Extraction of Treatment Information From Electronic Health Records and Evaluation of Testosterone Recovery in Patients With Prostate Cancer

PURPOSE

Data quality and standardization remain a challenge when analyzing real-world clinical data. We built a clinical research database, using machine learning and natural learning processing, and investigated factors influencing testosterone recovery (T-recovery) in patients with localized prostate cancer (LPC) after initial androgen deprivation therapy (ADT).

METHODS

Medication and treatment-associated dates missing in structured tables were extracted from patient notes using ConceptMapper, an automated data extraction tool, standardized and curated in Sema4 clinical research database. ADT usage duration was evaluated, and T-recovery in patients with LPC was analyzed by the Kaplan-Meier method and multivariable Cox proportional hazards models. We assessed the prognostic value of post-ADT T-recovery with prostate-specific antigen progression-free survival and failure-free survival.

RESULTS

In total, 4,125 of 30,832 (13.4%) patients with prostate cancer had medication exclusively from notes with high precision and recall, F1 score ≥ 0.95. Association of dates with medication usage had a F1 score of 0.76. ADT duration estimation had higher accuracy combining information from notes to tables from electronic medical record (70% v 45%). Baseline testosterone was the strongest predictor of T-recovery in these patients. Patients with a baseline testosterone ≥ 300 ng/dL recovered in 9.79 versus 38 months for patients with baseline testosterone < 300 ng/dL (P < .0001). Shorter prostate-specific antigen progression-free interval was observed for patients with T-recovery (≥ 300 ng/dL) at 6 months after ADT cessation compared with patients without T-recovery (< 300 ng/dL; 13.7 v 25.1 months; P = .055).

CONCLUSION

We augmented structured electronic medical record data with data extracted from notes and improved the accuracy of medication information for patients. ADT exposure and T-recovery in patients with LPC produced results consistent with the literature and clinical experience and illustrates the power of applying machine learning methods to enhance the quality of real-world evidence in answering clinically relevant questions.

Abstract P1-17-06: Impact of body mass index on the efficacy of aromatase inhibitors in patients with metastatic breast cancer

Background: Aromatase inhibitors (AIs) are part of standard endocrine therapy for hormone receptor (HR)-positive breast cancer (BC) and are used in both adjuvant and metastatic settings. Since AIs work by inhibiting the conversion of androgens to estrogen in peripheral adipose tissue, the higher levels of estrogen in obese patients may lead to incomplete inhibition by AIs and influence their efficacy. A retrospective analysis of the ABCSG-12 trial found that overweight premenopausal patients with early-stage BC treated with anastrozole had a 60% increased risk of disease recurrence and more than doubling in risk of death compared with normal weight patients on anastrozole. Similarly, an exploratory analysis from the ATAC trial found that in post-menopausal women with early-stage BC, overall recurrence rates were lower in patients randomized to anastrozole versus tamoxifen but in women with body mass index (BMI) &gt; 30, there was no significant difference in disease recurrence between anastrozole and tamoxifen. While these findings raise concern for the efficacy of adjuvant AI therapy in obese patients with early-stage BC, this has not yet been demonstrated in the metastatic setting. The aim of this study was to determine the impact of BMI on efficacy of AIs in patients with metastatic HR-positive BC. Methods: We performed a retrospective chart review of all female patients with metastatic HR-positive BC on an AI in first- or second-line settings and seen at our academic institution between 2001-2020. The primary endpoint was progression-free survival (PFS), defined as the time from start of AI to disease progression or death from any cause. PFS was compared across BMI groups using Kaplan-Meier curves and log-rank tests. Cox proportional hazards regression model was used for univariate and multivariate analyses. Results: We identified 219 patients who had received an AI in the first- or second-line settings for metastatic HR-positive BC and with documented information on BMI. The median age was 59 with 45% of patients White, 29% African American, 16% Hispanic/Latino, 5.5% Asian and remainder other/unknown. 32% (71) had HER-2 positive disease. 82% (179) were on an AI in the first-line setting. Overall, 53% were on letrozole, 42% on anastrozole and 5.5% on exemestane. Of the 219 patients, 56% (123) had a low BMI (defined as &lt; 27 kg/m2) and 44% (96) had a high BMI (≥ 27 kg/m2; based on the Breast Cancer Weight Loss [BWEL] trial). The median PFS was 21.9 months (95% CI, 14.5 to 28.4) in the low BMI group versus 20.2 months (95% CI, 14.3 to 27.5) in the high BMI group with no statistically significant difference (p =0.73). There were 8 (6.5%) deaths in the low BMI group and 7 (7.3%) deaths in the high BMI group. Multivariate cox regression model did not demonstrate any significant impact of BMI on PFS when adjusting for age, race/ethnicity, HER2 status, type of AI, line of therapy, drug partner and type of metastatic disease (HR =0.91, 95% CI =0.64 to 1.30, p =0.6 for high BMI group). Subgroup analysis of patients on an AI in the first line setting also did not show a significant difference in PFS with median PFS 19.3 and 18 months in the low and high BMI groups, respectively. Conclusions: In patients on an AI for metastatic HR-positive breast cancer, there was no statistically significant difference in PFS in patients with low versus high BMI. While BMI influences efficacy of AIs in the adjuvant setting, our results demonstrate that in the metastatic setting, BMI does not significantly impact the efficacy of AIs among our patient population. This discrepancy could be due to other differences in disease characteristics that make complete aromatase inhibition more important in the adjuvant setting when disease burden is the lowest. Additional larger studies are needed to confirm this finding.

Citation Format: Rima Patel, Zhiqiang Li, Brittney S. Zimmerman, Marc Y. Fink, Jason D. Wells, Xiang Zhou, Kristin L. Ayers, Arielle Redfern, Scott Newman, Rong Chen, William K. Oh, Amy Tiersten. Impact of body mass index on the efficacy of aromatase inhibitors in patients with metastatic breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-17-06.

OUP accepted manuscript

Introduction

Methods

Results

Conclusion

Human GPR17 missense variants identified in metabolic disease patients have distinct downstream signaling profiles

GPR17 is a G-protein-coupled receptor (GPCR) implicated in the regulation of glucose metabolism and energy homeostasis. Such evidence is primarily drawn from mouse knockout studies and suggests GPR17 as a potential novel therapeutic target for the treatment of metabolic diseases. However, links between human GPR17 genetic variants, downstream cellular signaling, and metabolic diseases have yet to be reported. Here, we analyzed GPR17 coding sequences from control and disease cohorts consisting of individuals with adverse clinical metabolic deficits including severe insulin resistance, hypercholesterolemia, and obesity. We identified 18 nonsynonymous GPR17 variants, including eight variants that were exclusive to the disease cohort. We characterized the protein expression levels, membrane localization, and downstream signaling profiles of nine GPR17 variants (F43L, V96M, V103M, D105N, A131T, G136S, R248Q, R301H, and G354V). These nine GPR17 variants had similar protein expression and subcellular localization as wild-type GPR17; however, they showed diverse downstream signaling profiles. GPR17-G136S lost the capacity for agonist-mediated cAMP, Ca²⁺, and β-arrestin signaling. GPR17-V96M retained cAMP inhibition similar to GPR17-WT, but showed impaired Ca²⁺ and β-arrestin signaling. GPR17-D105N displayed impaired cAMP and Ca²⁺ signaling, but unaffected agonist-stimulated β-arrestin recruitment. The identification and functional profiling of naturally occurring human GPR17 variants from individuals with metabolic diseases revealed receptor variants with diverse signaling profiles, including differential signaling perturbations that resulted in GPCR signaling bias. Our findings provide a framework for structure–function relationship studies of GPR17 signaling and metabolic disease.

Analysis of Real-World Data to Investigate the Impact of Race and Ethnicity on Response to Programmed Cell Death-1 and Programmed Cell Death-Ligand 1 Inhibitors in Advanced Non-Small Cell Lung Cancers

Background

Racial disparities among clinical trial participants present a challenge to assess whether trial results can be generalized into patients representing diverse races and ethnicities. The objective of this study was to evaluate the impact of race and ethnicity on treatment response in patients with advanced non‐small cell lung cancer (aNSCLC) treated with programmed cell death‐1 (PD‐1) or programmed cell death‐ligand 1 (PD‐L1) inhibitors through analysis of real‐world data (RWD).

Materials and Methods

A retrospective cohort study of 11,138 patients with lung cancer treated at hospitals within the Mount Sinai Health System was performed. Patients with confirmed aNSCLC who received anti‐PD‐1/PD‐L1 treatment were analyzed for clinical outcomes. Our cohort included 249 patients with aNSCLC who began nivolumab, pembrolizumab, or atezolizumab treatment between November 2014 and December 2018. Time‐to‐treatment discontinuation (TTD) and overall survival (OS) were the analyzed clinical endpoints.

Results

After a median follow‐up of 14.8 months, median TTD was 7.8 months (95% confidence interval, 5.4–not estimable [NE]) in 75 African American patients versus 4.6 (2.4–7.2) in 110 White patients (hazard ratio [HR], 0.63). Median OS was not reached (18.4–NE) in African American patients versus 11.6 months (9.7–NE) in White patients (HR, 0.58). Multivariable Cox regression conducted with potential confounders confirmed longer TTD (adjusted HR, 0.65) and OS (adjusted HR, 0.60) in African American versus White patients. Similar real‐world response rate (42.6% vs. 43.5%) and disease control rate (59.6% vs. 56.5%) were observed in the African American and White patient populations. Further investigation revealed the African American patient group had lower incidence (14.7%) of putative hyperprogressive diseases (HPD) upon anti‐PD‐1/PD‐L1 treatment than the White patient group (24.5%).

Conclusion

Analysis of RWD showed longer TTD and OS in African American patients with aNSCLC treated with anti‐PD‐1/PD‐L1 inhibitors. Lower incidence of putative HPD is a possible reason for the favorable outcomes in this patient population.

Implications for Practice

There is a significant underrepresentation of minority patients in randomized clinical trials, and this study demonstrates that real‐world data can be used to investigate the impact of race and ethnicity on treatment response. In retrospective analysis of patients with advanced non‐small cell lung cancer treated with programmed cell death‐1 or programmed cell death‐ligand 1 inhibitors, African American patients had significantly longer time‐to‐treatment discontinuation and longer overall survival. Analysis of real‐world data can yield clinical insights and establish a more complete picture of medical interventions in routine clinical practice.

Racial disparities in clinical trials affect whether trial results can be generalized for diverse patient populations. This article evaluates real‐world data to assess the impact of race and ethnicity on treatment response in patients with advanced non‐small cell lung cancer treated with PD‐1 or PD‐L1 inhibitors.

Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22613-2

A composite biomarker of neutrophil-lymphocyte ratio and hemoglobin level correlates with clinical response to PD-1 and PD-L1 inhibitors in advanced non-small cell lung cancers

BACKGROUND

Immune checkpoint inhibitors (ICIs) have been incorporated into various clinical oncology guidelines for systemic treatment of advanced non-small cell lung cancers (aNSCLC). However, less than 50% (and 20%) of the patients responded to the therapy as a first (or second) line of therapy. PD-L1 immunohistochemistry (IHC) is an extensively studied biomarker of response to ICI, but results from this test have equivocal predictive power. In order to identify other biomarkers that support clinical decision-making around whether to treat with ICIs or not, we performed a retrospective study of patients with aNSCLC who underwent ICI-based therapy in the Mount Sinai Health System between 2014 and 2019.

METHODS

We analyzed data from standard laboratory tests performed in patients as a part of the routine clinical workup during treatment, including complete blood counts (CBC) and a comprehensive metabolic panel (CMP), to correlate test results with clinical response and survival.

RESULTS

Of 11,138 NSCLC patients identified, 249 had been treated with ICIs. We found associations between high neutrophil-to-lymphocyte ratio (NLR ≥ 5) and poor survival in ICI-treated NSCLC. We further observed that sustained high NLR after initiation of treatment had a more profound impact on survival than baseline NLR, regardless of PD-L1 status. Hazard ratios when comparing patients with NLR ≥ 5 vs. NLR < 5 are 1.7 (p = 0.02), 3.4 (p = 4.2 × 10^- 8), and 3.9 (p = 1.4 × 10^- 6) at baseline, 2-8 weeks, and 8-14 weeks after treatment start, respectively. Mild anemia, defined as hemoglobin (HGB) less than 12 g/dL was correlated with survival independently of NLR. Finally, we developed a composite NLR and HGB biomarker. Patients with pretreatment NLR ≥ 5 and HGB < 12 g/dL had a median overall survival (OS) of 8.0 months (95% CI 4.5-11.5) compared to the rest of the cohort with a median OS not reached (95% CI 15.9-NE, p = 1.8 × 10^- 5), and a hazard ratio of 2.6 (95% CI 1.7-4.1, p = 3.5 × 10^- 5).

CONCLUSIONS

We developed a novel composite biomarker for ICI-based therapy in NSCLC based on routine CBC tests, which may provide meaningful clinical utility to guide treatment decision. The results suggest that treatment of anemia to elevate HGB before initiation of ICI therapy may improve patient outcomes or the use of alternative non-chemotherapy containing regimens.

A meta-analysis of genome-wide association studies identifies multiple longevity genes

Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.

Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Background

Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene.

Results

We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight.

Conclusion

In sum, by integrating genetic and electronic medical record data, and leveraging one of the world’s largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0542-3) contains supplementary material, which is available to authorized users.

Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease characterised by progressive destruction of intrahepatic bile ducts. The strongest genetic association is with HLA-DQA1*04:01, but at least three additional independent HLA haplotypes contribute to susceptibility. We used dense single nucleotide polymorphism (SNP) data in 2861 PBC cases and 8514 controls to impute classical HLA alleles and amino acid polymorphisms using state-of-the-art methodologies. We then demonstrated through stepwise regression that association in the HLA region can be largely explained by variation at five separate amino acid positions. Three-dimensional modelling of protein structures and calculation of electrostatic potentials for the implicated HLA alleles/amino acid substitutions demonstrated a correlation between the electrostatic potential of pocket P6 in HLA-DP molecules and the HLA-DPB1 alleles/amino acid substitutions conferring PBC susceptibility/protection, highlighting potential new avenues for future functional investigation.

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease that exhibits strong genetic associations, especially with variants in the human leukocyte antigen (HLA) gene region. Here we use dense single nucleotide polymorphism (SNP) data from the largest PBC study to date (2861 cases, 8514 controls) to investigate the likely underlying causes of this association, via performing imputation of HLA classical alleles and amino acids. We show that the HLA association can be largely explained by variation at five separate amino acid positions, one of which shows functional relevance to electrostatic potentials of HLA-DP molecules.

Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

Melanocortin 4 Receptor Pathway Dysfunction in Obesity: Patient Stratification Aimed at MC4R Agonist Treatment

CONTEXT

The hypothalamic melanocortin 4 receptor (MC4R) pathway serves a critical role in regulating body weight. Loss of function (LoF) mutations in the MC4R pathway, including mutations in the pro-opiomelanocortin (POMC), prohormone convertase 1 (PCSK1), leptin receptor (LEPR), or MC4R genes, have been shown to cause early-onset severe obesity.

METHODS

Through a comprehensive epidemiological analysis of known and predicted LoF variants in the POMC, PCSK1, and LEPR genes, we sought to estimate the number of US individuals with biallelic MC4R pathway LoF variants.

RESULTS

We predict ~650 α-melanocyte-stimulating hormone (MSH)/POMC, 8500 PCSK1, and 3600 LEPR homozygous and compound heterozygous individuals in the United States, cumulatively enumerating >12,800 MC4R pathway-deficient obese patients. Few of these variants have been genetically diagnosed to date. These estimates increase when we include a small subset of less rare variants: β-MSH/POMC,PCSK1 N221D, and a PCSK1 LoF variant (T640A). To further define the MC4R pathway and its potential impact on obesity, we tested associations between body mass index (BMI) and LoF mutation burden in the POMC, PCSK1, and LEPR genes in various populations. We show that the cumulative allele burden in individuals with two or more LoF alleles in one or more genes in the MC4R pathway are predisposed to a higher BMI than noncarriers or heterozygous LoF carriers with a defect in only one gene.

CONCLUSIONS

Our analysis represents a genetically rationalized study of the hypothalamic MC4R pathway aimed at genetic patient stratification to determine which obese subpopulations should be studied to elucidate MC4R agonist (e.g., setmelanotide) treatment responsiveness.

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).

Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.

Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

A duplication in a patient with 46,XX ovo-testicular disorder of sex development refines the SOX9 testis-specific regulatory region to 24 kb

A custom CGH microarray that covers the SOX9 regulatory region. Log2 ratio scatterplot showing individual data points. Blue box highlights copy number gain with 3' breakpoint region magnified.

A loss of function variant in CASP7 protects against Alzheimer's disease in homozygous APOE ε4 allele carriers

Background

Alzheimer’s disease (AD) represents the most common form of dementia in elder populations with approximately 30 million cases worldwide. Genome wide genotyping and sequencing studies have identified many genetic variants associated with late-onset Alzheimer’s disease (LOAD). While most of these variants are associated with increased risk of developing LOAD, only limited number of reports focused on variants that are protective against the disease.

Methods

Here we applied a novel approach to uncover protective alleles against AD by analyzing genetic and phenotypic data in Mount Sinai Biobank and Electronic Medical Record (EMR) databases.

Results

We discovered a likely loss-of-function small deletion variant in the caspase 7 (CASP7) gene associated with significantly reduced incidence of LOAD in carriers of the high-risk APOE ε4 allele. Further investigation of four independent cohorts of European ancestry revealed the protective effect of the CASP7 variant against AD is most significant in homozygous APOE ε4 allele carriers. Meta analysis of multiple datasets shows overall odds ratio = 0.45 (p = 0.004). Analysis of RNA sequencing derived gene expression data indicated the variant correlates with reduced caspase 7 expression in multiple brain tissues we examined.

Conclusions

Taken together, these results are consistent with the notion that caspase 7 plays a key role in microglial activation driving neuro-degeneration during AD pathogenesis, and may explain the underlying genetic mechanisms that anti-inflammatory interventions in AD show greater benefit in APOE ε4 carriers than non-carriers. Our findings inform potential novel therapeutic opportunities for AD and warrant further investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2725-z) contains supplementary material, which is available to authorized users.

Exome sequencing in dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is the second most common form of degenerative dementia. Siblings of affected individuals are at greater risk of developing DLB, but little is known about the underlying genetic basis of the disease. We set out to determine whether mutations in known highly penetrant neurodegenerative disease genes are found in patients with DLB. Whole-exome sequencing was performed on 91 neuropathologically confirmed cases of DLB, supplemented by independent APOE genotyping. Genetic variants were classified using established criteria, and additional neuropathological examination was performed for putative mutation carriers. Likely pathogenic variants previously described as causing monogenic forms of neurodegenerative disease were found in 4.4% of patients with DLB. The APOE ɛ4 allele increased the risk of disease (P=0.0001), conferred a shorter disease duration (P=0.043) and earlier age of death (P=0.0015). In conclusion, although known pathogenic mutations in neurodegenerative disease genes are uncommon in DLB, known genetic risk factors are present in >60% of cases. APOE ɛ4 not only modifies disease risk, but also modulates the rate of disease progression. The reduced penetrance of reported pathogenic alleles explains the lack of a family history in most patients, and the presence of variants previously described as causing frontotemporal dementia suggests a mechanistic overlap between DLB and other neurodegenerative diseases.

Analysis of Genetic Analysis Workshop 18 data with gene-based penalized regression

Under the premise that multiple causal variants exist within a disease gene and that we are underpowered to detect these variants individually, a variety of methods have been developed that attempt to cluster rare variants within a gene so that the variants may gather strength from one another. These methods group variants by gene or proximity, and test one gene or marker window at a time. We propose analyzing all genes simultaneously with a penalized regression method that enables grouping of all (rare and common) variants within a gene while subgrouping rare variants, thus borrowing strength from both rare and common variants within the same gene. We apply this approach using a burden based weighting of the rare variants to the Genetic Analysis Workshop 18 data.

Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age

The genetic contribution to the variation in human lifespan is ∼25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥85 years) and 16 121 younger controls (<65 years) followed by replication in an additional set of 13 060 long-lived individuals and 61 156 controls. In addition, we performed a subset analysis in cases aged ≥90 years. We observed genome-wide significant association with longevity, as reflected by survival to ages beyond 90 years, at a novel locus, rs2149954, on chromosome 5q33.3 (OR = 1.10, P = 1.74 × 10⁻⁸). We also confirmed association of rs4420638 on chromosome 19q13.32 (OR = 0.72, P = 3.40 × 10⁻³⁶), representing the TOMM40/APOE/APOC1 locus. In a prospective meta-analysis (n = 34 103), the minor allele of rs2149954 (T) on chromosome 5q33.3 associates with increased survival (HR = 0.95, P = 0.003). This allele has previously been reported to associate with low blood pressure in middle age. Interestingly, the minor allele (T) associates with decreased cardiovascular mortality risk, independent of blood pressure. We report on the first GWAS-identified longevity locus on chromosome 5q33.3 influencing survival in the general European population. The minor allele of this locus associates with low blood pressure in middle age, although the contribution of this allele to survival may be less dependent on blood pressure. Hence, the pleiotropic mechanisms by which this intragenic variation contributes to lifespan regulation have to be elucidated.

Identification of grouped rare and common variants via penalized logistic regression

In spite of the success of genome-wide association studies in finding many common variants associated with disease, these variants seem to explain only a small proportion of the estimated heritability. Data collection has turned toward exome and whole genome sequencing, but it is well known that single marker methods frequently used for common variants have low power to detect rare variants associated with disease, even with very large sample sizes. In response, a variety of methods have been developed that attempt to cluster rare variants so that they may gather strength from one another under the premise that there may be multiple causal variants within a gene. Most of these methods group variants by gene or proximity, and test one gene or marker window at a time. We propose a penalized regression method (PeRC) that analyzes all genes at once, allowing grouping of all (rare and common) variants within a gene, along with subgrouping of the rare variants, thus borrowing strength from both rare and common variants within the same gene. The method can incorporate either a burden-based weighting of the rare variants or one in which the weights are data driven. In simulations, our method performs favorably when compared to many previously proposed approaches, including its predecessor, the sparse group lasso [Friedman et al., 2010].

Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study

Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11 (LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1228 unrelated nonagenarian and 1907 geographically matched controls. Using a fixed-effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (P-value = 9.6 × 10(-8) ). By combined modeling of linkage and association, we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with P-value = 0.02 and P-value = 1.0 × 10(-5) , respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.

Genome-wide association study identifies loci on 12q24 and 13q32 associated with tetralogy of Fallot

We conducted a genome-wide association study to search for risk alleles associated with Tetralogy of Fallot (TOF), using a northern European discovery set of 835 cases and 5159 controls. A region on chromosome 12q24 was associated (P = 1.4 × 10(-7)) and replicated convincingly (P = 3.9 × 10(-5)) in 798 cases and 2931 controls [per allele odds ratio (OR) = 1.27 in replication cohort, P = 7.7 × 10(-11) in combined populations]. Single nucleotide polymorphisms in the glypican 5 gene on chromosome 13q32 were also associated (P = 1.7 × 10(-7)) and replicated convincingly (P = 1.2 × 10(-5)) in 789 cases and 2927 controls (per allele OR = 1.31 in replication cohort, P = 3.03 × 10(-11) in combined populations). Four additional regions on chromosomes 10, 15 and 16 showed suggestive association accompanied by nominal replication. This study, the first genome-wide association study of a congenital heart malformation phenotype, provides evidence that common genetic variation influences the risk of TOF.

The molecular genetics of ovarian differentiation in the avian model

In birds as in mammals, sex is determined at fertilization by the inheritance of sex chromosomes. However, sexual differentiation - development of a male or female phenotype - occurs during embryonic development. Sex differentiation requires the induction of sex-specific developmental pathways in the gonads, resulting in the formation of ovaries or testes. Birds utilize a different sex chromosome system to that of mammals, where females are the heterogametic sex (carrying Z and W chromosomes), while males are homogametic (carrying 2 Z chromosomes). Therefore, while some genes essential for testis and ovarian development are conserved, important differences also exist. Namely, the key mammalian male-determining factor SRY does not exist in birds, and another transcription factor, DMRT1, plays a central role in testis development. In contrast to our understanding of testis development, ovarian differentiation is less well-characterized. Given the presence of a female-specific chromosome, studies in chicken will provide insight into the induction and function of female-specific gonadal pathways. In this review, we discuss sexual differentiation in chicken embryos, with emphasis on ovarian development. We highlight genes that may play a conserved role in this process, and discuss how interaction between ovarian pathways may be regulated.

Penalized-regression-based multimarker genotype analysis of Genetic Analysis Workshop 17 data

Testing for association between multiple markers and a phenotype can not only capture untyped causal variants in weak linkage disequilibrium with nearby typed markers but also identify the effect of a combination of markers. We propose a sliding window approach that uses multimarker genotypes as variables in a penalized regression. We investigate a penalty with three separate components: (1) a group least absolute shrinkage and selection operator (LASSO) that selects multimarker genotypes in a gene to be included in or excluded from the model, (2) an allele-sharing penalty that encourages multimarker genotypes with similar alleles to have similar coefficients, and (3) a penalty that shrinks the size of coefficients while performing model selection. The penalized likelihood is minimized with a cyclic coordinate descent algorithm, allowing quick coefficient estimation for a large number of markers. We compare our method to single-marker analysis and a gene-based sparse group LASSO on the Genetic Analysis Workshop 17 data for quantitative trait Q2. We found that all of the methods were underpowered to detect the simulated rare causal variants at the low false-positive rates desired in association studies. However, the sparse group LASSO on multi-marker genotypes seems to provide some advantage over the sparse group LASSO applied to single SNPs within genes, giving further evidence that there may be an advantage to modeling combinations of rare variant alleles over modeling them individually.

SNP selection in genome-wide and candidate gene studies via penalized logistic regression

Penalized regression methods offer an attractive alternative to single marker testing in genetic association analysis. Penalized regression methods shrink down to zero the coefficient of markers that have little apparent effect on the trait of interest, resulting in a parsimonious subset of what we hope are true pertinent predictors. Here we explore the performance of penalization in selecting SNPs as predictors in genetic association studies. The strength of the penalty can be chosen either to select a good predictive model (via methods such as computationally expensive cross validation), through maximum likelihood-based model selection criterion (such as the BIC), or to select a model that controls for type I error, as done here. We have investigated the performance of several penalized logistic regression approaches, simulating data under a variety of disease locus effect size and linkage disequilibrium patterns. We compared several penalties, including the elastic net, ridge, Lasso, MCP and the normal-exponential-γ shrinkage prior implemented in the hyperlasso software, to standard single locus analysis and simple forward stepwise regression. We examined how markers enter the model as penalties and P-value thresholds are varied, and report the sensitivity and specificity of each of the methods. Results show that penalized methods outperform single marker analysis, with the main difference being that penalized methods allow the simultaneous inclusion of a number of markers, and generally do not allow correlated variables to enter the model, producing a sparse model in which most of the identified explanatory markers are accounted for.

Penalized estimation of haplotype frequencies

MOTIVATION

Low haplotype diversity and linkage disequilibrium are the rule in short genomic segments. This fact suggests that parsimony should be enforced in estimation of haplotype frequencies. The current article introduces a diversity penalty that automatically discards potential haplotypes with low explanatory power. The standard EM algorithm for haplotype frequency estimation can accommodate the penalty if one passes over to a more general minorize-maximize (MM) scheme for estimation.

RESULTS

Our new MM algorithm converges in fewer iterations, eliminates marginal haplotypes from further consideration and reduces the computational complexity of each iteration. Estimation by the MM algorithm also improves haplotyping and genotype imputation compared to naive application of the EM algorithm. Thus, the MM algorithm is a useful substitute for the EM algorithm. Compared to the most sophisticated current methods of haplotyping and genotype imputation, the MM algorithm is slightly less accurate but at least an order of magnitude faster.

AVAILABILITY

Our software will be made available in the next release the program Mendel at http://www.genetics.ucla.edu/software/.

A dictionary model for haplotyping, genotype calling, and association testing

We propose a new method for haplotyping, genotype calling, and association testing based on a dictionary model for haplotypes. In this framework, a haplotype arises as a concatenation of conserved haplotype segments, drawn from a predefined dictionary according to segment specific probabilities. The observed data consist of unphased multimarker genotypes gathered on a random sample of unrelated individuals. These genotypes are subject to mutation, genotyping errors, and missing data. The true pair of haplotypes corresponding to a person's multimarker genotype is reconstructed using a Markov chain that visits haplotype pairs according to their posterior probabilities. Our implementation of the chain alternates Gibbs steps, which rearrange the phase of a single marker, and Metropolis steps, which swap maternal and paternal haplotypes from a given maker onward. Output of the chain include the most likely haplotype pairs, the most likely genotypes at each marker, and the expected number of occurrences of each haplotype segment. Reconstruction accuracy is comparable to that achieved by the best existing algorithms. More importantly, the dictionary model yields expected counts of conserved haplotype segments. These imputed counts can serve as genetic predictors in association studies, as we illustrate by examples on cystic fibrosis, Friedreich's ataxia, and angiotensin-I converting enzyme levels.

Reconstructing ancestral haplotypes with a dictionary model

We propose a dictionary model for haplotypes. According to the model, a haplotype is constructed by randomly concatenating haplotype segments from a given dictionary of segments. A haplotype block is defined as a set of haplotype segments that begin and end with the same pair of markers. In this framework, haplotype blocks can overlap, and the model provides a setting for testing the accuracy of simpler models invoking only nonoverlapping blocks. Each haplotype segment in a dictionary has an assigned probability and alternate spellings that account for genotyping errors and mutation. The model also allows for missing data, unphased genotypes, and prior distribution of parameters. Likelihood evaluations rely on forward and backward recurrences similar to the ones encountered in hidden Markov models. Parameter estimation is carried out with an EM algorithm. The search for the optimal dictionary is particularly difficult because of the variable dimension of the model space. We define a minimum description length criteria to evaluate each dictionary and use a combination of greedy search and careful initialization to select a best dictionary for a given dataset. Application of the model to simulated data gives encouraging results. In a real dataset, we are able to reconstruct a parsimonious dictionary that captures patterns of linkage disequilibrium well.

Effects of ethanol on mature offspring of mice given ethanol during pregnancy

Male offspring of mice maintained on isocaloric liquid diets containing either 20% ethanol or sucrose derived calories during pregnancy were tested on their ability to discriminate different ethanol doses as adults. They were trained to lever-press for a food reward on each of two levers in an operant chamber, and were then maintained on an FR20 reinforcement schedule. After response rates stabilized, ethanol discrimination training was initiated by reinforcing only responses made on the drug appropriate-lever after i.p. injections of ethanol (1.0 g/kg) or water. After learning to discriminate the 1.0 g dose, the animals' ability to discriminate doses of 0.25, 0.50, 0.75, 1.0, and 1.5 g/kg was assessed by determining the percentage of responses made on the drug lever during a 2-min test period. Compared to sucrose controls, mice exposed to alcohol in utero learned the lever response more slowly and were less responsive to injected ethanol as evidenced by a reduced effect of the drug on response rates and by a reduction in their ability to discriminate the presence of injected ethanol. The results indicate that prenatal ethanol exposure can have long term consequences which reduce the effects of ethanol in fully mature animals.