A worldwide correlation of lactase persistence phenotype and genotypes

Background  

The ability of adult humans to digest the milk sugar lactose - lactase persistence - is a dominant Mendelian trait that has been a subject of extensive genetic, medical and evolutionary research. Lactase persistence is common in people of European ancestry as well as some African, Middle Eastern and Southern Asian groups, but is rare or absent elsewhere in the world. The recent identification of independent nucleotide changes that are strongly associated with lactase persistence in different populations worldwide has led to the possibility of genetic tests for the trait. However, it is highly unlikely that all lactase persistence-associated variants are known. Using an extensive database of lactase persistence phenotype frequencies, together with information on how those data were collected and data on the frequencies of lactase persistence variants, we present a global summary of the extent to which current genetic knowledge can explain lactase persistence phenotype frequency.     

The power comparison of the haplotype-based collapsing tests and the variant-based collapsing tests for detecting rare variants in pedigrees

Background

Both common and rare genetic variants have been shown to contribute to the etiology of complex diseases. Recent genome-wide association studies (GWAS) have successfully investigated how common variants contribute to the genetic factors associated with common human diseases. However, understanding the impact of rare variants, which are abundant in the human population (one in every 17 bases), remains challenging. A number of statistical tests have been developed to analyze collapsed rare variants identified by association tests. Here, we propose a haplotype-based approach. This work inspired by an existing statistical framework of the pedigree disequilibrium test (PDT), which uses genetic data to assess the effects of variants in general pedigrees. We aim to compare the performance between the haplotype-based approach and the rare variant-based approach for detecting rare causal variants in pedigrees.

Results

Extensive simulations in the sequencing setting were carried out to evaluate and compare the haplotype-based approach with the rare variant methods that drew on a more conventional collapsing strategy. As assessed through a variety of scenarios, the haplotype-based pedigree tests had enhanced statistical power compared with the rare variants based pedigree tests when the disease of interest was mainly caused by rare haplotypes (with multiple rare alleles), and vice versa when disease was caused by rare variants acting independently. For most of other situations when disease was caused both by haplotypes with multiple rare alleles and by rare variants with similar effects, these two approaches provided similar power in testing for association.

Conclusions

The haplotype-based approach was designed to assess the role of rare and potentially causal haplotypes. The proposed rare variants-based pedigree tests were designed to assess the role of rare and potentially causal variants. This study clearly documented the situations under which either method performs better than the other. All tests have been implemented in a software, which was submitted to the Comprehensive R Archive Network (CRAN) for general use as a computer program named rvHPDT.     

Genome survey on invasive veined rapa whelk (<Emphasis Type="Italic">Rapana venosa</Emphasis>) and development of microsatellite loci on large scale

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.     

Targeted exon sequencing fails to identify rare coding variants with large effect in rheumatoid arthritis

Introduction

Although it has been suggested that rare coding variants could explain the substantial missing heritability, very few sequencing studies have been performed in rheumatoid arthritis (RA). We aimed to identify novel functional variants with rare to low frequency using targeted exon sequencing of RA in Korea.

Methods

We analyzed targeted exon sequencing data of 398 genes selected from a multifaceted approach in Korean RA patients (n = 1,217) and controls (n = 717). We conducted a single-marker association test and a gene-based analysis of rare variants. For meta-analysis or enrichment tests, we also used ethnically matched independent samples of Korean genome-wide association studies (GWAS) (n = 4,799) or immunochip data (n = 4,722).

Results

After stringent quality control, we analyzed 10,588 variants of 398 genes from 1,934 Korean RA case controls. We identified 13 nonsynonymous variants with nominal association in single-variant association tests. In a meta-analysis, we did not find any novel variant with genome-wide significance for RA risk. Using a gene-based approach, we identified 17 genes with nominal burden signals. Among them, VSTM1 showed the greatest association with RA (P = 7.80 × 10⁻⁴). In the enrichment test using Korean GWAS, although the significant signal appeared to be driven by total genic variants, we found no evidence for enriched association of coding variants only with RA.

Conclusions

We were unable to identify rare coding variants with large effect to explain the missing heritability for RA in the current targeted resequencing study. Our study raises skepticism about exon sequencing of targeted genes for complex diseases like RA.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0447-7) contains supplementary material, which is available to authorized users.     

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

Background

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.

Results

Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.

Conclusion

The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

     

Resequencing the susceptibility gene,ITGAM, identifies two functionally deleterious rare variants in systemic lupus erythematosus cases

Introduction

The majority of the genetic variance of systemic lupus erythematosus (SLE) remains unexplained by the common disease-common variant hypothesis. Rare variants, which are not detectable by genome-wide association studies because of their low frequencies, are predicted to explain part of this ”missing heritability.” However, recent studies identifying rare variants within known disease-susceptibility loci have failed to show genetic associations because of their extremely low frequencies, leading to the questioning of the contribution of rare variants to disease susceptibility. A common (minor allele frequency = 17.4% in cases) nonsynonymous coding variant rs1143679 (R77H) in ITGAM (CD11b), which forms half of the heterodimeric integrin receptor, complement receptor 3 (CR3), is robustly associated with SLE and has been shown to impair CR3-mediated phagocytosis.

Methods

We resequenced ITGAM in 73 SLE cases and identified two previously unidentified, case-specific nonsynonymous variants, F941V and G1145S. Both variants were genotyped in 2,107 and 949 additional SLE cases, respectively, to estimate their frequencies in a disease population. An in vitro model was used to assess the impact of F941V and G1145S, together with two nonsynonymous ITGAM polymorphisms, A858V (rs1143683) and M441T (rs11861251), on CR3-mediated phagocytosis. A paired two-tailed t test was used to compare the phagocytic capabilities of each variant with that of wild-type CR3.

Results

Both rare variants, F941V and G1145S, significantly impair CR3-mediated phagocytosis in an in vitro model (61% reduction, P = 0.006; 26% reduction, P = 0.0232). However, neither of the common variants, M441T and A858V, had an effect on phagocytosis. Neither rare variant was observed again in the genotyping of additional SLE cases, suggesting that there frequencies are extremely low.

Conclusions

Our results add further evidence to the functional importance of ITGAM in SLE pathogenesis through impaired phagocytosis. Additionally, this study provides a new example of the identification of rare variants in common-allele-associated loci, which, because of their extremely low frequencies, are not statistically associated. However, the demonstration of their functional effects adds support to their contribution to disease risk, and questions the current notion of dismissing the contribution of very rare variants on purely statistical analyses.     

Association mapping of insecticide resistance in wild Anopheles gambiae populations: major variants identified in a low-linkage disequilbrium genome

Background

Association studies are a promising way to uncover the genetic basis of complex traits in wild populations. Data on population stratification, linkage disequilibrium and distribution of variant effect-sizes for different trait-types are required to predict study success but are lacking for most taxa. We quantified and investigated the impacts of these key variables in a large-scale association study of a strongly selected trait of medical importance: pyrethroid resistance in the African malaria vector Anopheles gambiae.

Methodology/Principal Findings

We genotyped ≈1500 resistance-phenotyped wild mosquitoes from Ghana and Cameroon using a 1536-SNP array enriched for candidate insecticide resistance gene SNPs. Three factors greatly impacted study power. (1) Population stratification, which was attributable to co-occurrence of molecular forms (M and S), and cryptic within-form stratification necessitating both a partitioned analysis and genomic control. (2) All SNPs of substantial effect (odds ratio, OR>2) were rare (minor allele frequency, MAF<0.05). (3) Linkage disequilibrium (LD) was very low throughout most of the genome. Nevertheless, locally high LD, consistent with a recent selective sweep, and uniformly high ORs in each subsample facilitated significant direct and indirect detection of the known insecticide target site mutation kdr L1014F (OR≈6; P<10⁻⁶), but with resistance level modified by local haplotypic background.

Conclusion

Primarily as a result of very low LD in wild A. Gambiae, LD-based association mapping is challenging, but is feasible at least for major effect variants, especially where LD is enhanced by selective sweeps. Such variants will be of greatest importance for predictive diagnostic screening.     

Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies

The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%.     

De novo mutations in ARID1B associated with both syndromic and non-syndromic short stature

Background

Human height is a complex trait with a strong genetic basis. Recently, a significant association between rare copy number variations (CNVs) and short stature has been identified, and candidate genes in these rare CNVs are being explored. This study aims to evaluate the association between mutations in ARID1B gene and short stature, both the syndromic and non-syndromic form.

Results

Based on a case-control study of whole genome chromosome microarray analysis (CMA), three overlapping CNVs were identified in patients with developmental disorders who exhibited short stature. ARID1B, a causal gene for Coffin Siris syndrome, is the only gene encompassed by all three CNVs. A following retrospective genotype-phenotype analysis based on a literature review confirmed that short stature is a frequent feature in those Coffin-Siris syndrome patients with ARID1B mutations. Mutation screening of ARID1B coding regions was further conducted in a cohort of 48 non-syndromic short stature patients,andfour novel missense variants including two de novo mutations were found.

Conclusion

These results suggest that haploinsufficient mutations of ARID1B are associated with syndromic short stature including Coffin-Siris syndrome and intellectual disability, while rare missense variants in ARID1B are associated with non-syndromic short stature. This study supports the notion that mutations in genes related to syndromic short stature may exert milder effect and contribute to short stature in the general population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1898-1) contains supplementary material, which is available to authorized users.     

Genome bioinformatic analysis of nonsynonymous SNPs

Background  

Genome-wide association studies of common diseases for common, low penetrance causal variants are underway. A proportion of these will alter protein sequences, the most common of which is the non-synonymous single nucleotide polymorphism (nsSNP). It would be an advantage if the functional effects of an nsSNP on protein structure and function could be predicted, both for the final identification process of a causal variant in a disease-associated chromosome region, and in further functional analyses of the nsSNP and its disease-associated protein.     

Identification of POMC Exonic Variants Associated with Substance Dependence and Body Mass Index

Background

Risk of substance dependence (SD) and obesity has been linked to the function of melanocortin peptides encoded by the proopiomelanocortin gene (POMC).

Methods and Results

POMC exons were Sanger sequenced in 280 African Americans (AAs) and 308 European Americans (EAs). Among them, 311 (167 AAs and 114 EAs) were affected with substance (alcohol, cocaine, opioid and/or marijuana) dependence and 277 (113 AAs and164 EAs) were screened controls. We identified 23 variants, including two common polymorphisms (rs10654394 and rs1042571) and 21 rare variants; 12 of which were novel. We used logistic regression to analyze the association between the two common variants and SD or body mass index (BMI), with sex, age, and ancestry proportion as covariates. The common variant rs1042571 in the 3′UTR was significantly associated with BMI in EAs (Overweight: P _adj = 0.005; Obese: P _adj = 0.018; Overweight+Obese: P _adj = 0.002) but not in AAs. The common variant, rs10654394, was not associated with BMI and neither common variant was associated with SD in either population. To evaluate the association between the rare variants and SD or BMI, we collapsed rare variants and tested their prevalence using Fisher’s exact test. In AAs, rare variants were nominally associated with SD overall and with specific SD traits (SD: P _FET,1df = 0.026; alcohol dependence: P _FET,1df = 0.027; cocaine dependence: P _FET,1df = 0.007; marijuana dependence: P _FET,1df = 0.050) (the P-value from cocaine dependence analysis survived Bonferroni correction). There was no such effect in EAs. Although the frequency of the rare variants did not differ significantly between the normal-weight group and the overweight or obese group in either population, certain rare exonic variants occurred only in overweight or obese subjects without SD.

Conclusion

These findings suggest that POMC exonic variants may influence risk for both SD and elevated BMI, in a population-specific manner. However, common and rare variants in this gene may exert different effects on these two phenotypes.     

No Consistent Effect of ADRB2 Haplotypes on Obesity,Hypertension and Quantitative Traits of Body Fatness and Blood Pressure among 6,514 Adult Danes

Background

Evidence regarding the association of variation within ADRB2, the gene encoding the beta-adrenergic receptor 2 (ADRB2) with obesity and hypertension is exceedingly ambiguous. Despite negative reports, functional impacts of individual genetic variants have been reported. Also, functional haplotypes as well as haplotype combinations affecting expression levels in vivo of ADRB2 mRNA and protein as well as receptor sensitivity have been reported. The aim of the present study was therefore to evaluate if variations within ADRB2 as haplotypes or as haplotype combinations confer an increased prevalence of obesity and hypertension among adults.

Methodology/Principal Findings

We genotyped five variants required to capture common variation in a region including the ADRB2 locus in a population-based study of 6,514 unrelated, middle-aged Danes. Phases of the genotypes were estimated in silico. The variations were then investigated for their combined association with obesity, hypertension and related quantitative traits. The present study did not find consistent evidence for an association of ADRB2 variants with either obesity or hypertension when variations were analysed in a case-control study. The same lack of impact was also seen in the quantitative trait analyses, apart from nominal differences on waist-to-hip ratio and systolic blood pressure between specific haplotype combinations.

Conclusions/Significance

In a population-based sample of 6,514 Danes we found no consistent associations between five common variants which tag the ADRB2 locus and prevalence of obesity or hypertension neither when analysed as individual haplotypes nor as haplotype pairs.     

Association and Mutation Analyses of 16p11.2 Autism Candidate Genes

Background

Autism is a complex childhood neurodevelopmental disorder with a strong genetic basis. Microdeletion or duplication of a ∼500–700-kb genomic rearrangement on 16p11.2 that contains 24 genes represents the second most frequent chromosomal disorder associated with autism. The role of common and rare 16p11.2 sequence variants in autism etiology is unknown.

Methodology/Principal Findings

To identify common 16p11.2 variants with a potential role in autism, we performed association studies using existing data generated from three microarray platforms: Affymetrix 5.0 (777 families), Illumina 550 K (943 families), and Affymetrix 500 K (60 families). No common variants were identified that were significantly associated with autism. To look for rare variants, we performed resequencing of coding and promoter regions for eight candidate genes selected based on their known expression patterns and functions. In total, we identified 26 novel variants in autism: 13 exonic (nine non-synonymous, three synonymous, and one untranslated region) and 13 promoter variants. We found a significant association between autism and a coding variant in the seizure-related gene SEZ6L2 (12/1106 autism vs. 3/1161 controls; p = 0.018). Sez6l2 expression in mouse embryos was restricted to the spinal cord and brain. SEZ6L2 expression in human fetal brain was highest in post-mitotic cortical layers, hippocampus, amygdala, and thalamus. Association analysis of SEZ6L2 in an independent sample set failed to replicate our initial findings.

Conclusions/Significance

We have identified sequence variation in at least one candidate gene in 16p11.2 that may represent a novel genetic risk factor for autism. However, further studies are required to substantiate these preliminary findings.     

Design of DNA Pooling to Allow Incorporation of Covariates in Rare Variants Analysis

Background

Rapid advances in next-generation sequencing technologies facilitate genetic association studies of an increasingly wide array of rare variants. To capture the rare or less common variants, a large number of individuals will be needed. However, the cost of a large scale study using whole genome or exome sequencing is still high. DNA pooling can serve as a cost-effective approach, but with a potential limitation that the identity of individual genomes would be lost and therefore individual characteristics and environmental factors could not be adjusted in association analysis, which may result in power loss and a biased estimate of genetic effect.

Methods

For case-control studies, we propose a design strategy for pool creation and an analysis strategy that allows covariate adjustment, using multiple imputation technique.

Results

Simulations show that our approach can obtain reasonable estimate for genotypic effect with only slight loss of power compared to the much more expensive approach of sequencing individual genomes.

Conclusion

Our design and analysis strategies enable more powerful and cost-effective sequencing studies of complex diseases, while allowing incorporation of covariate adjustment.     

Identifying rare and common disease associated variants in genomic data using Parkinson’s disease as a model

Background

Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson’s disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.

Results

We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson’s disease case–control dataset as a model to demonstrate the application of our method.Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson’s disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson’s disease.

Conclusions

Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson’s disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-014-0088-9) contains supplementary material, which is available to authorized users.     

Resequencing and Association Analysis of PTPRA,a Possible Susceptibility Gene for Schizophrenia and Autism Spectrum Disorders

Background

The PTPRA gene, which encodes the protein RPTP-α, is critical to neurodevelopment. Previous linkage studies, genome-wide association studies, controlled expression analyses and animal models support an association with both schizophrenia and autism spectrum disorders, both of which share a substantial portion of genetic risks.

Methods

We sequenced the protein-encoding areas of the PTPRA gene for single nucleotide polymorphisms or small insertions/deletions (InDel) in 382 schizophrenia patients. To validate their association with the disorders, rare (minor allele frequency <1%), missense mutations as well as one InDel in the 3′UTR region were then genotyped in another independent sample set comprising 944 schizophrenia patients, 336 autism spectrum disorders patients, and 912 healthy controls.

Results

Eight rare mutations, including 3 novel variants, were identified during the mutation-screening phase. In the following association analysis, L59P, one of the two missense mutations, was only observed among patients of schizophrenia. Additionally, a novel duplication in the 3′UTR region, 174620_174623dupTGAT, was predicted to be located within a Musashi Binding Element.

Major Conclusions

No evidence was seen for the association of rare, missense mutations in the PTPRA gene with schizophrenia or autism spectrum disorders; however, we did find some rare variants with possibly damaging effects that may increase the susceptibility of carriers to the disorders.     

Ectopic ossification presenting as osteoid metaplasia in a salivary mucocele in a Shih Tzu dog

Background  

Salivary mucocele is an accumulation of saliva in a single or multiloculated cavity lined by connective tissue that is contiguous to a salivary gland-duct complex and is the most common condition affecting the salivary glands in dogs. Occasionally, different types of metaplastic lesions, such as squamous and osseous metaplasia - which are rare lesions in animals - can be observed in association with salivary mucocele.     

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Background

As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.

Results

The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p = 4.3×10⁻¹⁶) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p = 0.003, frequency = 16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR = 3.1, p = 0.009, frequency 1.2%) and 5q13.2 deletions (OR = 1.5, p = 0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10⁻¹⁸).

Conclusion

Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-368) contains supplementary material, which is available to authorized users.