Rare disease susceptibility alleles in children with Crohn disease
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REPOSITORIES: dbGaP
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Project description:Crohn's disease is a complex genetic trait characterized by chronic relapsing intestinal inflammation. Genome wide association studies (GWAS) have identified more than 170 loci associated with the disease, accounting for ?14% of the disease variance. We hypothesized that rare genetic variation in GWAS positional candidates also contribute to disease pathogenesis. We performed targeted, massively-parallel sequencing of 101 genes in 205 children with Crohn's disease, including 179 parent-child trios and 200 controls, both of European ancestry. We used the gene burden test implemented in VAAST and estimated effect sizes using logistic regression and meta-analyses. We identified three genes with nominally significant p-values: NOD2, RTKN2, and MGAT3 Only NOD2 was significant after correcting for multiple comparisons. We identified eight novel rare variants in NOD2 that are likely disease-associated. Incorporation of rare variation and compound heterozygosity nominally increased the proportion of variance explained from 0.074 to 0.089. We estimated the population attributable risk and total heritability of variation in NOD2 to be 32.9% and 3.4%, respectively, with 3.7% and 0.25% accounted for by rare putatively functional variants. Sequencing probands (as opposed to genotyping) to identify rare variants and incorporating phase by sequencing parents can recover a portion of the missing heritability of Crohn's disease.
2018-01-01 | S-EPMC6118318 | BioStudies
Project description:The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of "missing heritability"--the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01<MAF<0.05), and 83,856 rare variants (MAF<0.01). We describe a novel method for estimating the proportion of the rare variants that are sequencing errors using parent-progeny duos. We then used mixed model methodology to estimate the proportion of variance captured by these different classes of variants for fat, milk and protein yields, as well as for fertility. Common sequence variants captured 83%, 77%, 76% and 84% of the total genetic variance for fat, milk, and protein yields and fertility, respectively. This was between 2 and 5% more variance than that captured from 600k SNPs on a high density chip, although the difference was not significant. Rare variants captured 3%, 0%, 1% and 14% of the genetic variance for fat, milk and protein yields, and fertility respectively, whereas pedigree explained the remaining amount of genetic variance (none for fertility). The proportion of variation explained by rare variants is likely to be under-estimated due to reduced accuracies of imputation for this class of variants. Using common sequence variants slightly improved accuracy of genomic predictions for fat and milk yield, compared to high density SNP array genotypes. However, including rare variants from transcript regions did not increase the accuracy of genomic predictions. These results suggest that rare variants recover a small percentage of the missing heritability for complex traits, however very large reference sets will be required to exploit this to improve the accuracy of genomic predictions. Our results do suggest the contribution of rare variants to genetic variation may be greater for fitness traits.
2015-01-01 | S-EPMC4671594 | BioStudies
Project description:Gout is a common arthritis caused by monosodium urate crystals. The heritability of serum urate levels is estimated to be 30-70%; however, common genetic variants account for only 7.9% of the variance in serum urate levels. This discrepancy is an example of "missing heritability." The "missing heritability" suggests that variants associated with uric acid levels are yet to be found. By using genomic sequences of the ToMMo cohort, we identified rare variants of the SLC22A12 gene that affect the urate transport activity of URAT1. URAT1 is a transporter protein encoded by the SLC22A12 gene. We grouped the participants with variants affecting urate uptake by URAT1 and analyzed the variance of serum urate levels. The results showed that the heritability explained by the SLC22A12 variants of men and women exceeds 10%, suggesting that rare variants underlie a substantial portion of the "missing heritability" of serum urate levels.
2020-01-01 | S-EPMC7153932 | BioStudies
Project description:Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits. However, they have explained relatively little trait heritability. Recently, we proposed a new analytical approach called regional heritability mapping (RHM) that captures more of the missing genetic variation. This method is applicable both to related and unrelated populations. Here, we demonstrate the power of RHM in comparison with single-SNP GWAS and gene-based association approaches under a wide range of scenarios with variable numbers of quantitative trait loci (QTL) with common and rare causal variants in a narrow genomic region. Simulations based on real genotype data were performed to assess power to capture QTL variance, and we demonstrate that RHM has greater power to detect rare variants and/or multiple alleles in a region than other approaches. In addition, we show that RHM can capture more accurately the QTL variance, when it is caused by multiple independent effects and/or rare variants. We applied RHM to analyze three biometrical eye traits for which single-SNP GWAS have been published or performed to evaluate the effectiveness of this method in real data analysis and detected some additional loci which were not detected by other GWAS methods. RHM has the potential to explain some of missing heritability by capturing variance caused by QTL with low MAF and multiple independent QTL in a region, not captured by other GWAS methods. RHM analyses can be implemented using the software REACTA (http://www.epcc.ed.ac.uk/projects-portfolio/reacta).
2013-01-01 | S-EPMC3832942 | BioStudies
Project description:We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ?97% and ?68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ?17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
2015-01-01 | S-EPMC4589513 | BioStudies
Project description:We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (?0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.
2015-01-01 | S-EPMC4676519 | BioStudies
Project description:We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.
1000-01-01 | S-EPMC5862984 | BioStudies
Project description:BACKGROUND:Mitochondrial dysfunction plays a key role in PD, but the underlying molecular mechanisms remain unresolved. We hypothesized that the disruption of mitochondrial function in PD is primed by rare, protein-altering variation in nuclear genes controlling mitochondrial structure and function. OBJECTIVE:The objective of this study was to assess whether genetic variation in genes associated with mitochondrial function influences the risk of idiopathic PD. METHODS:We employed whole-exome sequencing data from 2 independent cohorts of clinically validated idiopathic PD and controls, the Norwegian ParkWest cohort (n?=?411) and the North American Parkinson's Progression Markers Initiative (n?=?640). We applied burden-based and variance-based collapsing methods to assess the enrichment of rare, nonsynonymous, and damaging genetic variants on genes, exome-wide, and on a comprehensive set of mitochondrial pathways, defined as groups of genes controlling specific mitochondrial functions. RESULTS:Using the sequence kernel association test, we detected a significant polygenic enrichment of rare, nonsynonymous variants in the gene-set encoding the pathway of mitochondrial DNA maintenance. Notably, this was the strongest association in both cohorts and survived multiple testing correction (ParkWest P?=?6.3?× 10-3 , Parkinson's Progression Markers Initiative P?= 6.9?×?10-5 , metaanalysis P?=?3.2?×?10-6 ). CONCLUSIONS:Our results show that the enrichment of rare inherited variation in the pathway controlling mitochondrial DNA replication and repair influences the risk of PD. We propose that this polygenic enrichment contributes to the impairment of mitochondrial DNA homeostasis, thought to be a key mechanism in the pathogenesis of PD, and explains part of the disorder's "missing heritability." © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
2018-01-01 | S-EPMC6282592 | BioStudies
Project description:Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10??). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10??) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10?³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10?¹? and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10??), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10??) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10?¹? compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.
2013-01-01 | S-EPMC3873246 | BioStudies
Project description:Age-related macular degeneration (AMD) is a complex disease caused by a combination of genetic and environmental factors. Genome-wide association studies have identified several common genetic variants associated with AMD, which together account for 15%-65% of the heritability of AMD. Multiple hypotheses to clarify the unexplained portion of genetic variance have been proposed, such as gene-gene interactions, gene-environment interactions, structural variations, epigenetics, and rare variants. Several studies support a role for rare variants with large effect sizes in the pathogenesis of AMD. In this work, we review the methods that can be used to detect rare variants in common diseases, as well as the recent progress that has been made in the identification of rare variants in AMD. In addition, the relevance of these rare variants for diagnosis, prognosis, and treatment of AMD is highlighted.
2014-01-01 | S-EPMC4355254 | BioStudies