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.     

Relationship Estimation from Whole-Genome Sequence Data

The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1^st through 6^th degree relationships, and 55% of 9^th through 11^th degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1^st through 9^th degree relationships from whole-genome sequence data.     

Detecting Identity by Descent and Homozygosity Mapping in Whole-Exome Sequencing Data

The detection of genetic segments of Identical by Descent (IBD) in Genome-Wide Association Studies has proven successful in pinpointing genetic relatedness between reportedly unrelated individuals and leveraging such regions to shortlist candidate genes. These techniques depend on high-density genotyping arrays and their effectiveness in diverse sequence data is largely unknown. Due to decreasing costs and increasing effectiveness of high throughput techniques for whole-exome sequencing, an influx of exome sequencing data has become available. Studies using exomes and IBD-detection methods within known pedigrees have shown that IBD can be useful in finding hidden genetic candidates where known relatives are available. We set out to examine the viability of using IBD-detection in whole exome sequencing data in population-wide studies. In doing so, we extend GERMLINE, a method to detect IBD from exome sequencing data by finding small slices of matching alleles between pairs of individuals and extending them into full IBD segments. This algorithm allows for efficient population-wide detection in dense data. We apply this algorithm to a cohort of Crohn''s Disease cases where whole-exome and GWAS array data is available. We confirm that GWAS-based detected segments are highly accurate and predictive of underlying shared variation. Where segments inferred from GWAS are expected to be of high accuracy, we compare exome-based detection accuracy of multiple detection strategies. We find detection accuracy to be prohibitively low in all assessments, both in terms of segment sensitivity and specificity. Even after isolating relatively long segments beyond 10cM, exome-based detection continued to offer poor specificity/sensitivity tradeoffs. We hypothesize that the variable coverage and platform biases of exome capture account for this decreased accuracy and look toward whole genome sequencing data as a higher quality source for detecting population-wide IBD.     

MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue-residue contacts

Background

Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.

Results

We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.

Conclusions

Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.     

HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data

Identity by descent (IBD) can be reliably detected for long shared DNA segments, which are found in related individuals. However, many studies contain cohorts of unrelated individuals that share only short IBD segments. New sequencing technologies facilitate identification of short IBD segments through rare variants, which convey more information on IBD than common variants. Current IBD detection methods, however, are not designed to use rare variants for the detection of short IBD segments. Short IBD segments reveal genetic structures at high resolution. Therefore, they can help to improve imputation and phasing, to increase genotyping accuracy for low-coverage sequencing and to increase the power of association studies. Since short IBD segments are further assumed to be old, they can shed light on the evolutionary history of humans. We propose HapFABIA, a computational method that applies biclustering to identify very short IBD segments characterized by rare variants. HapFABIA is designed to detect short IBD segments in genotype data that were obtained from next-generation sequencing, but can also be applied to DNA microarray data. Especially in next-generation sequencing data, HapFABIA exploits rare variants for IBD detection. HapFABIA significantly outperformed competing algorithms at detecting short IBD segments on artificial and simulated data with rare variants. HapFABIA identified 160 588 different short IBD segments characterized by rare variants with a median length of 23 kb (mean 24 kb) in data for chromosome 1 of the 1000 Genomes Project. These short IBD segments contain 752 000 single nucleotide variants (SNVs), which account for 39% of the rare variants and 23.5% of all variants. The vast majority—152 000 IBD segments—are shared by Africans, while only 19 000 and 11 000 are shared by Europeans and Asians, respectively. IBD segments that match the Denisova or the Neandertal genome are found significantly more often in Asians and Europeans but also, in some cases exclusively, in Africans. The lengths of IBD segments and their sharing between continental populations indicate that many short IBD segments from chromosome 1 existed before humans migrated out of Africa. Thus, rare variants that tag these short IBD segments predate human migration from Africa. The software package HapFABIA is available from Bioconductor. All data sets, result files and programs for data simulation, preprocessing and evaluation are supplied at http://www.bioinf.jku.at/research/short-IBD.     

Detecting Identity by Descent and Estimating Genotype Error Rates in Sequence Data

Existing methods for identity by descent (IBD) segment detection were designed for SNP array data, not sequence data. Sequence data have a much higher density of genetic variants and a different allele frequency distribution, and can have higher genotype error rates. Consequently, best practices for IBD detection in SNP array data do not necessarily carry over to sequence data. We present a method, IBDseq, for detecting IBD segments in sequence data and a method, SEQERR, for estimating genotype error rates at low-frequency variants by using detected IBD. The IBDseq method estimates probabilities of genotypes observed with error for each pair of individuals under IBD and non-IBD models. The ratio of estimated probabilities under the two models gives a LOD score for IBD. We evaluate several IBD detection methods that are fast enough for application to sequence data (IBDseq, Beagle Refined IBD, PLINK, and GERMLINE) under multiple parameter settings, and we show that IBDseq achieves high power and accuracy for IBD detection in sequence data. The SEQERR method estimates genotype error rates by comparing observed and expected rates of pairs of homozygote and heterozygote genotypes at low-frequency variants in IBD segments. We demonstrate the accuracy of SEQERR in simulated data, and we apply the method to estimate genotype error rates in sequence data from the UK10K and 1000 Genomes projects.     

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.     

Analysis of porcine MUC4 gene as a candidate gene for prolificacy QTL on SSC13 in an Iberian × Meishan F2 population

Background

Canine atopic dermatitis (AD) is a common, heritable, chronic allergic skin condition prevalent in the West Highland White Terrier (WHWT). In canine AD, environmental allergens trigger an inflammatory response causing visible skin lesions and chronic pruritus that can lead to secondary bacterial and yeast infections. The disorder shares many of the clinical and histopathological characteristics of human AD and represents an animal model of this disorder that could be used to further elucidate genetic causes of human AD. Microsatellite markers genotyped in families of WHWTs affected with AD were used to perform a genome-wide linkage study in order to isolate chromosomal regions associated with the disorder.

Results

Blood samples and health questionnaires were collected from 108 WHWTs spanning three families. A linkage simulation using these 108 dogs showed high power to detect a highly penetrant mutation. Ninety WHWTs were genotyped using markers from the Minimal Screening Set 2 (MSS-2). Two hundred and fifty six markers were informative and were used for linkage analysis. Using a LOD score of 2.7 as a significance threshold, no chromosomal regions were identified with significant linkage to AD. LOD scores greater than 1.0 were located in a 56 cM region of chromosome 7.

Conclusions

The study was unable to detect any chromosomal regions significantly linked to canine AD. This could be a result of factors such as environmental modification of phenotype, incorrect assignment of phenotype, a mutation of low penetrance, or incomplete genome coverage. A genome-wide SNP association study in a larger cohort of WHWTs may prove more successful by providing higher density coverage and higher statistical power.     

Evaluation of next generation sequencing platforms for population targeted sequencing studies

Background

Next generation sequencing (NGS) platforms are currently being utilized for targeted sequencing of candidate genes or genomic intervals to perform sequence-based association studies. To evaluate these platforms for this application, we analyzed human sequence generated by the Roche 454, Illumina GA, and the ABI SOLiD technologies for the same 260 kb in four individuals.

Results

Local sequence characteristics contribute to systematic variability in sequence coverage (>100-fold difference in per-base coverage), resulting in patterns for each NGS technology that are highly correlated between samples. A comparison of the base calls to 88 kb of overlapping ABI 3730xL Sanger sequence generated for the same samples showed that the NGS platforms all have high sensitivity, identifying >95% of variant sites. At high coverage, depth base calling errors are systematic, resulting from local sequence contexts; as the coverage is lowered additional 'random sampling' errors in base calling occur.

Conclusions

Our study provides important insights into systematic biases and data variability that need to be considered when utilizing NGS platforms for population targeted sequencing studies.     

Haplotype frequencies at the DRD2 locus in populations of the East European Plain

Background

A key to increasing the power of multilocus association tests is to reduce the number of degrees of freedom by suppressing noise from data. One of the difficulties is to decide how much noise to suppress. An often overlooked problem is that commonly used association tests based on genotype data cannot utilize the genetic information contained in spatial ordering of SNPs (see proof in the Appendix), which may prevent them from achieving higher power.

Results

We develop a score test based on wavelet transform with empirical Bayesian thresholding. Extensive simulation studies are carried out under various LD structures as well as using HapMap data from many different chromosomes for both qualitative and quantitative traits. Simulation results show that the proposed test automatically adjusts the level of noise suppression according to LD structures, and it is able to consistently achieve higher or similar powers than many commonly used association tests including the principle component regression method (PCReg).

Conclusion

The wavelet-based score test automatically suppresses the right amount of noise and uses the information contained in spatial ordering of SNPs to achieve higher power.     

Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies

Background

The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms.

Methodology

We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants.

Conclusion

We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20–99%.     

Sequencing and analysis of an Irish human genome

Background

Recent studies generating complete human sequences from Asian, African and European subgroups have revealed population-specific variation and disease susceptibility loci. Here, choosing a DNA sample from a population of interest due to its relative geographical isolation and genetic impact on further populations, we extend the above studies through the generation of 11-fold coverage of the first Irish human genome sequence.

Results

Using sequence data from a branch of the European ancestral tree as yet unsequenced, we identify variants that may be specific to this population. Through comparisons with HapMap and previous genetic association studies, we identified novel disease-associated variants, including a novel nonsense variant putatively associated with inflammatory bowel disease. We describe a novel method for improving SNP calling accuracy at low genome coverage using haplotype information. This analysis has implications for future re-sequencing studies and validates the imputation of Irish haplotypes using data from the current Human Genome Diversity Cell Line Panel (HGDP-CEPH). Finally, we identify gene duplication events as constituting significant targets of recent positive selection in the human lineage.

Conclusions

Our findings show that there remains utility in generating whole genome sequences to illustrate both general principles and reveal specific instances of human biology. With increasing access to low cost sequencing we would predict that even armed with the resources of a small research group a number of similar initiatives geared towards answering specific biological questions will emerge.     

Comprehensive comparison of three commercial human whole-exome capture platforms

Background

Exome sequencing, which allows the global analysis of protein coding sequences in the human genome, has become an effective and affordable approach to detecting causative genetic mutations in diseases. Currently, there are several commercial human exome capture platforms; however, the relative performances of these have not been characterized sufficiently to know which is best for a particular study.

Results

We comprehensively compared three platforms: NimbleGen's Sequence Capture Array and SeqCap EZ, and Agilent's SureSelect. We assessed their performance in a variety of ways, including number of genes covered and capture efficacy. Differences that may impact on the choice of platform were that Agilent SureSelect covered approximately 1,100 more genes, while NimbleGen provided better flanking sequence capture. Although all three platforms achieved similar capture specificity of targeted regions, the NimbleGen platforms showed better uniformity of coverage and greater genotype sensitivity at 30- to 100-fold sequencing depth. All three platforms showed similar power in exome SNP calling, including medically relevant SNPs. Compared with genotyping and whole-genome sequencing data, the three platforms achieved a similar accuracy of genotype assignment and SNP detection. Importantly, all three platforms showed similar levels of reproducibility, GC bias and reference allele bias.

Conclusions

We demonstrate key differences between the three platforms, particularly advantages of solutions over array capture and the importance of a large gene target set.     

A BAC-based integrated linkage map of the silkworm Bombyx mori

Background

In 2004, draft sequences of the model lepidopteran Bombyx mori were reported using whole-genome shotgun sequencing. Because of relatively shallow genome coverage, the silkworm genome remains fragmented, hampering annotation and comparative genome studies. For a more complete genome analysis, we developed extended scaffolds combining physical maps with improved genetic maps.

Results

We mapped 1,755 single nucleotide polymorphism (SNP) markers from bacterial artificial chromosome (BAC) end sequences onto 28 linkage groups using a recombining male backcross population, yielding an average inter-SNP distance of 0.81 cM (about 270 kilobases). We constructed 6,221 contigs by fingerprinting clones from three BAC libraries digested with different restriction enzymes, and assigned a total of 724 single copy genes to them by BLAST (basic local alignment search tool) search of the BAC end sequences and high-density BAC filter hybridization using expressed sequence tags as probes. We assigned 964 additional expressed sequence tags to linkage groups by restriction fragment length polymorphism analysis of a nonrecombining female backcross population. Altogether, 361.1 megabases of BAC contigs and singletons were integrated with a map containing 1,688 independent genes. A test of synteny using Oxford grid analysis with more than 500 silkworm genes revealed six versus 20 silkworm linkage groups containing eight or more orthologs of Apis versus Tribolium, respectively.

Conclusion

The integrated map contains approximately 10% of predicted silkworm genes and has an estimated 76% genome coverage by BACs. This provides a new resource for improved assembly of whole-genome shotgun data, gene annotation and positional cloning, and will serve as a platform for comparative genomics and gene discovery in Lepidoptera and other insects.     

Genome-wide analysis of the role of GlnR in Streptomyces venezuelae provides new insights into global nitrogen regulation in actinomycetes

Background

Knowledge of the origins, distribution, and inheritance of variation in the malaria parasite (Plasmodium falciparum) genome is crucial for understanding its evolution; however the 81% (A+T) genome poses challenges to high-throughput sequencing technologies. We explore the viability of the Roche 454 Genome Sequencer FLX (GS FLX) high throughput sequencing technology for both whole genome sequencing and fine-resolution characterization of genetic exchange in malaria parasites.

Results

We present a scheme to survey recombination in the haploid stage genomes of two sibling parasite clones, using whole genome pyrosequencing that includes a sliding window approach to predict recombination breakpoints. Whole genome shotgun (WGS) sequencing generated approximately 2 million reads, with an average read length of approximately 300 bp. De novo assembly using a combination of WGS and 3 kb paired end libraries resulted in contigs ≤ 34 kb. More than 8,000 of the 24,599 SNP markers identified between parents were genotyped in the progeny, resulting in a marker density of approximately 1 marker/3.3 kb and allowing for the detection of previously unrecognized crossovers (COs) and many non crossover (NCO) gene conversions throughout the genome.

Conclusions

By sequencing the 23 Mb genomes of two haploid progeny clones derived from a genetic cross at more than 30× coverage, we captured high resolution information on COs, NCOs and genetic variation within the progeny genomes. This study is the first to resequence progeny clones to examine fine structure of COs and NCOs in malaria parasites.     

Association studies using family pools of outcrossing crops based on allele-frequency estimates from DNA sequencing

Key message

We propose a method in which GBS data can be conveniently analyzed without calling genotypes.

Abstract

F2 families are frequently used in breeding of outcrossing species, for instance to obtain trait measurements on plots. We propose to perform association studies by obtaining a matching “family genotype” from sequencing a pooled sample of the family, and to directly use allele frequencies computed from sequence read-counts for mapping. We show that, under additivity assumptions, there is a linear relationship between the family phenotype and family allele frequency, and that a regression of family phenotype on family allele frequency will estimate twice the allele substitution effect at a locus. However, medium-to-low sequencing depth causes underestimation of the true allele substitution effect. An expression for this underestimation is derived for the case that parents are diploid, such that F2 families have up to four dosages of every allele. Using simulation studies, estimation of the allele effect from F2-family pools was verified and it was shown that the underestimation of the allele effect is correctly described. The optimal design for an association study when sequencing budget would be fixed is obtained using large sample size and lower sequence depth, and using higher SNP density (resulting in higher LD with causative mutations) and lower sequencing depth. Therefore, association studies using genotyping by sequencing are optimal and use low sequencing depth per sample. The developed framework for association studies using allele frequencies from sequencing can be modified for other types of family pools and is also directly applicable for association studies in polyploids.     

Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits

The Drosophila Synthetic Population Resource (DSPR) is a newly developed multifounder advanced intercross panel consisting of >1600 recombinant inbred lines (RILs) designed for the genetic dissection of complex traits. Here, we describe the inference of the underlying mosaic founder structure for the full set of RILs from a dense set of semicodominant restriction-site-associated DNA (RAD) markers and use simulations to explore how variation in marker density and sequencing coverage affects inference. For a given sequencing effort, marker density is more important than sequence coverage per marker in terms of the amount of genetic information we can infer. We also assessed the power of the DSPR by assigning genotypes at a hidden QTL to each RIL on the basis of the inferred founder state and simulating phenotypes for different experimental designs, different genetic architectures, different sample sizes, and QTL of varying effect sizes. We found the DSPR has both high power (e.g., 84% power to detect a 5% QTL) and high mapping resolution (e.g., ～1.5 cM for a 5% QTL).     

Comparison of solution-based exome capture methods for next generation sequencing

Background

Techniques enabling targeted re-sequencing of the protein coding sequences of the human genome on next generation sequencing instruments are of great interest. We conducted a systematic comparison of the solution-based exome capture kits provided by Agilent and Roche NimbleGen. A control DNA sample was captured with all four capture methods and prepared for Illumina GAII sequencing. Sequence data from additional samples prepared with the same protocols were also used in the comparison.

Results

We developed a bioinformatics pipeline for quality control, short read alignment, variant identification and annotation of the sequence data. In our analysis, a larger percentage of the high quality reads from the NimbleGen captures than from the Agilent captures aligned to the capture target regions. High GC content of the target sequence was associated with poor capture success in all exome enrichment methods. Comparison of mean allele balances for heterozygous variants indicated a tendency to have more reference bases than variant bases in the heterozygous variant positions within the target regions in all methods. There was virtually no difference in the genotype concordance compared to genotypes derived from SNP arrays. A minimum of 11× coverage was required to make a heterozygote genotype call with 99% accuracy when compared to common SNPs on genome-wide association arrays.

Conclusions

Libraries captured with NimbleGen kits aligned more accurately to the target regions. The updated NimbleGen kit most efficiently covered the exome with a minimum coverage of 20×, yet none of the kits captured all the Consensus Coding Sequence annotated exons.     

Inferring coancestry in population samples in the presence of linkage disequilibrium

In both pedigree linkage studies and in population-based association studies there has been much interest in the use of modern dense genetic marker data to infer segments of gene identity by descent (ibd) among individuals not known to be related, to increase power and resolution in localizing genes affecting complex traits. In this article, we present a hidden Markov model (HMM) for ibd among a set of chromosomes and describe methods and software for inference of ibd among the four chromosomes of pairs of individuals, using either phased (haplotypic) or unphased (genotypic) data. The model allows for missing data and typing error, but does not model linkage disequilibrium (LD), because fitting an accurate LD model requires large samples from well-studied populations. However, LD remains a major confounding factor, since LD is itself a reflection of coancestry at the population level. To study the impact of LD, we have developed a novel simulation approach to generate realistic dense marker data for the same set of markers but at varying levels of LD. Using this approach, we present results of a study of the impact of LD on the sensitivity and specificity of our HMM model in estimating segments of ibd among sets of four chromosomes and between genotype pairs. We show that, despite not incorporating LD, our model has been quite successful in detecting segments as small as 10(6) bp (1 Mpb); we present also comparisons with fastIBD which uses an LD model in estimating ibd.