Parent-offspring Trios Research Articles

Rare Variants in NR2F2 Cause Congenital Heart Defects in Humans

Congenital heart defects (CHDs) are the most common birth defect worldwide and are a leading cause of neonatal mortality. Nonsyndromic atrioventricular septal defects (AVSDs) are an important subtype of CHDs for which the genetic architecture is poorly understood. We performed exome sequencing in 13 parent-offspring trios and 112 unrelated individuals with nonsyndromic AVSDs and identified five rare missense variants (two of which arose de novo) in the highly conserved gene NR2F2, a very significant enrichment (p = 7.7 × 10(-7)) compared to 5,194 control subjects. We identified three additional CHD-affected families with other variants in NR2F2 including a de novo balanced chromosomal translocation, a de novo substitution disrupting a splice donor site, and a 3 bp duplication that cosegregated in a multiplex family. NR2F2 encodes a pleiotropic developmental transcription factor, and decreased dosage of NR2F2 in mice has been shown to result in abnormal development of atrioventricular septa. Via luciferase assays, we showed that all six coding sequence variants observed in individuals significantly alter the activity of NR2F2 on target promoters.

A gradient-boosting approach for filtering de novo mutations in parent–offspring trios

Whole-genome and -exome sequencing on parent-offspring trios is a powerful approach to identifying disease-associated genes by detecting de novo mutations in patients. Accurate detection of de novo mutations from sequencing data is a critical step in trio-based genetic studies. Existing bioinformatic approaches usually yield high error rates due to sequencing artifacts and alignment issues, which may either miss true de novo mutations or call too many false ones, making downstream validation and analysis difficult. In particular, current approaches have much worse specificity than sensitivity, and developing effective filters to discriminate genuine from spurious de novo mutations remains an unsolved challenge. In this article, we curated 59 sequence features in whole genome and exome alignment context which are considered to be relevant to discriminating true de novo mutations from artifacts, and then employed a machine-learning approach to classify candidates as true or false de novo mutations. Specifically, we built a classifier, named De Novo Mutation Filter (DNMFilter), using gradient boosting as the classification algorithm. We built the training set using experimentally validated true and false de novo mutations as well as collected false de novo mutations from an in-house large-scale exome-sequencing project. We evaluated DNMFilter's theoretical performance and investigated relative importance of different sequence features on the classification accuracy. Finally, we applied DNMFilter on our in-house whole exome trios and one CEU trio from the 1000 Genomes Project and found that DNMFilter could be coupled with commonly used de novo mutation detection approaches as an effective filtering approach to significantly reduce false discovery rate without sacrificing sensitivity. The software DNMFilter implemented using a combination of Java and R is freely available from the website at http://humangenome.duke.edu/software.

A novel method for detecting uniparental disomy from trio genotypes identifies a significant excess in children with developmental disorders.

Exome sequencing of parent-offspring trios is a popular strategy for identifying causative genetic variants in children with rare diseases. This method owes its strength to the leveraging of inheritance information, which facilitates de novo variant calling, inference of compound heterozygosity, and the identification of inheritance anomalies. Uniparental disomy describes the inheritance of a homologous chromosome pair from only one parent. This aberration is important to detect in genetic disease studies because it can result in imprinting disorders and recessive diseases. We have developed a software tool to detect uniparental disomy from child–mother–father genotype data that uses a binomial test to identify chromosomes with a significant burden of uniparentally inherited genotypes. This tool is the first to read VCF-formatted genotypes, to perform integrated copy number filtering, and to use a statistical test inherently robust for use in platforms of varying genotyping density and noise characteristics. Simulations demonstrated superior accuracy compared with previously developed approaches. We implemented the method on 1057 trios from the Deciphering Developmental Disorders project, a trio-based rare disease study, and detected six validated events, a significant enrichment compared with the population prevalence of UPD (1 in 3500), suggesting that most of these events are pathogenic. One of these events represents a known imprinting disorder, and exome analyses have identified rare homozygous candidate variants, mainly in the isodisomic regions of UPD chromosomes, which, among other variants, provide targets for further genetic and functional evaluation.

De NovoSCN8A Mutation Identified by Whole-Exome Sequencing in a Boy With Neonatal Epileptic Encephalopathy, Multiple Congenital Anomalies, and Movement Disorders

Epileptic encephalopathies represent a clinically and genetically heterogeneous group of disorders, majority of which are of unknown etiology. We used whole-exome sequencing of a parent-offspring trio to identify the cause of early infantile epileptic encephalopathy in a boy with neonatal seizures, movement disorders, and multiple congenital anomalies who died at the age of 17 months because of respiratory illness and identified a de novo heterozygous missense mutation (c.3979A>G; p.Ile1327Val) in SCN8A (voltage-gated sodium-channel type VIII alpha subunit) gene. The variant was confirmed in the proband with Sanger sequencing. Because the clinical phenotype associated with SCN8A mutations has previously been identified only in a few patients with or without epileptic seizures, these data together with our results suggest that mutations in SCN8A can lead to early infantile epileptic encephalopathy with a broad phenotypic spectrum. Additional investigations will be worthwhile to determine the prevalence and contribution of SCN8A mutations to epileptic encephalopathies.

A statistical variant calling approach from pedigree information and local haplotyping with phase informative reads

Variant calling from genome-wide sequencing data is essential for the analysis of disease-causing mutations and elucidation of disease mechanisms. However, variant calling in low coverage regions is difficult due to sequence read errors and mapping errors. Hence, variant calling approaches that are robust to low coverage data are demanded. We propose a new variant calling approach that considers pedigree information and haplotyping based on sequence reads spanning two or more heterozygous positions termed phase informative reads. In our approach, genotyping and haplotyping by the assignment of each read to a haplotype based on phase informative reads are simultaneously performed. Therefore, positions with low evidence for heterozygosity are rescued by phase informative reads, and such rescued positions contribute to haplotyping in a synergistic way. In addition, pedigree information supports more accurate haplotyping as well as genotyping, especially in low coverage regions. Although heterozygous positions are useful for haplotyping, homozygous positions are not informative and weaken the information from heterozygous positions, as majority of positions are homozygous. Thus, we introduce latent variables that determine zygosity at each position to filter out homozygous positions for haplotyping. In performance evaluation with a parent-offspring trio sequencing data, our approach outperforms existing approaches in accuracy on the agreement with single nucleotide polymorphism array genotyping results. Also, performance analysis considering distance between variants showed that the use of phase informative reads is effective for accurate variant calling, and further performance improvement is expected with longer sequencing data. kojima@megabank.tohoku.ac.jp .

Genome-wide association analysis identifies 13 new risk loci for schizophrenia

Schizophrenia is a heritable disorder with substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases, 6,243 controls) followed by meta-analysis with prior schizophrenia GWAS (8,832 cases, 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls, and 581 trios). In total, 22 regions met genome-wide significance (14 novel and one previously implicated in bipolar disorder). The results strongly implicate calcium signaling in the etiology of schizophrenia, and include genome-wide significant results for CACNA1C and CACNB2 whose protein products interact. We estimate that ∼8,300 independent and predominantly common SNPs contribute to risk for schizophrenia and that these collectively account for most of its heritability. Common genetic variation plays an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this devastating disorder.

Testing gene‐environment interactions in family‐based association studies using trait‐based ascertained samples

The study of gene-environment interactions is an increasingly important aspect of genetic epidemiological investigation. Historically, it has been difficult to study gene-environment interactions using a family-based design for quantitative traits or when parent-offspring trios were incomplete. The QBAT-I provides researchers a tool to estimate and test for a gene-environment interaction in families of arbitrary structure that are sampled without regard to the phenotype of interest, but is vulnerable to inflated type I error if families are ascertained on the basis of the phenotype. In this study, we verified the potential for type I error of the QBAT-I when applied to samples ascertained on a trait of interest. The magnitude of the inflation increases as the main genetic effect increases and as the ascertainment becomes more extreme. We propose an ascertainment-corrected score test that allows the use of the QBAT-I to test for gene-environment interactions in ascertained samples. Our results indicate that the score test and an ad hoc method we propose can often restore the nominal type I error rate, and in cases where complete restoration is not possible, dramatically reduce the inflation of the type I error rate in ascertained samples.

Rapid Multiplexed Genotyping of Simple Tandem Repeats using Capture and High-Throughput Sequencing

Although simple tandem repeats (STRs) comprise ~2% of the human genome and represent an important source of polymorphism, this class of variation remains understudied. We have developed a cost-effective strategy for performing targeted enrichment of STR regions that utilizes capture probes targeting the flanking sequences of STR loci, enabling specific capture of DNA fragments containing STRs for subsequent high-throughput sequencing. Utilizing a capture design targeting 6,243 STR loci <94 bp and multiplexing eight individuals in a single Illumina HiSeq2000 sequencing lane we were able to call genotypes in at least one individual for 67.5% of the targeted STRs. We observed a strong relationship between (G+C) content and genotyping rate. STRs with moderate (G+C) content were recovered with >90% success rate, whereas only 12% of STRs with ≥ 80% (G+C) were genotyped in our assay. Analysis of a parent-offspring trio, complete hydatidiform mole samples, repeat analyses of the same individual, and Sanger sequencing-based validation indicated genotyping error rates between 7.6% and 12.4%. The majority of such errors were a single repeat unit at mono- or dinucleotide repeats. Altogether, our STR capture assay represents a cost-effective method that enables multiplexed genotyping of thousands of STR loci suitable for large-scale population studies.

The Genome of the Netherlands: design, and project goals.

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent–offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910–1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14–15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.

De novo mutations in histone-modifying genes in congenital heart disease.

Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births1. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. By analysis of exome sequencing of parent-offspring trios, we compared the incidence of de novo mutations in 362 severe CHD cases and 264 controls. CHD cases showed a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging mutations. Similar odds ratios were seen across major classes of severe CHD. We found a marked excess of de novo mutations in genes involved in production, removal or reading of H3K4 methylation (H3K4me), or ubiquitination of H2BK120, which is required for H3K4 methylation2–4. There were also two de novo mutations in SMAD2; SMAD2 signaling in the embryonic left-right organizer induces demethylation of H3K27me5. H3K4me and H3K27me mark `poised' promoters and enhancers that regulate expression of key developmental genes6. These findings implicate de novo point mutations in several hundred genes that collectively contribute to ~10% of severe CHD.

Multi-cultural association of the serotonin transporter gene (SLC6A4) with substance use disorder.

A number of studies have reported associations between the serotonin transporter gene (SLC6A4) and alcohol, heroin, cocaine, or methamphetamine abuse. Other studies have yielded contrary results. There are a number of reasons for non-replication, including inadequate statistical power, population stratification, and poor phenotype definition. This study was to test the association using a meta-analytic approach across a variety of racial and ethnic populations. Using the genotype data of 55 studies (7999 cases, 8264 controls, and 676 families or parent-offspring trios) published in the past 15 years, we have conducted comprehensive meta-analyses to examine the associations of the 5-HTTLPR and STin2 polymorphisms with substance use disorder. The meta-analyses support the associations of 5-HTTLPR with alcohol, heroin, cocaine, and methamphetamine dependence and abuse (eg, the smallest P-values were 0.0058 with odds ratio (OR)=0.54 (0.35, 0.84); 0.0024 with OR=0.77 (0.66, 0.91); 0.018 with OR=1.38 (1.06, 1.81); and 0.028 with OR=0.46 (0.23, 0.92) for alcohol, heroin, cocaine, and methamphetamine dependence/abuse, respectively). When all the phenotypes are combined, the P-value was 0.0006 with OR=0.86 (0.78, 0.94) in the combined European, Asian, and Mexican populations and P-value was 0.0028 with OR=1.41 (1.13, 1.78) in the African populations. Evidence of significant associations was also identified in other subgroup analyses regarding differently combined substance and populations. The effect sizes of 5-HTTLPR were comparable among the European, Asian, and Mexican populations, however, the risk allele was more frequent in Asians than in Europeans and Mexicans. The opposite directions of risk allele in African population might be driven by the opposite directions of risk allele in cocaine dependence. This meta-analysis supports that the association of the SLC6A4 gene with substance use disorder varies depending on substances with different risk allele frequencies in the multi-cultural populations. Further studies using larger sample size are warranted.

Implication of a rare deletion at distal 16p11.2 in schizophrenia.

Large genomic copy number variations have been implicated as strong risk factors for schizophrenia. However, the rarity of these events has created challenges for the identification of further pathogenic loci, and extremely large samples are required to provide convincing replication. To detect novel copy number variations that increase the susceptibility to schizophrenia by using 2 ethnically homogeneous discovery cohorts and replication in large samples. Genetic association study of microarray data. Samples of DNA were collected at 9 sites from different countries. Two discovery cohorts consisted of 790 cases with schizophrenia and schizoaffective disorder and 1347 controls of Ashkenazi Jewish descent and 662 parent-offspring trios from Bulgaria, of which the offspring had schizophrenia or schizoaffective disorder. Replication data sets consisted of 12,398 cases and 17,945 controls. Statistically increased rate of specific copy number variations in cases vs controls. One novel locus was implicated: a deletion at distal 16p11.2, which does not overlap the proximal 16p11.2 locus previously reported in schizophrenia and autism. Deletions at this locus were found in 13 of 13,850 cases (0.094%) and 3 of 19,954 controls (0.015%) (odds ratio, 6.25 [95% CI, 1.78-21.93]; P = .001, Fisher exact test). Deletions at distal 16p11.2 have been previously implicated in developmental delay and obesity. The region contains 9 genes, several of which are implicated in neurological diseases, regulation of body weight, and glucose homeostasis. A telomeric extension of the deletion, observed in about half the cases but no controls, potentially implicates an additional 8 genes. Our findings add a new locus to the list of copy number variations that increase the risk for development of schizophrenia.

Inference of identity by descent in population isolates and optimal sequencing studies.

In an isolated population, individuals are likely to share large genetic regions inherited from common ancestors. Identity by descent (IBD) can be inferred from SNP genotypes, which is useful in a number of applications, including identifying genetic variants influencing complex disease risk, and planning efficient cohort-sequencing strategies. We present ANCHAP--a method for detecting IBD in isolated populations. We compare accuracy of the method against other long-range and local phasing methods, using parent-offspring trios. In our experiments, we show that ANCHAP performs similarly as the other long-range method, but requires an order-of-magnitude less computational resources. A local phasing model is able to achieve similar sensitivity, but only at the cost of higher false discovery rates. In some regions of the genome, the studied individuals share haplotypes particularly often, which hints at the history of the populations studied. We demonstrate the method using SNP genotypes from three isolated island populations, as well as in a cohort of unrelated individuals. In samples from three isolated populations of around 1000 individual each, an average individual shares a haplotype at a genetic locus with 9-12 other individuals, compared with only 1 individual within the non-isolated population. We describe an application of ANCHAP to optimally choose samples in resequencing studies. We find that with sample sizes of 1000 individuals from an isolated population genotyped using a dense SNP array, and with 20% of these individuals sequenced, 65% of sequences of the unsequenced subjects can be partially inferred.

Combining genetic association study designs: a GWAS case study

Genome-wide association studies (GWAS) explore the relationship between genome variability and disease susceptibility with either population- or family-based data. Here, we have evaluated the utility of combining population- and family-based statistical association tests and have proposed a method for reducing the burden of multiple testing. Unrelated singleton and parent-offspring trio cases and controls from the Genetics of Kidneys in Diabetes (GoKinD) study were analyzed for genetic association with diabetic nephropathy (DN) in type 1 diabetics (T1D). The Cochran-Armitage test for trend and the family-based association test were employed using either unrelated cases and controls or trios, respectively. In addition to combining single nucleotide polymorphism (SNP) p-values across these tests via Fisher's method, we employed a novel screening approach to rank SNPs based on conditional power for more efficient testing. Using either the population-based or family-based subset alone predictably limited resolution to detect DN SNPs. For 384,197 SNPs passing quality control (QC), none achieved strict genome-wide significance (1.4 × 10−7) using 1171 singletons (577/594 cases/controls) or 1738 pooled singletons and offspring probands (841/897). Similarly, none of the 352,004 SNPs passing QC in 567 family trios (264/303 case/control proband trios) reached genome-wide significance. Testing the top 10 SNPs ranked using aggregated conditional power resulted in two SNPs reaching genome-wide significance, rs11645147 on chromosome 16 (p = 1.74 × 10−4 < 0.05/10 = 0.005) and rs7866522 on chromosome 9 (p = 0.0033). Efficient usage of mixed designs incorporating both unrelated and family-based data may help to uncover associations otherwise difficult to detect in the presence of massive multiple testing corrections. Capitalizing on the strengths of both types while using screening approaches may be useful especially in light of large-scale, next-generation sequencing and rare variant studies.

A new era in the discovery of de novomutations underlying human genetic disease

Editorial text Germline mutations arise anew during meiosis in every generation. Such spontaneously occurring genetic alterations are termed de novo mutations and serve to describe those heritable mutations that neither parent possessed or transmitted. Thus, de novo mutations denote mutations that arose in the gametes of the parents as distinct from post-zygotic somatic mutations that arise during embryonic development. Studies of de novo mutations in the human genome have been very challenging owing to past technological limitations. However, the advent of highthrough put next-generation sequencing (NGS) technologies has ushered in a new era in the study of de novo mutations. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) can now be performed on parentoffspring trios to identify de novo point mutations in the entire genome or within protein-coding regions, respectively [1-3]. Although genome-wide de novo copy number variants (CNVs) were previously studied using microarrays [4-7], the study of de novo point mutations or single nucleotide variants (SNVs) required the advent of largescale sequencing, which was not feasible using Sanger sequencing [1-3]. As with inherited mutations, de novo mutations range in size from point mutations and small indels of multiple bases to much larger CNVs and structural rearrangements [8]. In this article, we focus on de novo point mutations/SNVs and their implications for our understanding of the etiology of common complex diseases as well as rare Mendelian diseases. The availability of NGS has allowed first estimations of the human genome mutation rate as well as a glimpse of

Genotype calling and haplotyping in parent-offspring trios

Emerging sequencing technologies allow common and rare variants to be systematically assayed across the human genome in many individuals. In order to improve variant detection and genotype calling, raw sequence data are typically examined across many individuals. Here, we describe a method for genotype calling in settings where sequence data are available for unrelated individuals and parent-offspring trios and show that modeling trio information can greatly increase the accuracy of inferred genotypes and haplotypes, especially on low to modest depth sequencing data. Our method considers both linkage disequilibrium (LD) patterns and the constraints imposed by family structure when assigning individual genotypes and haplotypes. Using simulations, we show that trios provide higher genotype calling accuracy across the frequency spectrum, both overall and at hard-to-call heterozygous sites. In addition, trios provide greatly improved phasing accuracy—improving the accuracy of downstream analyses (such as genotype imputation) that rely on phased haplotypes. To further evaluate our approach, we analyzed data on the first 508 individuals sequenced by the SardiNIA sequencing project. Our results show that our method reduces the genotyping error rate by 50% compared with analysis using existing methods that ignore family structure. We anticipate our method will facilitate genotype calling and haplotype inference for many ongoing sequencing projects.

Estimating the human mutation rate using autozygosity in a founder population.

Knowledge of the rate and pattern of new mutation is critical to the understanding of human disease and evolution. We used extensive autozygosity in a genealogically well-defined population of Hutterites to estimate the human sequence mutation rate over multiple generations. We sequenced whole genomes from 5 parent-offspring trios and identified 44 segments of autozygosity. Using the number of meioses separating each pair of autozygous alleles and the 72 validated heterozygous single-nucleotide variants (SNVs) from 512 Mb of autozygous DNA, we obtained an SNV mutation rate of 1.20 × 10(-8) (95% confidence interval 0.89-1.43 × 10(-8)) mutations per base pair per generation. The mutation rate for bases within CpG dinucleotides (9.72 × 10(-8)) was 9.5-fold that of non-CpG bases, and there was strong evidence (P = 2.67 × 10(-4)) for a paternal bias in the origin of new mutations (85% paternal). We observed a non-uniform distribution of heterozygous SNVs (both newly identified and known) in the autozygous segments (P = 0.001), which is suggestive of mutational hotspots or sites of long-range gene conversion.

Determining de novo mutation rates

Knowing the human de novo mutation rate is important for understanding our evolution and the origins of genetic diseases. These two papers characterize the de novo mutation rate, both using Icelandic families. Kong et al. sequenced the whole genomes of 78 parent–offspring trios at high coverage to study the mutation rate directly, whereas Sun et al. analysed more than 85,000 individuals at 2,477 microsatellite repeats to get an indirect measurement. Both studies found that the paternal contribution to the de novo mutation rate is higher than that of the mother and that maternal age is not an important determinant. However, the rates estimated in the two studies are different (1.2 × 10−8 and 1.4 to 2.3 × 10−8 mutations per nucleotide per generation for Kong et al. and Sun et al., respectively), and Kong et al. identified a much stronger effect of paternal age; these discrepancies require further study.

Longer time scale for human evolution

During the past few years, the best estimates of the human single nucleotide mutation rate have been cut in half. Until recently, estimates of mutation rate have relied on counting substitutions between primate species and assuming that fossil relatives of living species can accurately pin dates onto phylogenetic branches. This procedure allows very precise estimates, but introduces systematic bias toward higher substitution rates and longer branch lengths because a new lineage can leave a fossil record only after its origin, never beforehand (1). Now, widespread resequencing, initially of de novo Mendelian genetic disorders (2, 3) and later of whole genomes in parent–offspring trios (4), has allowed direct comparisons of parent and offspring genomes. The most commonly used, but now outdated, estimate of the mutation rate was 2.4 × 10−8 changes per nucleotide per generation (5). Current estimates of the same value based on resequencing data are much lower, approximately 1.1 to 1.28 × 10−8 (3, 4).

Harnessing genomics to identify environmental determinants of heritable disease

Next-generation sequencing technologies can now be used to directly measure heritable de novo DNA sequence mutations in humans. However, these techniques have not been used to examine environmental factors that induce such mutations and their associated diseases. To address this issue, a working group on environmentally induced germline mutation analysis (ENIGMA) met in October 2011 to propose the necessary foundational studies, which include sequencing of parent-offspring trios from highly exposed human populations, and controlled dose-response experiments in animals. These studies will establish background levels of variability in germline mutation rates and identify environmental agents that influence these rates and heritable disease. Guidance for the types of exposures to examine come from rodent studies that have identified agents such as cancer chemotherapeutic drugs, ionizing radiation, cigarette smoke, and air pollution as germ-cell mutagens. Research is urgently needed to establish the health consequences of parental exposures on subsequent generations.