Does willpower mindset really moderate the ego-depletion effect? A preregistered replication of Job, Dweck, and Walton (2010).

This article reports a preregistered study in which we attempted to replicate the results of an influential study on the ego-depletion effect reported by Job, Dweck, and Walton in 2010. The original Job et al. study (Study 1, N = 60) provided evidence that the ego-depletion effect-a performance decrease on a self-control task after performing another self-control task-occurs only for individuals who hold a belief that their willpower is limited. This moderation of the ego-depletion effect by one's willpower mindset (limited vs. nonlimited) has been interpreted as evidence against a prevalent limited-resource account of self-control. Although this alternative account of the ego-depletion effect has become well-known, the statistical evidence of the original study was on shaky ground. We therefore conducted a preregistered replication of the original study with some methodological improvements. As in the original study, participants (N = 187) performed a self-control task (Stroop color-word interference task) after performing the control or depletion version of a letter cancelation task. Despite extensive analyses, we failed to replicate the original results: There was neither a significant main effect of ego depletion nor a significant moderation of this ego-depletion effect by individual differences in willpower mindset. Together with other recent failures to replicate the original moderation effect, our results cast doubts on the claim that an individual's view of whether willpower is limited or not affects one's susceptibility to the ego-depletion effect.

Genetic variants in the SHISA6 gene are associated with delayed cognitive impairment in two family datasets.

INTRODUCTION: Studies of cognitive impairment (CI) in Amish communities have identified sibships containing CI and cognitively unimpaired (CU) individuals. We hypothesize that CU individuals may carry protective alleles delaying age at onset (AAO) of CI. METHODS: A total of 1522 individuals screened for CI were genotyped. The outcome studied was AAO for CI individuals or age at last normal exam for CU individuals. Cox mixed-effects models examined association between age and single nucleotide variants (SNVs). RESULTS: Three SNVs were significantly associated (P < 5 × 10-8 ) with AAO on chromosomes 6 (rs14538074; hazard ratio [HR] = 3.35), 9 (rs534551495; HR = 2.82), and 17 (rs146729640; HR = 6.38). The chromosome 17 association was replicated in the independent National Institute on Aging Genetics Initiative for Late-Onset Alzheimer's Disease dataset. DISCUSSION: The replicated genome-wide significant association with AAO on chromosome 17 is located in the SHISA6 gene, which is involved in post-synaptic transmission in the hippocampus and is a biologically plausible candidate gene for Alzheimer's disease.

The genetic architecture of Alzheimer disease risk in the Ohio and Indiana Amish.

Alzheimer disease (AD) is the most common type of dementia and is currently estimated to affect 6.2 million Americans. It ranks as the sixth leading cause of death in the United States, and the proportion of deaths due to AD has been increasing since 2000, while the proportion of many other leading causes of deaths have decreased or remained constant. The risk for AD is multifactorial, including genetic and environmental risk factors. Although APOE ε4 remains the largest genetic risk factor for AD, more than 26 other loci have been associated with AD risk. Here, we recruited Amish adults from Ohio and Indiana to investigate AD risk and protective genetic effects. As a founder population that typically practices endogamy, variants that are rare in the general population may be of a higher frequency in the Amish population. Since the Amish have a slightly lower incidence and later age of onset of disease, they represent an excellent and unique population for research on protective genetic variants. We compared AD risk in the Amish and to a non-Amish population through APOE genotype, a non-APOE genetic risk score of genome-wide significant variants, and a non-APOE polygenic risk score considering all of the variants. Our results highlight the lesser relative impact of APOE and differing genetic architecture of AD risk in the Amish compared to a non-Amish, general European ancestry population.

Structured reflection increases intentions to reduce other people's health risks during COVID-19.

People believe they should consider how their behavior might negatively impact other people, Yet their behavior often increases others' health risks. This creates challenges for managing public health crises like the COVID-19 pandemic. We examined a procedure wherein people reflect on their personal criteria regarding how their behavior impacts others' health risks. We expected structured reflection to increase people's intentions and decisions to reduce others' health risks. Structured reflection increases attention to others' health risks and the correspondence between people's personal criteria and behavioral intentions. In four experiments during COVID-19, people (N  = 12,995) reported their personal criteria about how much specific attributes, including the impact on others' health risks, should influence their behavior. Compared with control conditions, people who engaged in structured reflection reported greater intentions to reduce business capacity (experiment 1) and avoid large social gatherings (experiments 2 and 3). They also donated more to provide vaccines to refugees (experiment 4). These effects emerged across seven countries that varied in collectivism and COVID-19 case rates (experiments 1 and 2). Structured reflection was distinct from instructions to carefully deliberate (experiment 3). Structured reflection increased the correlation between personal criteria and behavioral intentions (experiments 1 and 3). And structured reflection increased donations more among people who scored lower in cognitive reflection compared with those who scored higher in cognitive reflection (experiment 4). These findings suggest that structured reflection can effectively increase behaviors to reduce public health risks.

Lower Levels of Education Are Associated with Cognitive Impairment in the Old Order Amish.

BACKGROUND: Lower education has been reported to be associated with dementia. However, many studies have been done in settings where 12 years of formal education is the standard. Formal schooling in the Old Order Amish communities (OOA) ends at 8th grade which, along with their genetic homogeneity, makes it an interesting population to study the effect of education on cognitive impairment. OBJECTIVE: The objective of this study was to examine the association of education with cognitive function in individuals from the OOA. We hypothesized that small differences in educational attainment at lower levels of formal education were associated with risk for cognitive impairment. METHODS: Data of 2,426 individuals from the OOA aged 54-99 were analyzed. The Modified Mini-Mental State Examination (3MS-R) was used to classify participants as CI or normal. Individuals were classified into three education categories: <8, 8, and >8 years of education. To measure the association of education with cognitive status, a logistic regression model was performed adding age and sex as covariates. RESULTS: Our results showed that individuals who attained lowest levels of education (<8 and 8) had a higher probability of becoming cognitvely impaired compared with people attending >8 years (OR = 2.96 and 1.85). CONCLUSION: Even within a setting of low levels of formal education, small differences in educational attainment can still be associated with the risk of cognitive impairment. Given the homogeneity of the OOA, these results are less likely to be biased by differences in socioeconomic backgrounds.

Sensitivity to differential NRF1 gene signatures contributes to breast cancer disparities.

PURPOSE: Nuclear respiratory factor 1 (NRF1) drives estrogen-dependent breast tumorigenesis. Herein we examined the impact of NRF1 activity on the aggressiveness and disparate molecular signature of breast cancer in Black, White, Asian, and Hispanic women. METHODS: NRF1 activity by transcription factor target enrichment analysis and causal NRF1-target gene signatures by Bayesian Network Inference with Java Objects (BANJO) and Markov Chain Monte Carlo (MCMC)-based gene order were examined in The Cancer Genome Atlas (TCGA) breast cancer cohorts. RESULTS: We are the first to report increased NRF1 activity based on its differential effects on genome-wide transcription associated with luminal A and B, HER2+ and triple-negative (TN) molecular subtypes of breast cancer in women of different race/ethnicity. We observed disparate NRF1 motif-containing causal gene signatures unique to Black, White, Asian, and Hispanic women for luminal A breast cancer. Further gene order searches showed molecular heterogeneity of each subtype of breast cancer. Six different gene order sequences involving CDK1, HMMR, CCNB2, CCNB1, E2F1, CREB3L4, GTSE1, and LMNB1 with almost equal weight predicted the probability of luminal A breast cancer in whites. Three different gene order sequences consisting of CCNB1 and GTSE1, and CCNB1, LMNB1, CDK1 or CASP3 predicted almost 100% probability of luminal B breast cancer in whites; CCNB1 and LMNB1 or GTSE predicted 100% HER2+ breast cancer in whites. GTSE1 and TUBA1C combined together predicted 100% probability of developing TNBC in whites; NRF1, TUBA1B and BAX with EFNA4, and NRF1 and BTRC predicated 100% TNBC in blacks. High expressor NRF1 TN breast tumors showed unfavorable prognosis with a high risk of breast cancer death in white women. CONCLUSION: Our findings showed how sensitivity to high NRF1 transcriptional activity coupled with its target gene signatures contribute to racial differences in luminal A and TN breast cancer subtypes. This knowledge may be useful in personalized intervention to prevent and treat this clinically challenging problem.

Integrated Chip-Seq and RNA-Seq Data Analysis Coupled with Bioinformatics Approaches to Investigate Regulatory Landscape of Transcription Modulators in Breast Cancer Cells.

The objective of this chapter is to describe step-by-step bioinformatics and functional genomics solutions for analyzing ChIP-Seq and RNA-Seq data for understanding the regulatory mechanisms of chromatin modifiers and transcription factors that can drive pathogenesis of chronic complex human diseases, such as cancer. Here we have used two transcription regulatory proteins: nuclear respiratory factor 1 (NRF1) and inhibitor of differentiation protein 3 (ID3) for ChIP-Seq and RNA-Seq data as examples for discussing the importance of selecting the appropriate computational analysis methods, software, and parameters for the processing of raw data as well as their integrative regulatory landscape analysis to obtain accurate and reliable results. Both ChIP-Seq and RNA-Seq analytic methodologies are used as instructional examples to identify NRF1 or ID3 binding to the promoters and enhancers in the genome and their effects on the activity as well as to discover target genes that can drive breast cancer.

The Puerto Rico Alzheimer Disease Initiative (PRADI): A Multisource Ascertainment Approach.

INTRODUCTION: Puerto Ricans, the second largest Latino group in the continental US, are underrepresented in genomic studies of Alzheimer disease (AD). To increase representation of this group in genomic studies of AD, we developed a multisource ascertainment approach to enroll AD patients, and their family members living in Puerto Rico (PR) as part of the Alzheimer's Disease Sequencing Project (ADSP), an international effort to advance broader personalized/precision medicine initiatives for AD across all populations. METHODS: The Puerto Rico Alzheimer Disease Initiative (PRADI) multisource ascertainment approach was developed to recruit and enroll Puerto Rican adults aged 50 years and older for a genetic research study of AD, including individuals with cognitive decline (AD, mild cognitive impairment), their similarly, aged family members, and cognitively healthy unrelated individuals age 50 and up. Emphasizing identification and relationship building with key stakeholders, we conducted ascertainment across the island. In addition to reporting on PRADI ascertainment, we detail admixture analysis for our cohort by region, group differences in age of onset, cognitive level by region, and ascertainment source. RESULTS: We report on 674 individuals who met standard eligibility criteria [282 AD-affected participants (42% of the sample), 115 individuals with mild cognitive impairment (MCI) (17% of the sample), and 277 cognitively healthy individuals (41% of the sample)]. There are 43 possible multiplex families (10 families with 4 or more AD-affected members and 3 families with 3 AD-affected members). Most individuals in our cohort were ascertained from the Metro, Bayamón, and Caguas health regions. Across health regions, we found differences in ancestral backgrounds, and select clinical traits. DISCUSSION: The multisource ascertainment approach used in the PRADI study highlights the importance of enlisting a broad range of community resources and providers. Preliminary results provide important information about our cohort that will be useful as we move forward with ascertainment. We expect that results from the PRADI study will lead to a better understanding of genetic risk for AD among this population.

It depends: Partisan evaluation of conditional probability importance.

Policies to suppress rare events such as terrorism often restrict co-occurring categories such as Muslim immigration. Evaluating restrictive policies requires clear thinking about conditional probabilities. For example, terrorism is extremely rare. So even if most terrorist immigrants are Muslim-a high "hit rate"-the inverse conditional probability of Muslim immigrants being terrorists is extremely low. Yet the inverse conditional probability is more relevant to evaluating restrictive policies such as the threat of terrorism if Muslim immigration were restricted. We suggest that people engage in partisan evaluation of conditional probabilities, judging hit rates as more important when they support politically prescribed restrictive policies. In two studies, supporters of expelling asylum seekers from Tel Aviv, Israel, of banning Muslim immigration and travel to the United States, and of banning assault weapons judged "hit rate" probabilities (e.g., that terrorists are Muslims) as more important than did policy opponents, who judged the inverse conditional probabilities (e.g., that Muslims are terrorists) as more important. These partisan differences spanned restrictive policies favored by Rightists and Republicans (expelling asylum seekers and banning Muslim travel) and by Democrats (banning assault weapons). Inviting partisans to adopt an unbiased expert's perspective partially reduced these partisan differences. In Study 2 (but not Study 1), partisan differences were larger among more numerate partisans, suggesting that numeracy supported motivated reasoning. These findings have implications for polarization, political judgment, and policy evaluation. Even when partisans agree about what the statistical facts are, they markedly disagree about the relevance of those statistical facts.

NRF1 motif sequence-enriched genes involved in ER/PR -ve HER2 +ve breast cancer signaling pathways.

Nuclear respiratory factor 1 (NRF1) transcription factor has recently been shown to control breast cancer progression. However, mechanistic aspects by which NRF1 may contribute to susceptibility to different breast tumor subtypes are still not fully understood. Since transcriptional control of NRF1 seems to be dependent on epidermal growth factor receptor signaling, herein, we investigated the role of NRF1 in estrogen receptor/progesterone receptor negative, but human epidermal growth factor receptor 2-positive (ER/PR -ve HER2 +ve) breast cancer. We found that both mRNA and protein levels of NRF1 and its transcriptional activity were significantly higher in ER/PR -ve HER2 +ve breast cancer samples compared to normal breast tissues. This was consistent with our observation of higher NRF1 protein expression in the experimental model of HER2+ breast cancer brain metastasis. To identify network-based pathways involved in the susceptibility to the ER/PR -ve HER2 +ve breast cancer subtype, the NRF1 transcriptional regulatory genome-wide landscape was analyzed using the approach consisting of a systematic integration of ChIP DNA-seq, RNA-Microarray, NRF1 protein-DNA motif binding, signal pathway analysis, and Bayesian machine learning. Our findings show that a high percentage of known HER2+ breast cancer susceptibility genes, including EGFR, IGFR, and E2F1, are under transcriptional control of NRF1. Promoters of several genes from the KEGG HER2+ breast cancer pathway and 11 signaling pathways linked to 6 hallmarks of cancer contain the NRF1 motif. By pathway analysis, key breast cancer hallmark genes of epithelial-mesenchymal transition, stemness, cell apoptosis, cell cycle regulation, chromosomal integrity, and DNA damage/repair were highly enriched with NRF1 motifs. In addition, we found using Bayesian network-based machine learning that 30 NRF1 motif-enriched genes including growth factor receptors-FGFR1, IGF1R; E2Fs transcription factor family-E2F1, E2F3; MAPK pathway-SHC2, GRB2, MAPK1; PI3K-AKT-mTOR signaling pathway-PIK3CD, PIK3R1, PIK3R3, RPS6KB2; WNT signaling pathway-WNT7B, DLV1, DLV2, GSK3B, NRF1, and DDB2, known for its role in DNA repair and involvement in early events associated with metastatic progression of breast cancer cells, were associated with HER2-amplified breast cancer. Machine learning search further revealed that the likelihood of HER2-positive breast cancer is almost 100% in a patient with the high NRF1 expression combined with expression patterns of high E2F3, GSK3B, and MAPK1, low or no change in E2F1 and FGFR1, and high or no change in PIK3R3. In summary, our findings suggest novel roles of NRF1 and its regulatory networks in susceptibility to the ER/PR -ve HER2 +ve aggressive breast cancer subtype. Clinical confirmation of our machine learned Bayesian networks will have significant impact on our understanding of the role of NRF1 as a valuable biomarker for breast cancer diagnosis and prognosis as well as provide strong rationale for future studies to develop NRF1 signaling-based therapeutics to target HER2+ breast cancer.