Making the case for more inclusive GWAS

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

CRISPR in Public Health: The Health Equity Implications and Role of Community in Gene-Editing Research and Applications

CRISPR (clustered regularly interspaced short palindromic repeats) is a Nobel Prize-winning technology that holds significant promise for revolutionizing the prevention and treatment of human disease through gene editing. However, CRISPR's public health implications remain relatively uncertain and underdiscussed because (1) targeting genetic factors alone will have limited influence on population health, and (2) minority populations (racial/ethnic, sexual and gender)-who bear the nation's greatest health burdens-historically suffer unequal benefits from emerging health care innovations and tools. This article introduces CRISPR and its potential public health benefits (e.g., improving virus surveillance, curing genetic diseases that pose public health problems such as sickle cell anemia) while outlining several major ethical and practical threats to health equity. This includes minorities' grave underrepresentation in genomics research, which may lead to less effective and accepted CRISPR tools and therapies for these groups, and their anticipated unequal access to these tools and therapies in health care. Informed by the principles of fairness, justice, and equitable access, ensuring gene editing promotes rather than diminishes health equity will require the meaningful centering and engagement of minority patients and populations in gene-editing research using community-based participatory research approaches. (Am J Public Health. 2023;113(8):874-882. https://doi.org/10.2105/AJPH.2023.307315).

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Associations between depression-relevant genetic risk and youth stress exposure: Evidence of gene-environment correlations

Familial risk for depression is associated with youth exposure to self-generated dependent stressful life events and independent events that are out of youth's control. Familial risk includes both genetic and environmental influences, raising the question of whether genetic influences, specifically, are associated with youth exposure to both dependent and independent stressful life events. To address this question, this study examined the relation between a genome-wide association study (GWAS)-derived depression-based polygenic risk score (DEP-PRS) and youth experiences of dependent and independent stress. Participants were 180 youth (ages 8 to 14, 52.2% female) of European ancestry and their biological mothers recruited based on the presence versus absence of a history of major depressive disorder (MDD) in the mothers. Youth and mothers were interviewed every 6 months for 2 years regarding the occurrence of stressful life events, which were coded as independent or dependent (self-generated). Results indicated that youth's DEP-PRS and maternal history of MDD were uniquely associated with increased exposure to both dependent and independent events. Similar results were observed when examining major versus minor events separately, with the additional finding of a DEP-PRS × mother MDD interaction for major dependent events such that levels of moderate to severe dependent life stressors were highest among youth with high DEP-PRSs who also had mothers with MDD. These results not only support the presence of depression-relevant gene-environment correlations (rGEs), but also highlight the possibility that rather than only capturing depression-specific genetic liability, GWAS-derived polygenic risk scores may also capture genetic variance contributing to stress exposure. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Enriching the design of Alzheimer's disease clinical trials: Application of the polygenic hazard score and composite outcome measures

INTRODUCTION

Selecting individuals at high risk of Alzheimer's disease (AD) dementia and using the most sensitive outcome measures are important aspects of trial design.

METHODS

We divided participants from Alzheimer's Disease Neuroimaging Initiative at the 50th percentile of the predicted absolute risk of the polygenic hazard score (PHS). Outcome measures were the Alzheimer's Disease Assessment Schedule-Cognitive Subscale (ADAS-Cog), ADNI-Mem, Clinical Dementia Rating-Sum of Boxes (CDR SB), and Cognitive Function Composite 2 (CFC2). In addition to modeling, we use a power analysis compare numbers needed with each technique.

RESULTS

Data from 188 cognitively normal and 319 mild cognitively impaired (MCI) participants were analyzed. Using the ADAS-Cog to estimate sample sizes, without stratification over 24 months, would require 930 participants with MCI, while using the CFC2 and restricting participants to those in the upper 50th percentile would require only 284 participants.

DISCUSSION

Combining stratification by PHS and selection of a sensitive combined outcome measure in a cohort of patients with MCI can allow trial design that is more efficient, potentially less burdensome on participants, and more cost effective.