Efficient Replication of Over 180 Genetic Associations with Self-Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggests that online collection of self-reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations

How do practitioners characterize land tenure security?

Improving land tenure security (LTS) is a significant challenge for sustainable development. The Sustainable Development Goals and other recent global initiatives have renewed and increased the need to improve LTS to address climate change, biodiversity loss, food security, poverty reduction, and other challenges. At the same time, policymakers are increasingly interested in evidence- based policies and decisions, creating urgency for practitioners and researchers to work together. Yet, incongruent characterizations of LTS (identifying the key components of LTS) by practitioners and researchers can limit collaboration and information flows necessary for research and effective policymaking. While there are systematic reviews of how LTS is characterized in the academic literature, no prior study has assessed how practitioners characterize LTS. We address this gap using data from 54 interviews of land tenure practitioners working in 10 countries of global importance for biodiversity and climate change mitigation. Practitioners characterize LTS as complex and multifaceted, and a majority of practitioners refer to de jure terms (e.g., titling) when characterizing it. Notably, in our data just one practitioner characterized LTS in terms of perceptions of the landholder, contrasting the recent emphasis in the academic literature on landholder perceptions in LTS characterizations. Researchers should be aware of incongruence in how LTS is characterized in the academic literature when engaging practitioners.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155485/1/csp2186.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155485/2/csp2186_am.pd

A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites

Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs

Raiders of the Lost Bark: Orangutan Foraging Strategies in a Degraded Landscape

Deforestation is rapidly transforming primary forests across the tropics into human-dominated landscapes. Consequently, conservationists need to understand how different taxa respond and adapt to these changes in order to develop appropriate management strategies. Our two year study seeks to determine how wild Sumatran orangutans (Pongo abelii) adapt to living in an isolated agroforest landscape by investigating the sex of crop-raiders related to population demographics, and their temporal variations in feeding behaviour and dietary composition. From focal animal sampling we found that nine identified females raided cultivated fruits more than the four males. Seasonal adaptations were shown through orangutan feeding habits that shifted from being predominantly fruit-based (56% of the total feeding time, then 22% on bark) to the fallback food of bark (44%, then 35% on fruits), when key cultivated resources such as jackfruit (Artocarpus integer), were unavailable. Cultivated fruits were mostly consumed in the afternoon and evening, when farmers had returned home. The finding that females take greater crop-raiding risks than males differs from previous human-primate conflict studies, probably because of the low risks associated (as farmers rarely retaliated) and low intraspecific competition between males. Thus, the behavioral ecology of orangutans living in this human-dominated landscape differs markedly from that in primary forest, where orangutans have a strictly wild food diet, even where primary rainforests directly borders farmland. The importance of wild food availability was clearly illustrated in this study with 21% of the total orangutan feeding time being allocated to feeding on cultivated fruits. As forests are increasingly converted to cultivation, humans and orangutans are predicted to come into conflict more frequently. This study reveals orangutan adaptations for coexisting with humans, e.g. changes in temporal foraging patterns, which should be used for guiding the development of specific human-wildlife conflict mitigation strategies to lessen future crop-raiding and conflicts

Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance

SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of meta-information to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what meta-information should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting

Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits

Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach

The Genome Sequence of the Leaf-Cutter Ant Atta cephalotes Reveals Insights into Its Obligate Symbiotic Lifestyle

Leaf-cutter ants are one of the most important herbivorous insects in the Neotropics, harvesting vast quantities of fresh leaf material. The ants use leaves to cultivate a fungus that serves as the colony's primary food source. This obligate ant-fungus mutualism is one of the few occurrences of farming by non-humans and likely facilitated the formation of their massive colonies. Mature leaf-cutter ant colonies contain millions of workers ranging in size from small garden tenders to large soldiers, resulting in one of the most complex polymorphic caste systems within ants. To begin uncovering the genomic underpinnings of this system, we sequenced the genome of Atta cephalotes using 454 pyrosequencing. One prediction from this ant's lifestyle is that it has undergone genetic modifications that reflect its obligate dependence on the fungus for nutrients. Analysis of this genome sequence is consistent with this hypothesis, as we find evidence for reductions in genes related to nutrient acquisition. These include extensive reductions in serine proteases (which are likely unnecessary because proteolysis is not a primary mechanism used to process nutrients obtained from the fungus), a loss of genes involved in arginine biosynthesis (suggesting that this amino acid is obtained from the fungus), and the absence of a hexamerin (which sequesters amino acids during larval development in other insects). Following recent reports of genome sequences from other insects that engage in symbioses with beneficial microbes, the A. cephalotes genome provides new insights into the symbiotic lifestyle of this ant and advances our understanding of host–microbe symbioses

doi:10.1093/bioinformatics/btl243

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Efficient Replication of Over 180 Genetic Associations with Self‐Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web‐based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We
found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in‐depth questions to refine self‐reported diagnoses. Our data suggests that online collection of self‐reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations