Large Reference Panels Research Articles

O.18 Systematic identification of causal mutations in Mendelian disorders using exome sequence data

Exome sequencing has proven to be a powerful and cost-effective approach for the identification of causal mutations in many patients suffering from rare, severe Mendelian diseases. However, exome analysis unambiguously identifies a causal mutation in only 30–50% of sequenced families, indicating much work remains to be done to increase the yield of causal variants from sequencing-based approaches. Causal mutations can be missed by current exome sequencing approaches for a variety of reasons. Conversely, there may be multiple gene candidates that require further information to prioritize for functional studies. We describe the development of an integrated pipeline for the identification of causal variants from exome data and its application to a cohort of severe, undiagnosed muscle disease patients. Our online application called xBrowse enables the intuitive analysis of family-based exome data, permitting researchers and clinicians to rapidly explore the effects of altering inheritance modes and function/quality filters on the identification of potential causal mutations. Through xBrowse, our collaborators have early access to gene-based RNA expression data across various human tissues, a disease gene-centric protein–protein interaction networks and a large reference panel of over 50,000 exomes. We have applied this integrated approach to exome data over 250 individuals consisting of families and probands affected by a range of neuromuscular diseases. We describe the detection of novel sequence variants with strong evidence for causality in these patients, and provide case studies indicating the value of tissue expression data, protein–protein interaction networks, large reference panels for the prioritization of disease-associated mutations.

Improved ancestry inference using weights from external reference panels

Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies. We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R(2)) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies. The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/. Supplementary data are available at Bioinformatics online.

A pruning strategy of reference panels for fast SNP genotype imputation

In recent genome-wide association studies, the task of genotype imputation for missing SNPs is a common procedure to increase the power of observed genetic markers. For genotype imputation, they usually employ publicly available resources, such as the International HapMap Project data or the 1000 Genome Project data, as a reference panel. However, lately, the volume of publicly available resources is rapidly increasing with the maturation of high-throughput genotyping technology. Thus, it often requires heavy computation for learning large reference panels, leading to long imputation time. In this work, to handle such problem, we propose a pruning strategy for the construction of imputation reference panels which is to reduce the size of reference panel data by excluding (or pruning) somewhat redundant samples from the reference panel based on the estimation of the kinship coefficients between samples. For evaluation, this approach was implemented under the Beagle framework and was tested on two real datasets, Mao et al.’s prostate cancer data and KNIH’s diabetes data. Our experiment results show that the proposed pruning strategy for reference panel construction can provide fast imputation time without the loss of imputation accuracy.

High-resolution whole-genome haplotyping using limited seed data

High-resolution whole-genome haplotyping using limited seed data

MaCH‐Admix: Genotype Imputation for Admixed Populations

Imputation in admixed populations is an important problem but challenging due to the complex linkage disequilibrium (LD) pattern. The emergence of large reference panels such as that from the 1,000 Genomes Project enables more accurate imputation in general, and in particular for admixed populations and for uncommon variants. To efficiently benefit from these large reference panels, one key issue to consider in modern genotype imputation framework is the selection of effective reference panels. In this work, we consider a number of methods for effective reference panel construction inside a hidden Markov model and specific to each target individual. These methods fall into two categories: identity-by-state (IBS) based and ancestry-weighted approach. We evaluated the performance on individuals from recently admixed populations. Our target samples include 8,421 African Americans and 3,587 Hispanic Americans from the Women' Health Initiative, which allow assessment of imputation quality for uncommon variants. Our experiments include both large and small reference panels; large, medium, and small target samples; and in genome regions of varying levels of LD. We also include BEAGLE and IMPUTE2 for comparison. Experiment results with large reference panel suggest that our novel piecewise IBS method yields consistently higher imputation quality than other methods/software. The advantage is particularly noteworthy among uncommon variants where we observe up to 5.1% information gain with the difference being highly significant (Wilcoxon signed rank test P-value < 0.0001). Our work is the first that considers various sensible approaches for imputation in admixed populations and presents a comprehensive comparison.

Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.

From HLA association to function

A new study refines the association signals for rheumatoid arthritis susceptibility in the major histocompatibility complex (MHC) region to five amino-acid positions encoded in three HLA genes, all within peptide-binding grooves. By adapting statistical methods from genome-wide association studies (GWAS) and using imputation from a large reference panel, they demonstrate the potential for this approach to identify functional variants in associated regions.

Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to HLA-DRB1 alleles. Yet controversy persists about the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 seropositive cases and 14,974 controls, we imputed and tested classical alleles and amino acid polymorphisms for HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 along with 3,117 SNPs across the MHC. Conditional and haplotype analyses reveal that three amino acid positions (11, 71 and 74) in HLA-DRβ1, and single amino acid polymorphisms in HLA-B (position 9) and HLA-DPβ1 (position 9), all located in the peptide-binding grooves, almost completely explain the MHC association to disease risk. This study illustrates how imputation of functional variation from large reference panels can help fine-map association signals in the MHC.

Genetic, morphometric, and behavioral factors linked to the midsagittal area of the corpus callosum.

The corpus callosum is the main commissure connecting left and right cerebral hemispheres, and varies widely in size. Differences in the midsagittal area of the corpus callosum (MSACC) have been associated with a number of cognitive and behavioral phenotypes, including obsessive-compulsive disorders, psychopathy, suicidal tendencies, bipolar disorder, schizophrenia, autism, and attention deficit hyperactivity disorder. Although there is evidence to suggest that MSACC is heritable in normal human populations, there is surprisingly little evidence concerning the genetic modulation of this variation. Mice provide a potentially ideal tool to dissect the genetic modulation of MSACC. Here, we use a large genetic reference panel – the BXD recombinant inbred line – to dissect the natural variation of the MSACC. We estimated the MSACC in over 300 individuals from nearly 80 strains. We found a 4-fold difference in MSACC between individual mice, and a 2.5-fold difference among strains. MSACC is a highly heritable trait (h2 = 0.60), and we mapped a suggestive QTL to the distal portion of Chr 14. Using sequence data and neocortical expression databases, we were able to identify eight positional and plausible biological candidate genes within this interval. Finally, we found that MSACC correlated with behavioral traits associated with anxiety and attention.

Integrating common and rare genetic variation in diverse human populations.

Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called 'HapMap 3', includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of <or=5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.

A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R(2), and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.

Should healthy volunteers in clinical trials be paid according to risk? No

<h3>Abstract</h3> Low-coverage whole genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined...

Natural variation and genetic covariance in adult hippocampal neurogenesis

Adult hippocampal neurogenesis is highly variable and heritable among laboratory strains of mice. Adult neurogenesis is also remarkably plastic and can be modulated by environment and activity. Here, we provide a systematic quantitative analysis of adult hippocampal neurogenesis in two large genetic reference panels of recombinant inbred strains (BXD and AXB/BXA, n = 52 strains). We combined data on variation in neurogenesis with a new transcriptome database to extract a set of 190 genes with expression patterns that are also highly variable and that covary with rates of (i) cell proliferation, (ii) cell survival, or the numbers of surviving (iii) new neurons, and (iv) astrocytes. Expression of a subset of these neurogenesis-associated transcripts was controlled in cis across the BXD set. These self-modulating genes are particularly interesting candidates to control neurogenesis. Among these were musashi (Msi1h) and prominin1/CD133 (Prom1), both of which are linked to stem-cell maintenance and division. Twelve neurogenesis-associated transcripts had significant cis-acting quantitative trait loci, and, of these, six had plausible biological association with adult neurogenesis (Prom1, Ssbp2, Kcnq2, Ndufs2, Camk4, and Kcnj9). Only one cis-acting candidate was linked to both neurogenesis and gliogenesis, Rapgef6, a downstream target of ras signaling. The use of genetic reference panels coupled with phenotyping and global transcriptome profiling thus allowed insight into the complexity of the genetic control of adult neurogenesis.

Genetics of body weight in the LXS recombinant inbred mouse strains

This is the first phenotypic analysis of 75 new recombinant inbred (RI) strains derived from ILS and ISS progenitors. We analyzed body weight in two independent cohorts of female mice at various ages and in males at 60 days. Body weight is a complex trait which has been mapped in numerous crosses in rodents. The LXS RI strains displayed a large range of weights, transgressing those of the inbred progenitors, supporting the utility of this large panel for mapping traits not selected in the progenitors. Numerous QTLs for body weight mapped in single- and multilocus scans. We assessed replication between these and previously reported QTLs based on overlapping confidence intervals of published QTLs for body weight at 60 days and used meta-analyses to determine combined p values for three QTL regions located on Chromosomes 4, 5, and 11. Strain distribution patterns of microsatellite marker genotypes, weight, and other phenotypes are available on WebQTL (http://www.webqtl.org/search.html ) and allow genetic mapping of any heritable quantitative phenotype measured in these strains. We report one such analysis, correlating brain and body weights. Large reference panels of RI strains, such as the LXS, are invaluable for identifying genetic correlations, GXE (Gene X Environment) interactions, and replicating previously identified QTLs.

Methods for detection and characterization of anti-nuclear antibodies

The detection and characterization of ANA can actually be done in most clinical laboratories using commercially available ready-to-use reagents. However when chosing a kit great care should be taken in the selection of standardized preparations previously tested with a large reference panel of sera, including a maximum of ANA patterns as well as negative control sera, to be sure that every specificity will be correctly detected.