Role of Segregation for Variant Discovery in Multiplex Families Ascertained by Probands With Left Sided Cardiovascular Malformations

Cardiovascular malformations (CVM) are common birth defects (incidence of 2–5/100 live births). Although a genetic basis is established, in most cases the cause remains unknown. Analysis of whole exome sequencing (WES) in left sided CVM case and trio series has identified large numbers of potential variants but evidence of causality has remained elusive except in a small percentage of cases. We sought to determine whether variant segregation in families would aid in novel gene discovery. The objective was to compare conventional and co-segregation approaches for WES in multiplex families. WES was performed on 52 individuals from 4 multiplex families ascertained by probands with hypoplastic left heart syndrome (HLHS). We identified rare variants with informatics support (RVIS, minor allele frequency ≤0.01 and Combined Annotation Dependent Depletion score ≥20) in probands. Non-RVIS variants did not meet these criteria. Family specific two point logarithm of the odds (LOD) scores identified co-segregating variants (C-SV) using a dominant model and 80% penetrance. In families, 702 RVIS in 668 genes were identified, but only 1 RVIS was also a C-SV (LOD ≥ 1). On the other hand, there were 109 non-RVIS variants with LOD ≥ 1. Among 110 C-SV, 97% were common (MAF > 1%). These results suggest that conventional variant identification methods focused on RVIS, miss most C-SV. For diseases such as left sided CVM, which exhibit strong familial transmission, co-segregation can identify novel candidates

Modeling of Multivariate Longitudinal Phenotypes in Family Genetic Studies with Bayesian Multiplicity Adjustment

Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family members and repeated measures, respectively. Correlation between traits is introduced by incorporating multivariate random effects and allowing time-specific trait residuals to correlate as in seemingly unrelated regressions. The proposed model can examine multiple single-nucleotide variations simultaneously, as well as incorporate familyspecific, subject-specific, or time-varying covariates. Bayesian multiplicity technique is used to effectively control false positives. Genetic Analysis Workshop 18 simulated data illustrate the proposed approach\u27s applicability in modeling longitudinal multivariate outcomes in family genetic association studies

On Family-Based Genome-Wide Association Studies with Large Pedigrees: Observations and Recommendations

Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA

Genetic Influences on Behavioral Outcomes After Childhood TBI: A Novel Systems Biology-Informed Approach

Objectives: To test whether genetic associations with behavioral outcomes after early childhood traumatic brain injury (TBI) are enriched for biologic pathways underpinning neurocognitive and behavioral networks.Design: Cross-sectional evaluation of the association of genetic factors with early (~ 6 months) and long-term (~ 7 years) post-TBI behavioral outcomes. We combined systems biology and genetic association testing methodologies to identify biologic pathways associated with neurocognitive and behavior outcomes after TBI. We then evaluated whether genes/single nucleotide polymorphism (SNPs) associated with these biologic pathways were more likely to demonstrate a relationship (i.e., enrichment) with short and long-term behavioral outcomes after early childhood TBI compared to genes/SNPs not associated with these biologic pathways.Setting: Outpatient research setting.Participants:140 children, ages 3–6:11 years at time of injury, admitted for a TBI or orthopedic injury (OI).Interventions: Not Applicable.Main Outcome Measures: Child behavior checklist total problems T score.Results: Systems biology methodology identified neuronal systems and neurotransmitter signaling (Glutamate receptor, dopamine, serotonin, and calcium signaling), inflammatory response, cell death, immune systems, and brain development as important biologic pathways to neurocognitive and behavioral outcomes after TBI. At 6 months post injury, the group (TBI versus OI) by polymorphism interaction was significant when the aggregate signal from the highest ranked 40% of case gene associations was compared to the control set of genes. At ~ 7 years post injury, the selected polymorphisms had a significant main effect after controlling for injury type when the aggregate signal from the highest ranked 10% of the case genes were compared to the control set of genesConclusions: Findings demonstrate the promise of applying a genomics approach, informed by systems biology, to understanding behavioral recovery after pediatric TBI. A mixture of biologic pathways and processes are associated with behavioral recovery, specifically genes associated with cell death, inflammatory response, neurotransmitter signaling, and brain development. These results provide insights into the complex biology of TBI recovery

Using Mendelian inheritance errors as quality control criteria in whole genome sequencing data set

Although the technical and analytic complexity of whole genome sequencing is generally appreciated, best practices for data cleaning and quality control have not been defined. Family based data can be used to guide the standardization of specific quality control metrics in nonfamily based data. Given the low mutation rate, Mendelian inheritance errors are likely as a result of erroneous genotype calls. Thus, our goal was to identify the characteristics that determine Mendelian inheritance errors. To accomplish this, we used chromosome 3 whole genome sequencing family based data from the Genetic Analysis Workshop 18. Mendelian inheritance errors were provided as part of the GAW18 data set. Additionally, for binary variants we calculated Mendelian inheritance errors using PLINK. Based on our analysis, nonbinary single-nucleotide variants have an inherently high number of Mendelian inheritance errors. Furthermore, in binary variants, Mendelian inheritance errors are not randomly distributed. Indeed, we identified 3 Mendelian inheritance error peaks that were enriched with repetitive elements. However, these peaks can be lessened with the inclusion of a single filter from the sequencing file. In summary, we demonstrated that erroneous sequencing calls are nonrandomly distributed across the genome and quality control metrics can dramatically reduce the number of mendelian inheritance errors. Appropriate quality control will allow optimal use of genetic data to realize the full potential of whole genome sequencing

Age and Sex of Mice Markedly Affect Survival Times Associated with Hyperoxic Acute Lung Injury.

Mortality associated with acute lung injury (ALI) remains substantial, with recent estimates of 35-45% similar to those obtained decades ago. Although evidence for sex-related differences in ALI mortality remains equivocal, death rates differ markedly for age, with more than 3-fold increased mortality in older versus younger patients. Strains of mice also show large differences in ALI mortality. To tease out genetic factors affecting mortality, we established a mouse model of differential hyperoxic ALI (HALI) survival. Separate genetic analyses of backcross and F2 populations generated from sensitive C57BL/6J (B) and resistant 129X1/SvJ (X1) progenitor strains identified two quantitative trait loci (QTLs; Shali1 and Shali2) with strong, equal but opposite, within-strain effects on survival. Congenic lines confirmed these opposing QTL effects, but also retained the low penetrance seen in the 6-12 week X1 control strain. Sorting mice into distinct age groups revealed that 'age at exposure' inversely correlated with survival time and explained reduced penetrance of the resistance trait. While B mice were already sensitive by 6 weeks old, X1 mice maintained significant resistance up to 3-4 weeks longer. Reanalysis of F2 data gave analogous age-related findings, and also supported sex-specific linkage for Shali1 and Shali2. Importantly, we have demonstrated in congenic mice that these age effects on survival correspond with B alleles for Shali1 (6-week old mice more sensitive) and Shali2 (10-week old mice more resistant) placed on the X1 background. Further studies revealed significant sex-specific survival differences in subcongenics for both QTLs. Accounting for age and sex markedly improved penetrance of both QTLs, thereby reducing trait variability, refining Shali1 to <8.5Mb, and supporting several sub-QTLs within the Shali2 interval. Together, these congenics will allow age- and sex-specific studies to interrogate myriad subphenotypes affected during ALI development and progression and identify intermediary injury biomarkers that can predict outcome