Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restrictive interests and/or repetitive behaviors and deficits in social interaction and communication. ASD is a multifactorial disease with a complex polygenic genetic architecture. Its genetic contributing factors are not yet fully understood, especially large structural variations (SVs). In this study, we aimed to assess the contribution of SVs, including copy number variants (CNVs), insertions, deletions, duplications, and mobile element insertions, to ASD and related language impairments in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Within the cohort, ~77% of the families contain SVs that followed expected segregation or de novo patterns and passed our filtering criteria. These SVs affected 344 brain-expressed genes and can potentially contribute to the genetic etiology of the disorders. Gene Ontology and protein-protein interaction network analysis suggested several clusters of genes in different functional categories, such as neuronal development and histone modification machinery. Genes and biological processes identified in this study contribute to the understanding of ASD and related neurodevelopment disorders.

The Rosetta Phenotype Harmonization Method Facilitates Finding a Relationship Quantitative Trait Locus for a Complex Cognitive Trait.

Genetics researchers increasingly combine data across many sources to increase power and to conduct analyses that cross multiple individual studies. However, there is often a lack of alignment on outcome measures when the same constructs are examined across studies. This inhibits comparison across individual studies and may impact the findings from meta-analysis. Using a well-characterized genotypic (brain-derived neurotrophic factor: BDNF) and phenotypic constructs (working memory and reading comprehension), we employ an approach called Rosetta, which allows for the simultaneous examination of primary studies that employ related but incompletely overlapping data. We examined four studies of BDNF, working memory, and reading comprehension with a combined sample size of 1711 participants. Although the correlation between working memory and reading comprehension over all participants was high, as expected (ρ = 0.45), the correlation between working memory and reading comprehension was attenuated in the BDNF Met/Met genotype group (ρ = 0.18, n.s.) but not in the Val/Val (ρ = 0.44) or Val/Met (ρ = 0.41) groups. These findings indicate that Met/Met carriers may be a unique and robustly defined subgroup in terms of memory and reading comprehension. This study demonstrates the utility of the Rosetta method when examining complex phenotypes across multiple studies, including psychiatric genetic studies, as shown here, and also for the mega-analysis of cohorts generally.

EARLY CONSIDERATIONS OF GENETICS IN APHASIA REHABILITATION: A NARRATIVE REVIEW.

Background: Early investigations linking language and genetics were focused on the evolution of human communication in populations with developmental speech and language disorders. Recently, studies suggest that genes may also modulate recovery from post-stroke aphasia. Aims: Our goal is to review current literature related to the influence of genetics on post-stroke recovery, and the implications for aphasia rehabilitation. We describe candidate genes implicated by empirical findings and address additional clinical considerations. Main Contribution: We describe existing evidence and mechanisms supporting future investigations into how genetic factors may modulate aphasia recovery and propose that two candidate genes, brain derived neurotrophic factor (BDNF) and apolipoprotein E (APOE), may be important considerations for future research assessing response to aphasia treatment. Evidence suggests that BDNF is important for learning, memory, and neuroplasticity. APOE influences cognitive functioning and memory in older individuals and has also been implicated in neural repair. Moreover, recent data suggest an interaction between specific alleles of the BDNF and APOE genes in influencing episodic memory. Conclusions: Genetic influences on recovery from aphasia have been largely unexplored in the literature despite evidence that genetic factors influence behaviour and recovery from brain injury. As researchers continue to explore prognostic factors that may influence response to aphasia treatment, it is time for genetic factors to be considered as a source of variability. As the field moves in the direction of personalized medicine, eventually allied health professionals may utilize genetic profiles to inform treatment decisions and education for patients and care partners.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics.

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

Invasive or More Direct Measurements Can Provide an Objective Early-Stopping Ceiling for Training Deep Neural Networks on Non-invasive or Less-Direct Biomedical Data.

Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting.

Common genetic risk factors in ASD and ADHD co-occurring families.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders.

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3' untranslated region (3' UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3' UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein-protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies.

Improvement of automated analysis of coronary Doppler echocardiograms.

Coronary artery disease is the leading cause of heart disease, and while it can be assessed through transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze coronary flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles. The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program's accuracy. The goal of this current study was to refine the existing automation algorithm and heuristics by (1) moving the program to a Python environment, (2) increasing the program's ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases.

MiR-29b is associated with perinatal inflammation in extremely preterm infants.

BACKGROUND: Inflammation is strongly associated with premature birth and neonatal morbidities. Increases in infant haptoglobin, haptoglobin-related protein (Hp&HpRP), and interleukin-6 (IL-6) levels are indicators of intra-amniotic inflammation (IAI) and have been linked to poor neonatal outcomes. Inflammation causes epigenetic changes, specifically suppression of miR-29 expression. The current study sought to determine whether miR-29b levels in cord blood or neonatal venous blood are associated with IAI, identified by elevated IL-6 and Hp, and subsequent clinical morbidities in the infant. METHODS: We tested 92 cord blood samples from premature newborns and 18 venous blood samples at 36 weeks corrected gestational age. MiR-29b, Hp&HpRP, and IL-6 were measured by polymerase chain reaction and enzyme-linked immunosorbent assay, respectively. RESULTS: Decreased levels of miR-29b were observed in infants exposed to IAI with elevated Hp&HpRP and IL-6 levels and in infants delivered by spontaneous preterm birth. Lower miR-29 levels were also observed in women diagnosed with histological chorioamnionitis or funisitis and in infants with cerebral palsy. Higher levels of miR-29 were measured in infants small for gestational age and in venous samples from older infants. CONCLUSIONS: MiR-29 may be an additional biomarker of IAI and a potential therapeutic target for treating poor newborn outcomes resulting from antenatal exposure to IAI. IMPACT: Decreases in miR-29b are associated with intrauterine inflammation. Hp&HpRP increases are associated with decreased miR-29b. MiR-29b may be an additional biomarker for neonatal outcomes and a potential therapeutic target for intrauterine inflammation.

Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features.

Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization collecting data makes unique decisions on instruments, protocols, and methods of processing the data. This typically denies literal concatenation of the raw data since constituent cohorts do not have the same measurements (i.e., columns of data). When measurements across datasets are similar prima facie, there is a desire to combine the data to increase power, but mixing non-identical measurements could greatly reduce the sensitivity of the downstream analysis. Here, we discuss a statistical method that is applicable when certain patterns of missing data are found; namely, it is possible to combine datasets that measure the same underlying constructs (or latent traits) when there is only partial overlap of measurements across the constituent datasets. Our method, ROSETTA empirically derives a set of common latent trait metrics for each related measurement domain using a novel variation of factor analysis to ensure equivalence across the constituent datasets. The advantage of combining datasets this way is the simplicity, statistical power, and modeling flexibility of a single joint analysis of all the data. Three simulation studies show the performance of ROSETTA on datasets with only partially overlapping measurements (i.e., systematically missing information), benchmarked to a condition of perfectly overlapped data (i.e., full information). The first study examined a range of correlations, while the second study was modeled after the observed correlations in a well-characterized clinical, behavioral cohort. Both studies consistently show significant correlations >0.94, often >0.96, indicating the robustness of the method and validating the general approach. The third study varied within and between domain correlations and compared ROSETTA to multiple imputation and meta-analysis as two commonly used methods that ostensibly solve the same data integration problem. We provide one alternative to meta-analysis and multiple imputation by developing a method that statistically equates similar but distinct manifest metrics into a set of empirically derived metrics that can be used for analysis across all datasets.

Outcomes following a comprehensive versus a selective approach for infants born at 22 weeks of gestation.

OBJECTIVE: To examine outcomes at two institutions with different approaches to care among infants born at 22 weeks of gestation. STUDY DESIGN: Retrospective, cohort study (2006-2015). Enrollment was limited to mother-infant dyads at 22 weeks of gestation. Proactive care was defined as provision of antenatal corticosteroids and neonatal resuscitation and intensive care. One center (Uppsala, Sweden; UUCH) provided proactive care to all mother-infant dyads (comprehensive center); the other center (Nationwide Children's Hospital, USA; NCH) initiated or withheld treatment based on physician and family preferences (selective center). Differences in outcomes between the two centers were evaluated. RESULT: Among 112 live-born infants at 22 weeks of gestation, those treated at UUCH had in-hospital survival rates higher than those at NCH (21/40, 53% vs. 6/72, 8%; P < 0.01). Among the subgroup of infants receiving proactive care (UUCH: 40/40, 100%; NCH: 16/72, 22%) survival was higher at UUCH than at NCH (21/40, 53% vs. 3/16, 19%; P < 0.05). CONCLUSION: Even when mother-infant dyads were provided proactive care at NCH (selective center), survival was lower than infants provided proactive care at UUCH (comprehensive center). Differences between the approaches to care at the two centers at 22 weeks of gestation merits further investigation.

Understanding developmental language disorder - the Helsinki longitudinal SLI study (HelSLI): a study protocol.

BACKGROUND: Developmental language disorder (DLD, also called specific language impairment, SLI) is a common developmental disorder comprising the largest disability group in pre-school-aged children. Approximately 7% of the population is expected to have developmental language difficulties. However, the specific etiological factors leading to DLD are not yet known and even the typical linguistic features appear to vary by language. We present here a project that investigates DLD at multiple levels of analysis and aims to make the reliable prediction and early identification of the difficulties possible. Following the multiple deficit model of developmental disorders, we investigate the DLD phenomenon at the etiological, neural, cognitive, behavioral, and psychosocial levels, in a longitudinal study of preschool children. METHODS: In January 2013, we launched the Helsinki Longitudinal SLI study (HelSLI) at the Helsinki University Hospital ( http://tiny.cc/HelSLI ). We will study 227 children aged 3-6 years with suspected DLD and their 160 typically developing peers. Five subprojects will determine how the child's psychological characteristics and environment correlate with DLD and how the child's well-being relates to DLD, the characteristics of DLD in monolingual versus bilingual children, nonlinguistic cognitive correlates of DLD, electrophysiological underpinnings of DLD, and the role of genetic risk factors. Methods include saliva samples, EEG, computerized cognitive tasks, neuropsychological and speech and language assessments, video-observations, and questionnaires. DISCUSSION: The project aims to increase our understanding of the multiple interactive risk and protective factors that affect the developing heterogeneous cognitive and behavioral profile of DLD, including factors affecting literacy development. This accumulated knowledge will form a heuristic basis for the development of new interventions targeting linguistic and non-linguistic aspects of DLD.

Behavioral and Molecular Genetics of Reading-Related AM and FM Detection Thresholds.

Auditory detection thresholds for certain frequencies of both amplitude modulated (AM) and frequency modulated (FM) dynamic auditory stimuli are associated with reading in typically developing and dyslexic readers. We present the first behavioral and molecular genetic characterization of these two auditory traits. Two extant extended family datasets were given reading tasks and psychoacoustic tasks to determine FM 2 Hz and AM 20 Hz sensitivity thresholds. Univariate heritabilities were significant for both AM (h 2  = 0.20) and FM (h 2  = 0.29). Bayesian posterior probability of linkage (PPL) analysis found loci for AM (12q, PPL = 81 %) and FM (10p, PPL = 32 %; 20q, PPL = 65 %). Bivariate heritability analyses revealed that FM is genetically correlated with reading, while AM was not. Bivariate PPL analysis indicates that FM loci (10p, 20q) are not also associated with reading.

A Review on Genomics APIs.

The constant improvement and falling prices of whole human genome Next Generation Sequencing (NGS) has resulted in rapid adoption of genomic information at both clinics and research institutions. Considered together, the complexity of genomics data, due to its large volume and diversity along with the need for genomic data sharing, has resulted in the creation of Application Programming Interface (API) for secure, modular, interoperable access to genomic data from different applications, platforms, and even organizations. The Genomics APIs are a set of special protocols that assist software developers in dealing with multiple genomic data sources for building seamless, interoperable applications leading to the advancement of both genomic and clinical research. These APIs help define a standard for retrieval of genomic data from multiple sources as well as to better package genomic information for integration with Electronic Health Records. This review covers three currently available Genomics APIs: a) Google Genomics, b) SMART Genomics, and c) 23andMe. The functionalities, reference implementations (if available) and authentication protocols of each API are reviewed. A comparative analysis of the different features across the three APIs is provided in the Discussion section. Though Genomics APIs are still under active development and have yet to reach widespread adoption, they hold the promise to make building of complicated genomics applications easier with downstream constructive effects on healthcare.

Evidence for the multiple hits genetic theory for inherited language impairment: a case study.

Communication disorders have complex genetic origins, with constellations of relevant gene markers that vary across individuals. Some genetic variants are present in healthy individuals as well as those affected by developmental disorders. Growing evidence suggests that some variants may increase susceptibility to these disorders in the presence of other pathogenic gene mutations. In the current study, we describe eight children with specific language impairment and four of these children had a copy number variant in one of these potential susceptibility regions on chromosome 15. Three of these four children also had variants in other genes previously associated with language impairment. Our data support the theory that 15q11.2 is a susceptibility region for developmental disorders, specifically language impairment.

Molecular Genetic Evidence for Shared Etiology of Autism and Prodigy.

Child prodigies are rare individuals with an exceptional working memory and unique attentional skills that may facilitate the attainment of professional skill levels at an age well before what is observed in the general population. Some characteristics of prodigy have been observed to be quantitatively similar to those observed in autism spectrum disorder (ASD), suggesting possible shared etiology, though objectively validated prodigies are so rare that evidence has been sparse. We performed a family-based genome-wide linkage analysis on 5 nuclear and extended families to search for genetic loci that influence the presence of both prodigy and ASD, assuming that the two traits have the same genetic etiology in the analysis model in order to find shared loci. A shared locus on chromosome 1p31-q21 reached genome-wide significance with two extended family-based linkage methods consisting of the Bayesian PPL method and the LOD score maximized over the trait parameters (i.e., MOD), yielding a simulation-based empirical significance of p = 0.000742 and p = 0.000133, respectively. Within linkage regions, we performed association analysis and assessed if copy number variants could account for the linkage signal. No evidence of specificity for either the prodigy or the ASD trait was observed. This finding suggests that a locus on chromosome 1 increases the likelihood of both prodigy and autism in these families.

An integrative analysis of regional gene expression profiles in the human brain.

Studies of the brain's transcriptome have become prominent in recent years, resulting in an accumulation of datasets with somewhat distinct attributes. These datasets, which are often analyzed only in isolation, also are often collected with divergent goals, which are reflected in their sampling properties. While many researchers have been interested in sampling gene expression in one or a few brain areas in a large number of subjects, recent efforts from the Allen Institute for Brain Sciences and others have focused instead on dense neuroanatomical sampling, necessarily limiting the number of individual donor brains studied. The purpose of the present work is to develop methods that draw on the complementary strengths of these two types of datasets for study of the human brain, and to characterize the anatomical specificity of gene expression profiles and gene co-expression networks derived from human brains using different specific technologies. The approach is applied using two publicly accessible datasets: (1) the high anatomical resolution Allen Human Brain Atlas (AHBA, Hawrylycz et al., 2012) and (2) a relatively large sample size, but comparatively coarse neuroanatomical dataset described previously by Gibbs et al. (2010). We found a relatively high degree of correspondence in differentially expressed genes and regional gene expression profiles across the two datasets. Gene co-expression networks defined in individual brain regions were less congruent, but also showed modest anatomical specificity. Using gene modules derived from the Gibbs dataset and from curated gene lists, we demonstrated varying degrees of anatomical specificity based on two classes of methods, one focused on network modularity and the other focused on enrichment of expression levels. Two approaches to assessing the statistical significance of a gene set's modularity in a given brain region were studied, which provide complementary information about the anatomical specificity of a gene network of interest. Overall, the present work demonstrates the feasibility of cross-dataset analysis of human brain microarray studies, and offers a new approach to annotating gene lists in a neuroanatomical context.

Collection and extraction of saliva DNA for next generation sequencing.

The preferred source of DNA in human genetics research is blood, or cell lines derived from blood, as these sources yield large quantities of high quality DNA. However, DNA extraction from saliva can yield high quality DNA with little to no degradation/fragmentation that is suitable for a variety of DNA assays without the expense of a phlebotomist and can even be acquired through the mail. However, at present, no saliva DNA collection/extraction protocols for next generation sequencing have been presented in the literature. This protocol optimizes parameters of saliva collection/storage and DNA extraction to be of sufficient quality and quantity for DNA assays with the highest standards, including microarray genotyping and next generation sequencing.

Understanding the sequence requirements of protein families: insights from the BioVis 2013 contests.

INTRODUCTION: In 2011, the BioVis symposium of the IEEE VisWeek conferences inaugurated a new variety of data analysis contest. Aimed at fostering collaborations between computational scientists and biologists, the BioVis contest provided real data from biological domains with emerging visualization needs, in the hope that novel approaches would result in powerful new tools for the community. In 2011 and 2012 the theme of these contests was expression Quantitative Trait Locus analysis, within and across tissues respectively. In 2013 the topic was updated to protein sequence and mutation visualization. METHODS: The contest was framed in the context of a real protein with numerous mutations that had lost function, and the question posed "what minimal set of changes would you propose to rescue function, or how could you support a biologist attempting to answer that question?". The data was grounded in actual experimental results in triosephosphate isomerase(TIM) enzymes. Seven teams composed of 36 individuals submitted entries with proposed solutions and approaches to the challenge. Their contributions ranged from careful analysis of the visualization and analytical requirements for the problem through integration of existing tools for analyzing the context and consequences of protein mutations, to completely new tools addressing the problem. RESULTS: Judges found valuable and novel contributions in each of the entries, including interesting ways to hierarchicalize the protein into domains of informational interaction, tools for simultaneously understanding both sequential and spatial order, and approaches for conveying some types of inter-residue dependencies. In this manuscript we document the problem presented to the contestants, summarize the biological contributions of their entries, and suggest opportunities that this work has highlighted for even more improved tools in the future.

Do the symptoms of language disorder align with treatment goals? An exploratory study of primary-grade students' IEPs.

UNLABELLED: Individual Education Plans (IEPs) are legal documents that guide the treatment of students with language disorder (LD). This exploratory study investigated the extent to which students' symptoms of LD align with goals on their IEPs. A total of 99 kindergarten and first-grade students receiving treatment for LD in the public schools participated. IEPs were collected and coded for each student and norm-referenced measures were used to assess students' grammar, vocabulary, listening comprehension, and literacy skills in the fall of the academic year. Results showed there to be alignment between students' symptoms and IEP goals only in the area of vocabulary, such that students who had an IEP goal for vocabulary had lower scores on a vocabulary assessment than those without a goal. In general, there is limited alignment between observed symptoms of LD and treated symptoms as identified on students' IEPs. The limited alignment found in this study suggests more investigation is needed to understand the extent to which IEP goals, as potential indicators of treatment foci, should map on to students' symptoms. LEARNING OUTCOMES: Readers will be able to: (1) explain the theoretical and practical relevance of treatment goals aligning to symptoms for children with language impairment; (2) identify three analytic methods used to investigate alignment between treatment goals and symptoms; and (3) describe the extent to which IEP goals align to children's symptoms in a sample of children receiving services in the public schools.