation arrays (some already in the market) and the use of tailored approaches to explore the full dimension of genome variability beyond the single nucleotide scale.     

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene–gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.     

Comprehensive genetic evaluation of common E-cadherin sequence variants and prostate cancer risk: strong confirmation of functional promoter SNP

The E-cadherin gene (CDH1) has been proposed as a prostate cancer (PC) susceptibility gene in several studies. Aberrant protein expression has been related to prognosis and progression in PC. In addition, a functional promoter SNP (rs16260) has been found to associate with PC risk. We performed a comprehensive genetic analysis of CDH1 by using the method of haplotype tagged SNPs in a large Swedish population-based case-control study consisting of 801 controls and 1,636 cases. In addition, Swedish PC families comprising a total of 157 cases sampled for DNA were analyzed for selected SNPs. Seven SNPs, including the promoter SNP rs16260, that captured over 96% of CDH1 haplotype variation were selected as haplotype tagging SNPs and analyzed for associated PC risk. We observed significant confirmation of rs16260 (P=0.003) for cases with a positive family history of PC (FH+) both in an independent case-control population and in PC families. In addition, a common haplotype (HapB, 25%) including the variant allele of rs16260 was associated (P=0.004) with PC risk among FH+ cases. The promoter SNP rs16260 as well as HapB were significantly transmitted to affected offspring in PC families. We report strong confirmation of the association between PC risk in FH+ cases and a functional CDH1 promoter SNP in an independent population. In conjunction with the biological importance of CDH1 our findings encourage further evaluation of genetic variation in CDH1 in relation to PC etiology. Due to the difficulties in replication of genetic association studies, this finding is unusual and novel.     

Common and rare genetic variants of human red blood cell enzymes in Italy

In the present paper we report on new data of the frequency of common and rare variants in the Italian population for ADA, AK-1, 6-PGD, EsA, EsB, EsD, PGM-1, PGM-2, SOD-A, AcP, GPT, and PGI. Moreover we present a comprehensive review of the available data on the electrophoretic variants of red cell enzymes in Italians. We find a considerable degree of genetic heterogeneity between the various populations living in the Peninsula and between the population of the Peninsula and of Sardinia. We also find that the estimates of the average heterozygosity are considerably smaller for the population of Sardinia as compared to Peninsula and Sicily. Finally, we report on the occurrence of several uncommon enzyme variants, which overall frequency is very similar to previously reported estimates for North European populations (Harris et al. 1974).     

Purification and properties of genetic variants of mouse trypsinogen

The presence of three trypsinogens (Try-III, Try-I, and Try-II) in the mouse is demonstrated by DEAE-cellulose column chromatography. Two genetic variants of Try-I are detected, because the activity of Try-I is different between the Mol-A strain and seven other strains. The Prt-3 locus controls the activity of Try-I. The Prt-3a gene exists in CFO, BS, KR, BALB/cJ, C57BL/6J, CBA/J, and 129/Sv-S1-CP strains, whereas the Prt-3b gene is present only in the Mol-A strain. Each Try-I from the CFO or Mol-A strain was purified. The fact that Try-I activity of the Mol-A strain is much higher than those of other strains is because of a difference in the specific activity; the ratio of the Kcat (sec-1) value with Tos-Arg-OMe to that with Bz-Arg-OEt is different between the variants from the CFO and those from the Mol-A strains, being much higher in the Mol-A strain. Also, chicken ovomucoid inhibited Try-I activity of the CFO strain at a molar ratio of one ovomucoid to two trypsins; Try-I activity of the Mol-A strain was only 50% inhibited even with an excess of ovomucoid. There was no difference between genetic variants of Try-I in molecular weight, Km values with Bz-Arg-OEt or Tos-Arg-OMe, pH optimum profile, or inhibition by soybean trypsin inhibitor.     

Networks of neuronal genes affected by common and rare variants in autism spectrum disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk.     

Isoelectric focusing of rare transferrin (Tf) variants and common TfC subtypes

Summary Transferrin^C (Tf^C) subtypes were determined by isoelectric focusing (PAGIF) on samples from 90 carriers of the Tf^B and Tf^D alleles. In all cases of CB and CD heterozygotes only one of the two common subtypes of the Tf^C allele, Tf^C1 or Tf^C2, was observed. This is considered strong support for the hypothesis of two common alleles at the Tf locus. The different isofocusing patterns of rare B and D variants are compared with the results obtained after agarose gel electrophoresis (AGE).