Varied performance of picture description task as a screening tool across MCI subtypes

A picture description task is a component of Miro Health's platform for self-administration of neurobehavioral assessments. Picture description has been used as a screening tool for identification of individuals with Alzheimer's disease and mild cognitive impairment (MCI), but currently requires in-person administration and scoring by someone with access to and familiarity with a scoring rubric. The Miro Health implementation allows broader use of this assessment through self-administration and automated processing, analysis, and scoring to deliver clinically useful quantifications of the users' speech production, vocal characteristics, and language. Picture description responses were collected from 62 healthy controls (HC), and 33 participants with MCI: 18 with amnestic MCI (aMCI) and 15 with non-amnestic MCI (naMCI). Speech and language features and contrasts between pairs of features were evaluated for differences in their distributions in the participant subgroups. Picture description features were selected and combined using penalized logistic regression to form risk scores for classification of HC versus MCI as well as HC versus specific MCI subtypes. A picture-description based risk score distinguishes MCI and HC with an area under the receiver operator curve (AUROC) of 0.74. When contrasting specific subtypes of MCI and HC, the classifiers have an AUROC of 0.88 for aMCI versus HC and and AUROC of 0.61 for naMCI versus HC. Tests of association of individual features or contrasts of pairs of features with HC versus aMCI identified 20 features with p-values below 5e-3 and False Discovery Rates (FDRs) at or below 0.113, and 61 contrasts with p-values below 5e-4 and FDRs at or below 0.132. Findings suggest that performance of picture description as a screening tool for MCI detection will vary greatly by MCI subtype or by the proportion of various subtypes in an undifferentiated MCI population.

Validation of a Mobile, Sensor-based Neurobehavioral Assessment With Digital Signal Processing and Machine-learning Analytics

BACKGROUND

The Miro Health Mobile Assessment Platform consists of self-administered neurobehavioral and cognitive assessments that measure behaviors typically measured by specialized clinicians.

OBJECTIVE

To evaluate the Miro Health Mobile Assessment Platform's concurrent validity, test-retest reliability, and mild cognitive impairment (MCI) classification performance.

METHOD

Sixty study participants were evaluated with Miro Health version V.2. Healthy controls (HC), amnestic MCI (aMCI), and nonamnestic MCI (naMCI) ages 64-85 were evaluated with version V.3. Additional participants were recruited at Johns Hopkins Hospital to represent clinic patients, with wider ranges of age and diagnosis. In all, 90 HC, 21 aMCI, 17 naMCI, and 15 other cases were evaluated with V.3. Concurrent validity of the Miro Health variables and legacy neuropsychological test scores was assessed with Spearman correlations. Reliability was quantified with the scores' intraclass correlations. A machine-learning algorithm combined Miro Health variable scores into a Risk score to differentiate HC from MCI or MCI subtypes.

RESULTS

In HC, correlations of Miro Health variables with legacy test scores ranged 0.27-0.68. Test-retest reliabilities ranged 0.25-0.79, with minimal learning effects. The Risk score differentiated individuals with aMCI from HC with an area under the receiver operator curve (AUROC) of 0.97; naMCI from HC with an AUROC of 0.80; combined MCI from HC with an AUROC of 0.89; and aMCI from naMCI with an AUROC of 0.83.

CONCLUSION

The Miro Health Mobile Assessment Platform provides valid and reliable assessment of neurobehavioral and cognitive status, effectively distinguishes between HC and MCI, and differentiates aMCI from naMCI.

A Large-Scale Association Study Detects Novel Rare Variants, Risk Genes, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th-60th percentile OR = 2.62, P = 2.55 × 10-191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. SIGNIFICANCE: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer.See related commentary by Lachance, p. 1637.

GBAT: a gene-based association test for robust detection of trans-gene regulation

The observation that disease-associated genetic variants typically reside outside of exons has inspired widespread investigation into the genetic basis of transcriptional regulation. While associations between the mRNA abundance of a gene and its proximal SNPs (cis-eQTLs) are now readily identified, identification of high-quality distal associations (trans-eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-positive signals. To address these issues, we develop GBAT, a powerful gene-based pipeline that allows robust detection of high-quality trans-gene regulation signal.

Mutational Landscape of Aggressive Prostate Tumors in African American Men

Prostate cancer is the most frequently diagnosed and second most fatal nonskin cancer among men in the United States. African American men are two times more likely to develop and die of prostate cancer compared with men of other ancestries. Previous whole genome or exome tumor-sequencing studies of prostate cancer have primarily focused on men of European ancestry. In this study, we sequenced and characterized somatic mutations in aggressive (Gleason ≥7, stage ≥T2b) prostate tumors from 24 African American patients. We describe the locations and prevalence of small somatic mutations (up to 50 bases in length), copy number aberrations, and structural rearrangements in the tumor genomes compared with patient-matched normal genomes. We observed several mutation patterns consistent with previous studies, such as large copy number aberrations in chromosome 8 and complex rearrangement chains. However, TMPRSS2-ERG gene fusions and PTEN losses occurred in only 21% and 8% of the African American patients, respectively, far less common than in patients of European ancestry. We also identified mutations that appeared specific to or more common in African American patients, including a novel CDC27-OAT gene fusion occurring in 17% of patients. The genomic aberrations reported in this study warrant further investigation of their biologic significant role in the incidence and clinical outcomes of prostate cancer in African Americans. Cancer Res; 76(7); 1860-8. ©2016 AACR.

Detecting gene-environment interactions in human birth defects: Study designs and statistical methods

BACKGROUND

The National Birth Defects Prevention Study (NBDPS) contains a wealth of information on affected and unaffected family triads, and thus provides numerous opportunities to study gene-environment interactions (G×E) in the etiology of birth defect outcomes. Depending on the research objective, several analytic options exist to estimate G×E effects that use varying combinations of individuals drawn from available triads.

METHODS

In this study, we discuss important considerations in the collection of genetic data and environmental exposures.

RESULTS

We will also present several population- and family-based approaches that can be applied to data from the NBDPS including case-control, case-only, family-based trio, and maternal versus fetal effects. For each, we describe the data requirements, applicable statistical methods, advantages, and disadvantages.

CONCLUSION

A range of approaches can be used to evaluate potentially important G×E effects in the NBDPS. Investigators should be aware of the limitations inherent to each approach when choosing a study design and interpreting results.

Genetic variants in transcription factors are associated with the pharmacokinetics and pharmacodynamics of metformin

One-third of type 2 diabetes patients do not respond to metformin. Genetic variants in metformin transporters have been extensively studied as a likely contributor to this high failure rate. Here, we investigate, for the first time, the effect of genetic variants in transcription factors on metformin pharmacokinetics (PK) and response. Overall, 546 patients and healthy volunteers contributed their genome-wide, pharmacokinetic (235 subjects), and HbA1c data (440 patients) for this analysis. Five variants in specificity protein 1 (SP1), a transcription factor that modulates the expression of metformin transporters, were associated with changes in treatment HbA1c (P < 0.01) and metformin secretory clearance (P < 0.05). Population pharmacokinetic modeling further confirmed a 24% reduction in apparent clearance in homozygous carriers of one such variant, rs784888. Genetic variants in other transcription factors, peroxisome proliferator-activated receptor-α and hepatocyte nuclear factor 4-α, were significantly associated with HbA1c change only. Overall, our study highlights the importance of genetic variants in transcription factors as modulators of metformin PK and response.

Association of the innate immunity and inflammation pathway with advanced prostate cancer risk

Prostate cancer is the most frequent and second most lethal cancer in men in the United States. Innate immunity and inflammation may increase the risk of prostate cancer. To determine the role of innate immunity and inflammation in advanced prostate cancer, we investigated the association of 320 single nucleotide polymorphisms, located in 46 genes involved in this pathway, with disease risk using 494 cases with advanced disease and 536 controls from Cleveland, Ohio. Taken together, the whole pathway was associated with advanced prostate cancer risk (P = 0.02). Two sub-pathways (intracellular antiviral molecules and extracellular pattern recognition) and four genes in these sub-pathways (TLR1, TLR6, OAS1, and OAS2) were nominally associated with advanced prostate cancer risk and harbor several SNPs nominally associated with advanced prostate cancer risk. Our results suggest that the innate immunity and inflammation pathway may play a modest role in the etiology of advanced prostate cancer through multiple small effects.

Joint association testing of common and rare genetic variants using hierarchical modeling

New sequencing technologies provide an opportunity for assessing the impact of rare and common variants on complex diseases. Several methods have been developed for evaluating rare variants, many of which use weighted collapsing to combine rare variants. Some approaches require arbitrary frequency thresholds below which to collapse alleles, and most assume that effect sizes for each collapsed variant are either the same or a function of minor allele frequency. Some methods also further assume that all rare variants are deleterious rather than protective. We expect that such assumptions will not hold in general, and as a result performance of these tests will be adversely affected. We propose a hierarchical model, implemented in the new program CHARM, to detect the joint signal from rare and common variants within a genomic region while properly accounting for linkage disequilibrium between variants. Our model explores the scale, rather than the center of the odds ratio distribution, allowing for both causative and protective effects. We use cross-validation to assess the evidence for association in a region. We use model averaging to widen the range of disease models under which we will have good power. To assess this approach, we simulate data under a range of disease models with effects at common and/or rare variants. Overall, our method had more power than other well-known rare variant approaches; it performed well when either only rare, or only common variants were causal, and better than other approaches when both common and rare variants contributed to disease.

Enumerating the gene sets in breast cancer, a "direct" alternative to hierarchical clustering

Background

Two-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values.

Results

Applied to four large breast cancer datasets, this alternative exploratory method detects more than thirty sets of co-regulated genes, many of which are conserved across experiments and across platforms. Many of these sets are readily identified in biological terms, e.g., "estrogen", "erbb2", and 8p11-12, and several are clinically significant as prognostic of either increased survival ("adipose", "stromal"...) or diminished survival ("proliferation", "immune/interferon", "histone",...). Of special interest are the sets that effectively factor "immune response" and "stromal signalling".

Conclusion

The gene sets induced by the enumeration include many of the sets reported in the literature. In this regard these inventories confirm and consolidate findings from microarray-based work on breast cancer over the last decade. But, the enumerations also identify gene sets that have not been studied as of yet, some of which are prognostic of survival. The sets induced are robust, biologically meaningful, and serve to reveal a finer structure in existing breast cancer microarrays.