Using genome-wide complex trait analysis to quantify ‘missing heritability’ in Parkinson’s disease

A full list of The International Parkinson's Disease Genomics Consortium (IPDGC) and The Wellcome Trust Case Control Consortium 2 (WTCCC2) members and affiliations appears at the end of this manuscript.
Human Molecular Genetics (Impact Factor: 6.39). 08/2012; 21(22):4996-5009. DOI: 10.1093/hmg/dds335
Source: PubMed


Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

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    • "Given these results, it is expected that low-frequency variants, in particular functional variants in coding sequences, with relatively large effect sizes still remain to be identified [22]. For instance, GWASs have identified risk variants at over two dozen loci affecting the development of PD; however, only 3–5% of phenotypic variance associated with PD can be explained using all SNPs within a region identified by replicated GWASs [23]. These estimates are substantially smaller than those obtained in epidemiological studies [24] [25], pointing to the compelling evidence of yet-to-be-discovered additional genetic factors. "
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    ABSTRACT: Sporadic neurodegenerative diseases are complex in nature, that is, they involve multiple genetic and environmental factors that may play roles at the molecular level. In contrast to diseases with Mendelian inheritance, the genomic signatures of common sporadic forms of neurodegenerative diseases largely remain unknown. Over the past decade, genome-wide association studies employing common single-nucleotide polymorphisms have been intensively conducted, in which the theoretical framework is based on the "common disease-common variants" hypothesis. Another paradigm is a sequence-based association study under the "common disease-multiple rare variants" hypothesis. Because current next-generation sequencing technologies enable us to obtain virtually all the variants in human genome irrespective of allele frequencies, it is anticipated that sequence-based association studies will become the mainstream approach. In this review, we present brief overviews of molecular genetic approaches to elucidate the molecular bases of sporadic forms of neurodegenerative diseases, including Alzheimer disease, Parkinson disease, and multiple system atrophy as examples.
    Full-text · Article · Jul 2014 · Biochemical and Biophysical Research Communications
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    • "Furthermore, current estimations of the proportion of PD heritability that can be explained by SNPs tested directly or indirectly by GWAS (whether they were found to be associated or not) suggest that most of the heritability has yet to be identified. In a meta-analysis of GWAS, Keller et al. calculated that 27% of the phenotypic variance is explained for PD in general, 15% for early-onset PD and 24% for late-onset PD [44]. The missing heritability is expected to be to be hidden in common modest effect variants located in loci still unknown but also in rare variants such as the GBA mutations located in both known and unknown loci and in variants that are not traditionally well captured by current genotyping and sequencing platforms. "
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    ABSTRACT: Parkinson's disease is a common age-related progressive neurodegenerative disorder. Over the last 10 years, advances have been made in our understanding of the etiology of the disease with the greatest insights perhaps coming from genetic studies, including genome-wide association approaches. These large scale studies allow the identification of genomic regions harboring common variants associated to disease risk. Since the first genome-wide association study on sporadic Parkinson's disease performed in 2005, improvements in study design, including the advent of meta-analyses, have allowed the identification of ~21 susceptibility loci. The first loci to be nominated were previously associated to familial PD (SNCA, MAPT, LRRK2) and these have been extensively replicated. For other more recently identified loci (SREBF1, SCARB2, RIT2) independent replication is still warranted. Cumulative risk estimates of associated variants suggest that more loci are still to be discovered. Additional association studies combined with deep re-sequencing of known genome-wide association study loci are necessary to identify the functional variants that drive disease risk. As each of these associated genes and variants are identified they will give insight into the biological pathways involved the etiology of Parkinson's disease. This will ultimately lead to the identification of molecules that can be used as biomarkers for diagnosis and as targets for the development of better, personalized treatment.
    Full-text · Article · Feb 2014 · Current Genomics
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    ABSTRACT: Purpose: The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease. Methods: A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve. Results: Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone. Conclusion: Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.
    No preview · Article · Dec 2012 · Genetics in medicine: official journal of the American College of Medical Genetics
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