The effect of ascertainment on penetrance estimates for rare variants: Implications for establishing pathogenicity and for genetic counselling.

Next-generation sequencing has led to an explosion of genetic findings for many rare diseases. However, most of the variants identified are very rare and were also identified in small pedigrees, which creates challenges in terms of penetrance estimation and translation into genetic counselling in the setting of cascade testing. We use simulations to show that for a rare (dominant) disorder where a variant is identified in a small number of small pedigrees, the penetrance estimate can both have large uncertainty and be drastically inflated, due to underlying ascertainment bias. We have developed PenEst, an app that allows users to investigate the phenomenon across ranges of parameter settings. We also illustrate robust ascertainment corrections via the LOD (logarithm of the odds) score, and recommend a LOD-based approach to assessing pathogenicity of rare variants in the presence of reduced penetrance.

A genome-wide association analysis of loss of ambulation in dystrophinopathy patients suggests multiple candidate modifiers of disease severity.

The major determinant of disease severity in Duchenne muscular dystrophy (DMD) or milder Becker muscular dystrophy (BMD) is whether the dystrophin gene (DMD) mutation truncates the mRNA reading frame or allows expression of a partially functional protein. However, even in the complete absence of dystrophin, variability in disease severity is observed, and candidate gene studies have implicated several genes as modifiers. Here we present the largest genome-wide search to date for loci influencing severity in N = 419 DMD patients. Availability of subjects for such studies is quite limited, leading to modest sample sizes, which present a challenge for GWAS design. We have therefore taken special steps to minimize heterogeneity within our dataset at the DMD locus itself, taking a novel approach to mutation classification to effectively exclude the possibility of residual dystrophin expression, and utilized statistical methods that are well adapted to smaller sample sizes, including the use of a novel linear regression-like residual for time to ambulatory loss and the application of evidential statistics for the GWAS approach. Finally, we applied an unbiased in silico pipeline, utilizing functional genomic datasets to explore the potential impact of the best supported SNPs. In all, we obtained eight SNPs (out of 1,385,356 total) with posterior probability of trait-marker association (PPLD) ≥ 0.4, representing six distinct loci. Our analysis prioritized likely non-coding SNP regulatory effects on six genes (ETAA1, PARD6G, GALNTL6, MAN1A1, ADAMTS19, and NCALD), each with plausibility as a DMD modifier. These results support both recurrent and potentially new pathways for intervention in the dystrophinopathies.

The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies.

In earlier work, we have developed and evaluated an alternative approach to the analysis of GWAS data, based on a statistic called the PPLD. More recently, motivated by a GWAS for genetic modifiers of the X-linked Mendelian disorder Duchenne Muscular Dystrophy (DMD), we adapted the PPLD for application to time-to-event (TE) phenotypes. Because DMD itself is relatively rare, this is a setting in which the very large sample sizes generally assembled for GWAS are simply not attainable. For this reason, statistical methods specially adapted for use in small data sets are required. Here we explore the behavior of the TE-PPLD via simulations, comparing the TE-PPLD with Cox Proportional Hazards analysis in the context of small to moderate sample sizes. Our results will help to inform our approach to the DMD study going forward, and they illustrate several respects in which the TE-PPLD, and by extension the original PPLD, offer advantages over regression-based approaches to GWAS in this context.

A new linear regression-like residual for survival analysis, with application to genome wide association studies of time-to-event data.

In linear regression, a residual measures how far a subject's observation is from expectation; in survival analysis, a subject's Martingale or deviance residual is sometimes interpreted similarly. Here we consider ways in which a linear regression-like interpretation is not appropriate for Martingale and deviance residuals, and we develop a novel time-to-event residual which does have a linear regression-like interpretation. We illustrate the utility of this new residual via simulation of a time-to-event genome-wide association study, motivated by a real study seeking genetic modifiers of Duchenne Muscular Dystrophy. By virtue of its linear regression-like characteristics, our new residual may prove useful in other contexts as well.

Segregating patterns of copy number variations in extended autism spectrum disorder (ASD) pedigrees.

Autism spectrum disorder (ASD) is a relatively common childhood onset neurodevelopmental disorder with a complex genetic etiology. While progress has been made in identifying the de novo mutational landscape of ASD, the genetic factors that underpin the ASD's tendency to run in families are not well understood. In this study, nine extended pedigrees each with three or more individuals with ASD, and others with a lesser autism phenotype, were phenotyped and genotyped in an attempt to identify heritable copy number variants (CNVs). Although these families have previously generated linkage signals, no rare CNV segregated with these signals in any family. A small number of clinically relevant CNVs were identified. Only one CNV was identified that segregated with ASD phenotype; namely, a duplication overlapping DLGAP2 in three male offspring each with an ASD diagnosis. This gene encodes a synaptic scaffolding protein, part of a group of proteins known to be pathologically implicated in ASD. On the whole, however, the heritable nature of ASD in the families studied remains poorly understood.

Long-range genomic regulators of THBS1 and LTBP4 modify disease severity in duchenne muscular dystrophy.

OBJECTIVE: Duchenne muscular dystrophy (DMD) is a severe X-linked recessive disease caused by loss-of-function dystrophin (DMD) mutations in boys, who typically suffer loss of ambulation by age 12. Previously, we reported that coding variants in latent transforming growth factor beta (TGFß)-binding protein 4 (LTBP4) were associated with reduced TGFß signaling and prolonged ambulation (p = 1.0 × 10-3 ) in DMD patients; this result was subsequently replicated by other groups. In this study, we evaluated whether additional DMD modifier genes are observed using whole-genome association in the original cohort. METHODS: We performed a genome-wide association study (GWAS) for single-nucleotide polymorphisms (SNPs) influencing loss of ambulation (LOA) in the same cohort of 253 DMD patients used to detect the candidate association with LTBP4 coding variants. Gene expression and chromatin interaction databases were used to fine-map association signals above the threshold for genome-wide significance. RESULTS: Despite the small sample size, two loci associated with prolonged ambulation met genome-wide significance and were tagged by rs2725797 (chr15, p = 6.6 × 10-9 ) and rs710160 (chr19, p = 4.7 × 10-8 ). Gene expression and chromatin interaction data indicated that the latter SNP tags regulatory variants of LTBP4, whereas the former SNP tags regulatory variants of thrombospondin-1 (THBS1): an activator of TGFß signaling by direct binding to LTBP4 and an inhibitor of proangiogenic nitric oxide signaling. INTERPRETATION: Together with previous evidence implicating LTBP4, the THBS1 modifier locus emphasizes the role that common regulatory variants in gene interaction networks can play in mitigating disease progression in muscular dystrophy. Ann Neurol 2018;84:234-245.

A single nucleotide polymorphism in the dimethylarginine dimethylaminohydrolase gene is associated with lower risk of pulmonary hypertension in bronchopulmonary dysplasia.

AIM: Pulmonary hypertension (PH) develops in 25-40% of bronchopulmonary dysplasia (BPD) patients, substantially increasing mortality. We have previously found that asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide (NO) production, is elevated in patients with BPD-associated PH. ADMA is metabolised by N(á´³) ,N(á´³) -dimethylarginine dimethylaminohydrolase (DDAH). Presently, we test the hypothesis that there are single nucleotide polymorphisms (SNPs) in DDAH1 and/or DDAH2 associated with the development of PH in BPD patients. METHODS: BPD patients were enrolled (n = 98) at Nationwide Children's Hospital. Clinical characteristics and 36 SNPs in DDAH1 and DDAH2 were compared between BPD-associated PH patients (cases) and BPD-alone patients (controls). RESULTS: In BPD patients, 25 (26%) had echocardiographic evidence of PH (cases). In this cohort, DDAH1 wild-type rs480414 was 92% sensitive and 53% specific for PH in BPD, and the DDAH1 SNP rs480414 decreased the risk of PH in an additive model of inheritance (OR = 0.39; 95% CI [0.18-0.88], p = 0.01). CONCLUSION: The rs480414 SNP in DDAH1 may be protective against the development of PH in patients with BPD. Furthermore, the DDAH1 rs480414 may be a useful biomarker in developing predictive models for PH in patients with BPD.

Using extended pedigrees to identify novel autism spectrum disorder (ASD) candidate genes.

Copy number variation has emerged as an important cause of phenotypic variation, particularly in relation to some complex disorders. Autism spectrum disorder (ASD) is one such disorder, in which evidence is emerging for an etiological role for some rare penetrant de novo and rare inherited copy number variants (CNVs). De novo variation, however, does not always explain the familial nature of ASD, leaving a gap in our knowledge concerning the heritable genetic causes of this disorder. Extended pedigrees, in which several members have ASD, provide an opportunity to investigate inherited genetic risk factors. In this current study, we recruited 19 extended ASD pedigrees, and, using the Illumina HumanOmni2.5 BeadChip, conducted genome-wide CNV interrogation. We found no definitive evidence of an etiological role for segregating CNVs in these pedigrees, and no evidence that linkage signals in these pedigrees are explained by segregating CNVs. However, a small number of putative de novo variants were transmitted from BAP parents to their ASD offspring, and evidence emerged for a rare duplication CNV at 11p13.3 harboring two putative 'developmental/neuropsychiatric' susceptibility gene(s), GSTP1 and NDUFV1.

Host-to-host variation of ecological interactions in polymicrobial infections.

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

Next-generation linkage and association methods applied to hypertension: a multifaceted approach to the analysis of sequence data.

To realize the full potential of next-generation sequencing, it is important to consider multiple sources of genetic information, including inheritance, association, and bioinformatics. To illustrate the promise of such an approach, we applied our next-generation linkage and association (NGLA) methods to the sequence data of a large 57-member Mexican American family with hypertension. Our results show that OSBPL10--a disease susceptibility gene for dyslipidemia--may also influence systolic blood pressure (SBP). In particular, our NGLA dense single-nucleotide polymorphism (SNP) analysis identified a 2.5-megabase (Mb) region that strongly cosegregates with low SBP (maximum posterior probability of linkage [PPL] = 68%). Furthermore, using the posterior probability of linkage disequilibrium (PPLD), we fine-mapped this region and identified 12 SBP-associated variants (PPLD ranging between 4% and 14%) that comprise a rare, 4-site haplotype. This haplotype extends into the candidate gene, OSBPL10 (oxysterol-binding protein-like 10). In contrast to our NGLA methods, a commonly used filter-based approach identified 23 variants with little evidence for spatial clustering around any particular gene or region of interest.

Evidence, temperature, and the laws of thermodynamics.

A primary purpose of statistical analysis in genetics is the measurement of the strength of evidence for or against hypotheses. As with any type of measurement, a properly calibrated measurement scale is necessary if we want to be able to meaningfully compare degrees of evidence across genetic data sets, across different types of genetic studies and/or across distinct experimental modalities. In previous papers in this journal and elsewhere, my colleagues and I have argued that geneticists ought to care about the scale on which statistical evidence is measured, and we have proposed the Kelvin temperature scale as a template for a context-independent measurement scale for statistical evidence. Moreover, we have claimed that, mathematically speaking, evidence and temperature may be one and the same thing. On first blush, this might seem absurd. Temperature is a property of systems following certain laws of nature (in particular, the 1st and 2nd Law of Thermodynamics) involving very physical quantities (e.g., energy) and processes (e.g., mechanical work). But what do the laws of thermodynamics have to do with statistical systems? Here I address that question.

The value of regenotyping older linkage data sets with denser marker panels.

OBJECTIVES: Linkage analysis can help determine regions of interest in whole-genome sequence studies. However, many linkage studies rely on older microsatellite (MSAT) panels. We set out to determine whether results would change if we regenotyped families using a dense map of SNPs. METHODS: We selected 47 Hispanic-American families from the NIMH Repository and Genomics Resource (NRGR) schizophrenia data repository. We regenotyped all individuals with DNA available from the NRGR on the Affymetrix Lat Array. After optimizing SNP selection for inclusion on the linkage map, we compared information content (IC) and linkage results using MSAT, SNP and MSAT+SNP maps. RESULTS: As expected, SNP provided a higher average IC (0.78, SD 0.03) than MSAT (0.51, SD 0.10) in a direct 'apples-to-apples' comparison using only individuals genotyped on both platforms; while MSAT+SNP provided only a slightly higher IC (0.82, SD 0.03). However, when utilizing all available individuals, including those who had genotypes available on only one platform, the IC was substantially increased using MSAT+SNP (0.76, SD 0.05) compared to SNP (0.61, SD 0.02). Linkage results changed appreciably between MSAT and MSAT+SNP in terms of magnitude, rank ordering and localization of peaks. CONCLUSIONS: Regenotyping older family data can substantially alter the conclusions of linkage analyses.

PEDSnet: a National Pediatric Learning Health System.

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.

Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.

Revisiting schizophrenia linkage data in the NIMH Repository: reanalysis of regularized data across multiple studies.

OBJECTIVE: The Combined Analysis of Psychiatric Studies (CAPS) project conducted extensive review and regularization across studies of all schizophrenia linkage data available as of 2011 from the National Institute of Mental Health-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI). The authors reanalyzed the data using statistical methods tailored to accumulation of evidence across multiple, potentially highly heterogeneous, sets of data. METHOD: Data were subdivided based on contributing study, major population group, and presence or absence within families of schizophrenia with a substantial affective component. The posterior probability of linkage (PPL) statistical framework was used to sequentially update linkage evidence across these data subsets (omnibus results). RESULTS: While some loci previously implicated using the HGI data were also identified in the present omnibus analysis (2q36.1, 15q23), others were not. Several loci were found that had not previously been reported in the HGI samples but are supported by independent linkage or association studies (3q28, 12q23.1, 11p11.2, Xq26.1). Not surprisingly, differences were seen across population groups. Of particular interest are signals on 11p15.3, 11p11.2, and Xq26.1, for which data from families with a substantial affective component support linkage while data from the remaining families provide evidence against linkage. All three of these loci overlap with loci reported in independent studies of bipolar disorder or mixed bipolar-schizophrenia samples. CONCLUSIONS: Public data repositories provide the opportunity to leverage large multisite data sets for studying complex disorders. Analysis with a statistical method specifically designed for such data enables us to extract new information from an existing data resource.

Cell responses only partially shape cell-to-cell variations in protein abundances in Escherichia coli chemotaxis.

Cell-to-cell variations in protein abundance in clonal cell populations are ubiquitous in living systems. Because protein composition determines responses in individual cells, it stands to reason that the variations themselves are subject to selective pressures. However, the functional role of these cell-to-cell differences is not well understood. One way to tackle questions regarding relationships between form and function is to perturb the form (e.g., change the protein abundances) and observe the resulting changes in some function. Here, we take on the form-function relationship from the inverse perspective, asking instead what specific constraints on cell-to-cell variations in protein abundance are imposed by a given functional phenotype. We develop a maximum entropy-based approach to posing questions of this type and illustrate the method by application to the well-characterized chemotactic response in Escherichia coli. We find that full determination of observed cell-to-cell variations in protein abundances is not inherent in chemotaxis itself but, in fact, appears to be jointly imposed by the chemotaxis program in conjunction with other factors (e.g., the protein synthesis machinery and/or additional nonchemotactic cell functions, such as cell metabolism). These results illustrate the power of maximum entropy as a tool for the investigation of relationships between biological form and function.

Data-driven quantification of the robustness and sensitivity of cell signaling networks.

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.

A molecular genetic study of autism and related phenotypes in extended pedigrees.

BACKGROUND: Efforts to uncover the risk genotypes associated with the familial nature of autism spectrum disorder (ASD) have had limited success. The study of extended pedigrees, incorporating additional ASD-related phenotypes into linkage analysis, offers an alternative approach to the search for inherited ASD susceptibility variants that complements traditional methods used to study the genetics of ASD. METHODS: We examined evidence for linkage in 19 extended pedigrees ascertained through ASD cases spread across at least two (and in most cases three) nuclear families. Both compound phenotypes (i.e., ASD and, in non-ASD individuals, the broad autism phenotype) and more narrowly defined components of these phenotypes, e.g., social and repetitive behavior, pragmatic language, and anxiety, were examined. The overarching goal was to maximize the aggregate information available on the maximum number of individuals and to disaggregate syndromic phenotypes in order to examine the genetic underpinnings of more narrowly defined aspects of ASD behavior. RESULTS: Results reveal substantial between-family locus heterogeneity and support the importance of previously reported ASD loci in inherited, familial, forms of ASD. Additional loci, not seen in the ASD analyses, show evidence for linkage to the broad autism phenotype (BAP). BAP peaks are well supported by multiple subphenotypes (including anxiety, pragmatic language, and social behavior) showing linkage to regions overlapping with the compound BAP phenotype. Whereas 'repetitive behavior', showing the strongest evidence for linkage (Posterior Probability of Linkage = 62% at 6p25.2-24.3, and 69% at 19p13.3), appears to be linked to novel regions not detected with other compound or narrow phenotypes examined in this study. CONCLUSIONS: These results provide support for the presence of key features underlying the complexity of the genetic architecture of ASD: substantial between-family locus heterogeneity, that the BAP appears to correspond to sets of subclinical features segregating with ASD within pedigrees, and that different features of the ASD phenotype segregate independently of one another. These findings support the additional study of larger, even more individually informative pedigrees, together with measurement of multiple, behavioral- and biomarker-based phenotypes, in both affected and non-affected individuals, to elucidate the complex genetics of familial ASD.

In silico modeling of Itk activation kinetics in thymocytes suggests competing positive and negative IP4 mediated feedbacks increase robustness.

The inositol-phosphate messenger inositol(1,3,4,5)tetrakisphosphate (IP4) is essential for thymocyte positive selection by regulating plasma-membrane association of the protein tyrosine kinase Itk downstream of the T cell receptor (TCR). IP4 can act as a soluble analog of the phosphoinositide 3-kinase (PI3K) membrane lipid product phosphatidylinositol(3,4,5)trisphosphate (PIP3). PIP3 recruits signaling proteins such as Itk to cellular membranes by binding to PH and other domains. In thymocytes, low-dose IP4 binding to the Itk PH domain surprisingly promoted and high-dose IP4 inhibited PIP3 binding of Itk PH domains. However, the mechanisms that underlie the regulation of membrane recruitment of Itk by IP4 and PIP3 remain unclear. The distinct Itk PH domain ability to oligomerize is consistent with a cooperative-allosteric mode of IP4 action. However, other possibilities cannot be ruled out due to difficulties in quantitatively measuring the interactions between Itk, IP4 and PIP3, and in generating non-oligomerizing Itk PH domain mutants. This has hindered a full mechanistic understanding of how IP4 controls Itk function. By combining experimentally measured kinetics of PLCγ1 phosphorylation by Itk with in silico modeling of multiple Itk signaling circuits and a maximum entropy (MaxEnt) based computational approach, we show that those in silico models which are most robust against variations of protein and lipid expression levels and kinetic rates at the single cell level share a cooperative-allosteric mode of Itk regulation by IP4 involving oligomeric Itk PH domains at the plasma membrane. This identifies MaxEnt as an excellent tool for quantifying robustness for complex TCR signaling circuits and provides testable predictions to further elucidate a controversial mechanism of PIP3 signaling.

Employing MCMC under the PPL framework to analyze sequence data in large pedigrees.

The increased feasibility of whole-genome (or whole-exome) sequencing has led to renewed interest in using family data to find disease mutations. For clinical phenotypes that lend themselves to study in large families, this approach can be particularly effective, because it may be possible to obtain strong evidence of a causal mutation segregating in a single pedigree even under conditions of extreme locus and/or allelic heterogeneity at the population level. In this paper, we extend our capacity to carry out positional mapping in large pedigrees, using a combination of linkage analysis and within-pedigree linkage trait-variant disequilibrium analysis to fine map down to the level of individual sequence variants. To do this, we develop a novel hybrid approach to the linkage portion, combining the non-stochastic approach to integration over the trait model implemented in the software package Kelvin, with Markov chain Monte Carlo-based approximation of the marker likelihood using blocked Gibbs sampling as implemented in the McSample program in the JPSGCS package. We illustrate both the positional mapping template, as well as the efficacy of the hybrid algorithm, in application to a single large pedigree with phenotypes simulated under a two-locus trait model.