Imputation of rare variants in next-generation association studies

Shared genetic risk between major orofacial cleft phenotypes in an African population

Nonsyndromic orofacial clefts (NSOFCs) represent a large proportion (70%-80%) of all OFCs. They can be broadly categorized into nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Although NSCL/P and NSCPO are considered etiologically distinct, recent evidence suggests the presence of shared genetic risks. Thus, we investigated the genetic overlap between NSCL/P and NSCPO using African genome-wide association study (GWAS) data on NSOFCs. These data consist of 814 NSCL/P, 205 NSCPO cases, and 2159 unrelated controls. We generated common single-nucleotide variants (SNVs) association summary statistics separately for each phenotype (NSCL/P and NSCPO) under an additive genetic model. Subsequently, we employed the pleiotropic analysis under the composite null (PLACO) method to test for genetic overlap. Our analysis identified two loci with genome-wide significance (rs181737795 [p = 2.58E-08] and rs2221169 [p = 4.5E-08]) and one locus with marginal significance (rs187523265 [p = 5.22E-08]). Using mouse transcriptomics data and information from genetic phenotype databases, we identified MDN1, MAP3k7, KMT2A, ARCN1, and VADC2 as top candidate genes for the associated SNVs. These findings enhance our understanding of genetic variants associated with NSOFCs and identify potential candidate genes for further exploration.

Multi-ethnic imputation system (MI-System): a genotype imputation server for high-dimensional data

OBJECTIVE

Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance.

METHODS

We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to, genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain, towards rapidly and efficiently conducting accurate genotype imputation.

RESULTS

We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds.

CONCLUSION

Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research.

STATEMENT OF SIGNIFICANCE

Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

NyuWa Genome resource: A deep whole-genome sequencing-based variation profile and reference panel for the Chinese population

The lack of haplotype reference panels and whole-genome sequencing resources specific to the Chinese population has greatly hindered genetic studies in the world's largest population. Here, we present the NyuWa genome resource, based on deep (26.2×) sequencing of 2,999 Chinese individuals, and construct a NyuWa reference panel of 5,804 haplotypes and 19.3 million variants, which is a high-quality publicly available Chinese population-specific reference panel with thousands of samples. Compared with other panels, the NyuWa reference panel reduces the Han Chinese imputation error rate by a margin ranging from 30% to 51%. Population structure and imputation simulation tests support the applicability of one integrated reference panel for northern and southern Chinese. In addition, a total of 22,504 loss-of-function variants in coding and noncoding genes are identified, including 11,493 novel variants. These results highlight the value of the NyuWa genome resource in facilitating genetic research in Chinese and Asian populations.

Exploring Impact of Rare Variation in Systemic Lupus Erythematosus by a Genome Wide Imputation Approach

The importance of low frequency and rare variation in complex disease genetics is difficult to estimate in patient populations. Genome-wide association studies are therefore, underpowered to detect rare variation. We have used a combined approach of genome-wide-based imputation with a highly stringent sequence kernel association (SKAT) test and a case-control burden test. We identified 98 candidate genes containing rare variation that in aggregate show association with SLE many of which have recognized immunological function, but also function and expression related to relevant tissues such as the joints, skin, blood or central nervous system. In addition we also find that there is a significant enrichment of genes annotated for disease-causing mutations in the OMIM database, suggesting that in complex diseases such as SLE, such mutations may be involved in subtle or combined phenotypes or could accelerate specific organ abnormalities found in the disease. We here provide an important resource of candidate genes for SLE.

Strategies for phasing and imputation in a population isolate

In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies. We simulated 1,115,604 variants on chromosome 10 for 477 members of the large complex pedigree of Campora, a village within the established isolate of Cilento in southern Italy. We assessed the phasing performance of identical by descent based software ALPHAPHASE and SLRP, LD-based software SHAPEIT2, SHAPEIT3, and BEAGLE, and new software EAGLE that combines both methodologies. For imputation we compared IMPUTE2, IMPUTE4, MINIMAC3, BEAGLE, and new software PBWT. Genotyping errors and missing genotypes were simulated to observe their effects on the performance of each software. Highly accurate phased data were achieved by all software with SHAPEIT2, SHAPEIT3, and EAGLE2 providing the most accurate results. MINIMAC3, IMPUTE4, and IMPUTE2 all performed strongly as imputation software and our study highlights the considerable gain in imputation accuracy provided by a genome sequenced reference panel specific to the population isolate.

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests

BACKGROUND

In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem.

METHODS

We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation.

RESULTS

Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause.

CONCLUSION

The PEGI method may improve power to detect an association over both CC and GI under many circumstances.

Accurate Imputation-Based Screening of Gln368Ter Myocilin Variant in Primary Open-Angle Glaucoma

PURPOSE

Myocilin (MYOC) is a well-established primary open-angle glaucoma (POAG) risk gene, with rare variants known to have high penetrance. The most common clinically relevant risk variant, Gln368Ter, has an allele frequency of 0.1% to 0.3% in populations of European ancestry. Detection of rare MYOC variants has traditionally been conducted using Sanger sequencing. Here we report the use of genotyping arrays and imputation to assess whether rare variants including Gln368Ter can be reliably detected.

METHODS

A total of 1155 cases with advanced POAG and 1992 unscreened controls genotyped on common variant arrays participated in this study. Accuracy of imputation of Gln368Ter variants was compared with direct sequencing. A genome-wide association study was performed using additive model adjusted for sex and the first six principal components.

RESULTS

We found that although the arrays we used were designed to tag common variants, we could reliably impute the Gln368Ter variant (rs74315329). When tested in 1155 POAG cases and 1992 controls, rs74315329 was strongly associated with risk (odds ratio = 15.53, P = 1.07 × 10-9). All POAG samples underwent full sequencing of the MYOC gene, and we found a sensitivity of 100%, specificity of 99.91%, positive predictive value of 95.65%, and negative predictive value of 100% between imputation and sequencing. Gln368Ter was also accurately imputed in a further set of 1801 individuals without POAG. Among the total set of 3793 (1992 + 1801) individuals without POAG, six were predicted (probability > 95%) to carry the risk variant.

CONCLUSIONS

We demonstrate that some clinically important rare variants can be reliably detected using arrays and imputation. These results have important implications for the detection of clinically relevant incidental findings in ongoing and future studies using arrays.

Two-phase and family-based designs for next-generation sequencing studies

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.