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.

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.

Mapping autism risk loci using genetic linkage and chromosomal rearrangements.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

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.

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.

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.

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.

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.

Rare familial 16q21 microdeletions under a linkage peak implicate cadherin 8 (CDH8) in susceptibility to autism and learning disability.

BACKGROUND: Autism spectrum disorder (ASD) is characterised by impairments in social communication and by a pattern of repetitive behaviours, with learning disability (LD) typically seen in up to 70% of cases. A recent study using the PPL statistical framework identified a novel region of genetic linkage on chromosome 16q21 that is limited to ASD families with LD. METHODS: In this study, two families with autism and/or LD are described which harbour rare >1.6 Mb microdeletions located within this linkage region. The deletion breakpoints are mapped at base-pair resolution and segregation analysis is performed using a combination of 1M single nucleotide polymorphism (SNP) technology, array comparative genomic hybridisation (CGH), long-range PCR, and Sanger sequencing. The frequency of similar genomic variants in control subjects is determined through analysis of published SNP array data. Expression of CDH8, the only gene disrupted by these microdeletions, is assessed using reverse transcriptase PCR and in situ hybridisation analysis of 9 week human embryos. RESULTS: The deletion of chr16: 60 025 584-61 667 839 was transmitted to three of three boys with autism and LD and none of four unaffected siblings, from their unaffected mother. In a second family, an overlapping deletion of chr16: 58 724 527-60 547 472 was transmitted to an individual with severe LD from his father with moderate LD. No copy number variations (CNVs) disrupting CDH8 were observed in 5023 controls. Expression analysis indicates that the two CDH8 isoforms are present in the developing human cortex. CONCLUSION: Rare familial 16q21 microdeletions and expression analysis implicate CDH8 in susceptibility to autism and LD.

Where's the evidence?

Science is in large part the art of careful measurement, and a fixed measurement scale is the sine qua non of this art. It is obvious to us that measurement devices lacking fixed units and constancy of scale across applications are problematic, yet we seem oddly laissez faire in our approach to measurement of one critically important quantity: statistical evidence. Here I reconsider problems with reliance on p values or maximum LOD scores as measures of evidence, from a measure-theoretic perspective. I argue that the lack of an absolute scale for evidence measurement is every bit as problematic for modern biological research as was lack of an absolute thermal scale in pre-thermodynamic physics. Indeed, the difficulty of establishing properly calibrated evidence measures is strikingly similar to the problem 19th century physicists faced in deriving an absolute scale for the measurement of temperature. I propose that the formal relationship between the two problems might enable us to apply the mathematical foundations of thermodynamics to establish an absolute scale for the measurement of evidence, in statistical applications and possibly other areas of mathematical modeling as well. Here I begin to sketch out what such an endeavor might look like.

KELVIN: a software package for rigorous measurement of statistical evidence in human genetics.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Association statistics under the PPL framework.

In this paper, we extend the PPL framework to the analysis of case-control (CC) data and introduce three new linkage disequilibrium (LD) statistics. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the seamless analysis of disparate data structures. They also provide other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets or subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, by implementing the new statistics within the PPL framework, we have a ready mechanism for incorporating linkage information, obtained from distinct data, into LD analyses in the form of a prior distribution. Here we examine the performance of the proposed LD statistics using simulated data, as well as assessing the effects of key modeling violations on this performance.

A multilocus model of the genetic architecture of autoimmune thyroid disorder, with clinical implications.

We report here a preliminary model of the genetic architecture of Autoimmune Thyroid Disorder (AITD). Using a flexible class of mathematical modeling techniques, applied to an established set of data and supplemented with information both from candidate-gene and genome-wide-association studies and from basic bioinformatics, we find strong statistical support for a model in which AITD is the result of "hits" along three distinct genetic pathways: affected individuals have (1) a genetic susceptibility to clinical AITD, along with (2) a separate predisposition to develop the autoantibodies characteristic of AITD, and they also have (3) a predisposition to develop high levels of autoantibodies once they occur. Genes underlying each of these factors then appear to interact with one another to cause clinical AITD. We also find that a genetic variant in CTLA4 that increases risk for AITD in some people might actually protect against AITD in others, depending on which additional risk variants an individual carries. Our data show that the use of statistical methods for the incorporation of information from multiple sources, combined with careful modeling of distinct intermediate phenotypes, can provide insights into the genetic architecture of complex diseases. This model has several clinical implications, which we believe will prove relevant to other complex diseases as well.

Expected monotonicity--a desirable property for evidence measures?

We consider here the principle of 'evidential consistency' - that as one gathers more data, any well-behaved evidence measure should, in some sense, approach the true answer. Evidential consistency is essential for the genome-scan design (GWAS or linkage), where one selects the most promising locus(i) for follow-up, expecting that new data will increase evidence for the correct hypothesis. Earlier work [Vieland, Hum Hered 2006;61:144-156] showed that many popular statistics do not satisfy this principle; Vieland concluded that the problem stems from fundamental difficulties in how we measure evidence and argued for determining criteria to evaluate evidence measures. Here, we investigate in detail one proposed consistency criterion - expected monotonicity (ExpM) - for a simple statistical model (binomial) and four likelihood ratio (LR)-based evidence measures. We show that, with one limited exception, none of these measures displays ExpM; what they do display is sometimes counterintuitive. We conclude that ExpM is not a reasonable requirement for evidence measures; moreover, no requirement based on expected values seems feasible. We demonstrate certain desirable properties of the simple LR and demonstrate a connection between the simple and integrated LRs. We also consider an alternative version of consistency, which is satisfied by certain forms of the integrated LR and posterior probability of linkage.

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.