Transmission Disequilibrium of Small CNVs in Simplex Autism

We searched for disruptive, genic rare copy-number variants (CNVs) among 411 families affected by sporadic autism spectrum disorder (ASD) from the Simons Simplex Collection by using available exome sequence data and CoNIFER (Copy Number Inference from Exome Reads). Compared to high-density SNP microarrays, our approach yielded ∼2× more smaller genic rare CNVs. We found that affected probands inherited more CNVs than did their siblings (453 versus 394, p = 0.004; odds ratio [OR] = 1.19) and that the probands’ CNVs affected more genes (921 versus 726, p = 0.02; OR = 1.30). These smaller CNVs (median size 18 kb) were transmitted preferentially from the mother (136 maternal versus 100 paternal, p = 0.02), although this bias occurred irrespective of affected status. The excess burden of inherited CNVs among probands was driven primarily by sibling pairs with discordant social-behavior phenotypes (p < 0.0002, measured by Social Responsiveness Scale [SRS] score), which contrasts with families where the phenotypes were more closely matched or less extreme (p > 0.5). Finally, we found enrichment of brain-expressed genes unique to probands, especially in the SRS-discordant group (p = 0.0035). In a combined model, our inherited CNVs, de novo CNVs, and de novo single-nucleotide variants all independently contributed to the risk of autism (p < 0.05). Taken together, these results suggest that small transmitted rare CNVs play a role in the etiology of simplex autism. Importantly, the small size of these variants aids in the identification of specific genes as additional risk factors associated with ASD.     

A Generalization of the Transmission/Disequilibrium Test for Uncertain-Haplotype Transmission

A new transmission/disequilibrium-test statistic is proposed for situations in which transmission is uncertain. Such situations arise when transmission of a multilocus marker haplotype is considered, since haplotype phase is often unknown in a substantial number of instances. Even for single-locus markers, transmission is uncertain if one or both parents are missing. In both these situations, uncertainty may be reduced by the typing of further siblings, whose disease status may be unaffected or unknown. The proposed test is a score test based on a partial score function that omits the terms most influenced by hidden population stratification.     

Handling Missing Data in Transmission Disequilibrium Test in Nuclear Families with One Affected Offspring

The Transmission Disequilibrium Test (TDT) compares frequencies of transmission of two alleles from heterozygote parents to an affected offspring. This test requires all genotypes to be known from all members of the nuclear families. However, obtaining all genotypes in a study might not be possible for some families, in which case, a data set results in missing genotypes. There are many techniques of handling missing genotypes in parents but only a few in offspring. The robust TDT (rTDT) is one of the methods that handles missing genotypes for all members of nuclear families [with one affected offspring]. Even though all family members can be imputed, the rTDT is a conservative test with low power. We propose a new method, Mendelian Inheritance TDT (MITDT-ONE), that controls type I error and has high power. The MITDT-ONE uses Mendelian Inheritance properties, and takes population frequencies of the disease allele and marker allele into account in the rTDT method. One of the advantages of using the MITDT-ONE is that the MITDT-ONE can identify additional significant genes that are not found by the rTDT. We demonstrate the performances of both tests along with Sib-TDT (S-TDT) in Monte Carlo simulation studies. Moreover, we apply our method to the type 1 diabetes data from the Warren families in the United Kingdom to identify significant genes that are related to type 1 diabetes.     

The Detection of Linkage Disequilibrium in Molecular Sequence Data

Studies of genetic variation in natural populations at the sequence level usually show that most polymorphic sites are very asymmetrical in allele frequencies, with the rarer allele at a site near fixation. When the rarer allele at a site is present only a few times in the sample, say below five representatives, it becomes very difficult to detect linkage disequilibrium between sites from tests of association. This is a consequence of the numerical properties of even the most powerful test of association, Fisher's exact test. Sites with fewer than five representatives in the sample should be excluded from association tests, but this generally leaves few site pairs eligible for testing. A test for overall linkage disequilibrium, based on the sign of the observed linkage disequilibria, is derived which can use all the data. It is shown that more power can be achieved by increasing the length of sequence determined than by increasing the number of genomes sampled for the same total work.     

A Transmission/Disequilibrium Test That Allows for Genotyping Errors in the Analysis of Single-Nucleotide Polymorphism Data

The present study assesses the effects of genotyping errors on the type I error rate of a particular transmission/disequilibrium test (TDT(std)), which assumes that data are errorless, and introduces a new transmission/disequilibrium test (TDT(ae)) that allows for random genotyping errors. We evaluate the type I error rate and power of the TDT(ae) under a variety of simulations and perform a power comparison between the TDT(std) and the TDT(ae), for errorless data. Both the TDT(std) and the TDT(ae) statistics are computed as two times a log-likelihood difference, and both are asymptotically distributed as chi(2) with 1 df. Genotype data for trios are simulated under a null hypothesis and under an alternative (power) hypothesis. For each simulation, errors are introduced randomly via a computer algorithm with different probabilities (called "allelic error rates"). The TDT(std) statistic is computed on all trios that show Mendelian consistency, whereas the TDT(ae) statistic is computed on all trios. The results indicate that TDT(std) shows a significant increase in type I error when applied to data in which inconsistent trios are removed. This type I error increases both with an increase in sample size and with an increase in the allelic error rates. TDT(ae) always maintains correct type I error rates for the simulations considered. Factors affecting the power of the TDT(ae) are discussed. Finally, the power of TDT(std) is at least that of TDT(ae) for simulations with errorless data. Because data are rarely error free, we recommend that researchers use methods, such as the TDT(ae), that allow for errors in genotype data.     

Identification of Small Exonic CNV from Whole-Exome Sequence Data and Application to Autism Spectrum Disorder

Copy number variation (CNV) is an important determinant of human diversity and plays important roles in susceptibility to disease. Most studies of CNV carried out to date have made use of chromosome microarray and have had a lower size limit for detection of about 30 kilobases (kb). With the emergence of whole-exome sequencing studies, we asked whether such data could be used to reliably call rare exonic CNV in the size range of 1–30 kilobases (kb), making use of the eXome Hidden Markov Model (XHMM) program. By using both transmission information and validation by molecular methods, we confirmed that small CNV encompassing as few as three exons can be reliably called from whole-exome data. We applied this approach to an autism case-control sample (n = 811, mean per-target read depth = 161) and observed a significant increase in the burden of rare (MAF ≤1%) 1–30 kb CNV, 1–30 kb deletions, and 1–10 kb deletions in ASD. CNV in the 1–30 kb range frequently hit just a single gene, and we were therefore able to carry out enrichment and pathway analyses, where we observed enrichment for disruption of genes in cytoskeletal and autophagy pathways in ASD. In summary, our results showed that XHMM provided an effective means to assess small exonic CNV from whole-exome data, indicated that rare 1–30 kb exonic deletions could contribute to risk in up to 7% of individuals with ASD, and implicated a candidate pathway in developmental delay syndromes.     

亲代传递的独立性与传递不平衡的边缘齐性检验

传递不平衡检验其实质是边缘齐性检验,需同时考察家系中两个亲代向受累子代的传递情形．但是,其零假设成立时,同一家系中父亲的传递与母亲的传递并不一定相互独立．本文探讨了父母传递的独立性条件对检验统计量的影响．结果表明,即使父母传递不独立时,边缘齐性检验仍然适合．     

A Discordant-Sibship Test for Disequilibrium and Linkage: No Need for Parental Data

The sibship disequilibrium test (SDT) is designed to detect both linkage in the presence of association and association in the presence of linkage (linkage disequilibrium). The test does not require parental data but requires discordant sibships with at least one affected and one unaffected sibling. The SDT has many desirable properties: it uses all the siblings in the sibship; it remains valid if there are misclassifications of the affectation status; it does not detect spurious associations due to population stratification; asymptotically it has a chi2 distribution under the null hypothesis; and exact P values can be easily computed for a biallelic marker. We show how to extend the SDT to markers with multiple alleles and how to combine families with parents and data from discordant sibships. We discuss the power of the test by presenting sample-size calculations involving a complex disease model, and we present formulas for the asymptotic relative efficiency (which is approximately the ratio of sample sizes) between SDT and the transmission/disequilibrium test (TDT) for special family structures. For sib pairs, we compare the SDT to a test proposed both by Curtis and, independently, by Spielman and Ewens. We show that, for discordant sib pairs, the SDT has good power for testing linkage disequilibrium relative both to Curtis''s tests and to the TDT using trios comprising an affected sib and its parents. With additional sibs, we show that the SDT can be more powerful than the TDT for testing linkage disequilibrium, especially for disease prevalence >.3.     

Selection, Hitchhiking and Disequilibrium Analysis at Three Linked Loci with Application to Hla Data

The HLA system has been extensively studied from an evolutionary perspective. Although it is clear that selection has acted on the genes in the HLA complex, the nature of this selection has yet to be fully clarified. A study of constrained disequilibrium values is presented that is applicable to HLA and other less polymorphic systems with three or more linked loci, with the purpose of identifying selection events. The method uses the fact that three locus systems impose additional constraints on the range of possible disequilibrium values for any pair of loci. We have thus examined the behavior of the normalized pairwise disequilibrium measures using two locus (D'), and also three locus (D"), constraints on pairwise disequilibria in a three locus system when one of the three loci is under positive selection. The difference between these measures, delta = magnitude of D' - magnitude of D", has a distribution for the two unselected loci differing from that for the selected locus with either of the unselected loci (the hallmark is a high positive value of delta for the two unselected loci). An examination of genetic drift indicates that positive delta values are unlikely to be found in human populations in the absence of selection when recombination is greater than about 0.1%. This measure can thus provide insight into which allele of several linked loci might have been subject to selection. Application of this method to HLA haplotypes from a large French population study (Provinces Francaise) identifies selected alleles on particular haplotypes. Application of a complementary method, disequilibrium pattern analysis also confirms the action of selection on these haplotypes.     

The Use of Family Relationships and Linkage Disequilibrium to Impute Phase and Missing Genotypes in Up to Whole-Genome Sequence Density Genotypic Data

A novel method, called linkage disequilibrium multilocus iterative peeling (LDMIP), for the imputation of phase and missing genotypes is developed. LDMIP performs an iterative peeling step for every locus, which accounts for the family data, and uses a forward–backward algorithm to accumulate information across loci. Marker similarity between haplotype pairs is used to impute possible missing genotypes and phases, which relies on the linkage disequilibrium between closely linked markers. After this imputation step, the combined iterative peeling/forward–backward algorithm is applied again, until convergence. The calculations per iteration scale linearly with number of markers and number of individuals in the pedigree, which makes LDMIP well suited to large numbers of markers and/or large numbers of individuals. Per iteration calculations scale quadratically with the number of alleles, which implies biallelic markers are preferred. In a situation with up to 15% randomly missing genotypes, the error rate of the imputed genotypes was <1% and ∼99% of the missing genotypes were imputed. In another example, LDMIP was used to impute whole-genome sequence data consisting of 17,321 SNPs on a chromosome. Imputation of the sequence was based on the information of 20 (re)sequenced founder individuals and genotyping their descendants for a panel of 3000 SNPs. The error rate of the imputed SNP genotypes was 10%. However, if the parents of these 20 founders are also sequenced, >99% of missing genotypes are imputed correctly.HIGH-DENSITY SNP arrays are currently available for an increasing number of species. QTL mapping, marker-assisted selection (MAS), and other genetic analyses often require or benefit greatly from imputing missing genotypes and from knowing the phase of the SNP genotypes. Although many statistical methods have been developed for phasing in the literature, new methods are needed because of the spectacular improvements in the efficiency of high-throughput genotyping. The older phasing methods often use linkage information (e.g., Sobel and Lange 1996). However, due to the use of increasingly dense marker maps, the use of linkage disequilibrium (LD) information has become increasingly attractive. Moreover, the linkage analysis methods became computationally intractable as the number of SNPs and/or the number of individuals increased. Phasing methods that rely solely on LD, such as Fastphase (Scheet and Stephens 2006), tend to mistakenly introduce recombinations when applied to genotypes covering long genetic distances (Kong et al. 2008). Linkage information and use of LD are not fundamentally different—use of LD may be thought of as linkage analysis based on common ancestors that occur before the known pedigree. Kong et al. suggested a new approach called “long-range phasing (LRP)” that relies on detecting identical-by-descent (IBD) haplotypes in different individuals and can phase large numbers of SNPs. For situations where the pedigree back to the common ancestor is available this may be considered linkage analysis but it can also be used without a pedigree and would use LD information. Long-range phasing may be seen as a set of sensible, heuristic rules to determine the phase using linkage analysis information, without attempting to extract all information in an optimal way. The latter is typical for modern linkage-based phasing methods: they are less concerned about optimally using all information, since there is a surplus of information, and are more concerned with handling high SNP densities over large genetic distances and for many genotyped individuals. The surplus of information is especially large if whole-genome sequence data are used. We define here whole-genome (re)sequence data as all the SNPs in the genome, which ignores information from copy number variation and other non-SNP genetic polymorphisms.Here we describe a new phasing method, called linkage disequilibrium multilocus iterative peeling (LDMIP), which combines linkage and linkage disequilibrium information and can handle tens of thousands of SNPs per chromosome and thousands of individuals. It was initially developed for the common situation where many individuals at the top of the pedigree are ungenotyped, but the method is general and can be applied in other situations. For instance, we apply it here to the situation where a few individuals have very dense genetic information (e.g., from genome sequencing) while most individuals have sparser or no genotype data. To make optimum use of the known pedigree, family information is used quite extensively in an iterative peeling approach (Elston and Stewart 1971; Van Arendonk et al. 1989; Janss et al. 1995). The use of LD information crudely follows the approach of Meuwissen and Goddard (2001, 2007).     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.     

Imputation of Exome Sequence Variants into Population- Based Samples and Blood-Cell-Trait-Associated Loci in African Americans: NHLBI GO Exome Sequencing Project

Researchers have successfully applied exome sequencing to discover causal variants in selected individuals with familial, highly penetrant disorders. We demonstrate the utility of exome sequencing followed by imputation for discovering low-frequency variants associated with complex quantitative traits. We performed exome sequencing in a reference panel of 761 African Americans and then imputed newly discovered variants into a larger sample of more than 13,000 African Americans for association testing with the blood cell traits hemoglobin, hematocrit, white blood count, and platelet count. First, we illustrate the feasibility of our approach by demonstrating genome-wide-significant associations for variants that are not covered by conventional genotyping arrays; for example, one such association is that between higher platelet count and an MPL c.117G>T (p.Lys39Asn) variant encoding a p.Lys39Asn amino acid substitution of the thrombpoietin receptor gene (p = 1.5 × 10⁻¹¹). Second, we identified an association between missense variants of LCT and higher white blood count (p = 4 × 10⁻¹³). Third, we identified low-frequency coding variants that might account for allelic heterogeneity at several known blood cell-associated loci: MPL c.754T>C (p.Tyr252His) was associated with higher platelet count; CD36 c.975T>G (p.Tyr325^∗) was associated with lower platelet count; and several missense variants at the α-globin gene locus were associated with lower hemoglobin. By identifying low-frequency missense variants associated with blood cell traits not previously reported by genome-wide association studies, we establish that exome sequencing followed by imputation is a powerful approach to dissecting complex, genetically heterogeneous traits in large population-based studies.     

Application of the Character Compatibility Approach to Generalized Molecular Sequence Data: Branching Order of the Proteobacterial Subdivisions

The character compatibility approach, which removes all homoplasic characters and involves finding the largest clique of compatible characters in a dataset, in principle, provides a powerful means for obtaining correct topology in difficult to resolve cases. However, the usefulness of this approach to generalized molecular sequence data for phylogeny determination has not been studied in the past. We have used this approach to determine the topology of 23 proteobacterial species (6 each of α-, β- and γ-, 3 δ-, and 2 ε-proteobacteria) using sequence data for 10 conserved proteins (Hsp60, Hsp70, EF-Tu, EF-G, alanyl-tRNA synthetase, RecA, GyrA, GyrB, RpoB and RpoC). All sites in the sequence alignments of these proteins where only two amino acids were found, with each amino acid present in at least two species, were selected. Mutual compatibility determination on these binary state sites was carried out by two means. In one case, all of these sites were combined into a large dataset (Set A; 957 characters) prior to compatibility analysis. In the second case, compatibility analysis was carried out on characters from individual proteins and all compatible sites were combined into a large dataset (Set B; 398 characters) for further studies. Upon compatibility analyses, the largest cliques that were obtained from Sets A and B consisted of 337 and 323 compatible characters, respectively. In these cliques, all proteobacterial subgroups were clearly distinguished and branching orders of most of the species were also resolved. The ε-proteobacteria exhibited the earliest branching, whereas the β- and γ-subgroups were found to have emerged last. The relative placement of the α- and δ-subgroups, however, was not resolved. The topology of these species was also determined based on 16S rRNA sequences and a concatenated dataset of sequences for all 10 proteins by means of neighbor-joining, maximum likelihood, and maximum parsimony methods. In the protein trees, all proteobacterial groups were reliably resolved and they branched in the following order: (ε(δ(α(β,γ)))). However, in the rRNA trees, the γ- and β-subgroups exhibited polyphyletic branching and many internal nodes were not resolved. These results indicate that the character compatibility analysis using generalized molecular sequence data provides a powerful means for evolutionary studies. Based on molecular sequences, it should be possible to obtain very large datasets of compatible characters that should prove very helpful in clarifying difficult to resolve phylogenetic relationships. [Reviewing Editor: Dr. Yves Van de Peer]     

LDx: Estimation of Linkage Disequilibrium from High-Throughput Pooled Resequencing Data

High-throughput pooled resequencing offers significant potential for whole genome population sequencing. However, its main drawback is the loss of haplotype information. In order to regain some of this information, we present LDx, a computational tool for estimating linkage disequilibrium (LD) from pooled resequencing data. LDx uses an approximate maximum likelihood approach to estimate LD (r²) between pairs of SNPs that can be observed within and among single reads. LDx also reports r² estimates derived solely from observed genotype counts. We demonstrate that the LDx estimates are highly correlated with r² estimated from individually resequenced strains. We discuss the performance of LDx using more stringent quality conditions and infer via simulation the degree to which performance can improve based on read depth. Finally we demonstrate two possible uses of LDx with real and simulated pooled resequencing data. First, we use LDx to infer genomewide patterns of decay of LD with physical distance in D. melanogaster population resequencing data. Second, we demonstrate that r² estimates from LDx are capable of distinguishing alternative demographic models representing plausible demographic histories of D. melanogaster.     

Genomewide Transmission/Disequilibrium Testing-Consideration of the Genotypic Relative Risks at Disease Loci

Genomewide association studies are set to become the tool of the future for detection of small-effect genes in complex diseases. It will therefore be necessary to calculate sufficient sample sizes with which to perform them. In this paper I illustrate how to calculate the required number of families for general genotypic relative risks (GRRs). I show the superior sensitivity of the genomewide association study over the standard genomewide affected-sib-pair linkage analysis, for a range of different underlying GRR patterns. I also illustrate the extent of change in the sample sizes that is necessary for a genomewide association analysis depending on the pattern of the GRRs at the disease locus. In many cases, the comparative numbers of families required under different genetic mechanisms vary by several orders of magnitude. These sometimes dramatic differences have important implications for the determination of the size of the collection of samples prior to analysis and for the types of effects that are likely--and unlikely--to be detected by such an analysis.