Brian L. Browning

Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering

Whole-genome association studies present many new statistical and computational challenges due to the large quantity of data obtained. One of these challenges is haplotype inference; methods for haplotype inference designed for small data sets from candidate-gene studies do not scale well to the large number of individuals genotyped in whole-genome association studies. We present a new method and software for inference of haplotype phase and missing data that can accurately phase data from whole-genome association studies, and we present the first comparison of haplotype-inference methods for real and simulated data sets with thousands of genotyped individuals. We find that our method outperforms existing methods in terms of both speed and accuracy for large data sets with thousands of individuals and densely spaced genetic markers, and we use our method to phase a real data set of 3,002 individuals genotyped for 490,032 markers in 3.1 days of computing time, with 99% of masked alleles imputed correctly. Our method is implemented in the Beagle software package, which is freely available.

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.

A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay

We report the identification of a recurrent, 520-kb 16p12.1 microdeletion associated with childhood developmental delay. The microdeletion was detected in 20 of 11,873 cases compared with 2 of 8,540 controls (P = 0.0009, OR = 7.2) and replicated in a second series of 22 of 9,254 cases compared with 6 of 6,299 controls (P = 0.028, OR = 2.5). Most deletions were inherited, with carrier parents likely to manifest neuropsychiatric phenotypes compared to non-carrier parents (P = 0.037, OR = 6). Probands were more likely to carry an additional large copy-number variant when compared to matched controls (10 of 42 cases, P = 5.7 × 10−5, OR = 6.6). The clinical features of individuals with two mutations were distinct from and/or more severe than those of individuals carrying only the co-occurring mutation. Our data support a two-hit model in which the 16p12.1 microdeletion both predisposes to neuropsychiatric phenotypes as a single event and exacerbates neurodevelopmental phenotypes in association with other large deletions or duplications. Analysis of other microdeletions with variable expressivity indicates that this two-hit model might be more generally applicable to neuropsychiatric disease.

Haplotype phasing: existing methods and new developments

Determination of haplotype phase is becoming increasingly important as we enter the era of large-scale sequencing because many of its applications, such as imputing low-frequency variants and characterizing the relationship between genetic variation and disease susceptibility, are particularly relevant to sequence data. Haplotype phase can be generated through laboratory-based experimental methods, or it can be estimated using computational approaches. We assess the haplotype phasing methods that are available, focusing in particular on statistical methods, and we discuss the practical aspects of their application. We also describe recent developments that may transform this field, particularly the use of identity-by-descent for computational phasing.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci

To perform a 1‐stage meta‐analysis of genome‐wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci.

A Fast, Powerful Method for Detecting Identity by Descent

We present a method, fastIBD, for finding tracts of identity by descent (IBD) between pairs of individuals. FastIBD can be applied to thousands of samples across genome-wide SNP data and is significantly more powerful for finding short tracts of IBD than existing methods for finding IBD tracts in such data. We show that fastIBD can detect facets of population structure that are not revealed by other methods. In the Wellcome Trust Case Control Consortium bipolar disorder case-control data, we find a genome-wide excess of IBD in case-case pairs of individuals compared to control-control pairs. We show that this excess can be explained by the geographical clustering of cases. We also show that it is possible to use fastIBD to generate highly accurate estimates of genome-wide IBD sharing between pairs of distant relatives. This is useful for estimation of relationship and for adjusting for relatedness in association studies. FastIBD is incorporated in the freely available Beagle software package.

Genetic loci associated with plasma phospholipid N-3 fatty acids: A Meta-Analysis of Genome-Wide association studies from the charge consortium

Long-chain n-3 polyunsaturated fatty acids (PUFAs) can derive from diet or from α-linolenic acid (ALA) by elongation and desaturation. We investigated the association of common genetic variation with plasma phospholipid levels of the four major n-3 PUFAs by performing genome-wide association studies in five population-based cohorts comprising 8,866 subjects of European ancestry. Minor alleles of SNPs in FADS1 and FADS2 (desaturases) were associated with higher levels of ALA (p = 3×10−64) and lower levels of eicosapentaenoic acid (EPA, p = 5×10−58) and docosapentaenoic acid (DPA, p = 4×10−154). Minor alleles of SNPs in ELOVL2 (elongase) were associated with higher EPA (p = 2×10−12) and DPA (p = 1×10−43) and lower docosahexaenoic acid (DHA, p = 1×10−15). In addition to genes in the n-3 pathway, we identified a novel association of DPA with several SNPs in GCKR (glucokinase regulator, p = 1×10−8). We observed a weaker association between ALA and EPA among carriers of the minor allele of a representative SNP in FADS2 (rs1535), suggesting a lower rate of ALA-to-EPA conversion in these subjects. In samples of African, Chinese, and Hispanic ancestry, associations of n-3 PUFAs were similar with a representative SNP in FADS1 but less consistent with a representative SNP in ELOVL2. Our findings show that common variation in n-3 metabolic pathway genes and in GCKR influences plasma phospholipid levels of n-3 PUFAs in populations of European ancestry and, for FADS1, in other ancestries.

Improving the Accuracy and Efficiency of Identity by Descent Detection in Population Data

Segments of indentity-by-descent (IBD) detected from high-density genetic data are useful for many applications, including long-range phase determination, phasing family data, imputation, IBD mapping, and heritability analysis in founder populations. We present Refined IBD, a new method for IBD segment detection. Refined IBD achieves both computational efficiency and highly accurate IBD segment reporting by searching for IBD in two steps. The first step (identification) uses the GERMLINE algorithm to find shared haplotypes exceeding a length threshold. The second step (refinement) evaluates candidate segments with a probabilistic approach to assess the evidence for IBD. Like GERMLINE, Refined IBD allows for IBD reporting on a haplotype level, which facilitates determination of multi-individual IBD and allows for haplotype-based downstream analyses. To investigate the properties of Refined IBD, we simulate SNP data from a model with recent superexponential population growth that is designed to match United Kingdom data. The simulation results show that Refined IBD achieves a better power/accuracy profile than fastIBD or GERMLINE. We find that a single run of Refined IBD achieves greater power than 10 runs of fastIBD. We also apply Refined IBD to SNP data for samples from the United Kingdom and from Northern Finland and describe the IBD sharing in these data sets. Refined IBD is powerful, highly accurate, and easy to use and is implemented in Beagle version 4.

High-resolution detection of identity by descent in unrelated individuals.

Detection of recent identity by descent (IBD) in population samples is important for population-based linkage mapping and for highly accurate genotype imputation and haplotype-phase inference. We present a method for detection of recent IBD in population samples. Our method accounts for linkage disequilibrium between SNPs to enable full use of high-density SNP data. We find that our method can detect segments of a length of 2 cM with moderate power and negligible false discovery rate in Illumina 550K data in Northwestern Europeans. We compare our method with GERMLINE and PLINK, and we show that our method has a level of resolution that is significantly better than these existing methods, thus extending the usefulness of recent IBD in analysis of high-density SNP data. We survey four genomic regions in a sample of UK individuals of European descent and find that on average, at a given location, our method detects IBD in 2.7 per 10,000 pairs of individuals in Illumina 550K data. We also present methodology and results for detection of homozygosity by descent (HBD) and survey the whole genome in a sample of 1373 UK individuals of European descent. We detect HBD in 4.7 individuals per 10,000 on average at a given location. Our methodology is implemented in the freely available BEAGLE software package.

Simultaneous Genotype Calling and Haplotype Phasing Improves Genotype Accuracy and Reduces False-Positive Associations for Genome-wide Association Studies

We present a novel method for simultaneous genotype calling and haplotype-phase inference. Our method employs the computationally efficient BEAGLE haplotype-frequency model, which can be applied to large-scale studies with millions of markers and thousands of samples. We compare genotype calls made with our method to genotype calls made with the BIRDSEED, CHIAMO, GenCall, and ILLUMINUS genotype-calling methods, using genotype data from the Illumina 550K and Affymetrix 500K arrays. We show that our method has higher genotype-call accuracy and yields fewer uncalled genotypes than competing methods. We perform single-marker analysis of data from the Wellcome Trust Case Control Consortium bipolar disorder and type 2 diabetes studies. For bipolar disorder, the genotype calls in the original study yield 25 markers with apparent false-positive association with bipolar disorder at a p < 10(-7) significance level, whereas genotype calls made with our method yield no associated markers at this significance threshold. Conversely, for markers with replicated association with type 2 diabetes, there is good concordance between genotype calls used in the original study and calls made by our method. Results from single-marker and haplotypic analysis of our methods genotype calls for the bipolar disorder study indicate that our method is highly effective at eliminating genotyping artifacts that cause false-positive associations in genome-wide association studies. Our new genotype-calling methods are implemented in the BEAGLE and BEAGLECALL software packages.