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Lessons from Dwarf8 on the strengths and weaknesses of structured association mapping.

ABSTRACT: The strengths of association mapping lie in its resolution and allelic richness, but spurious associations arising from historical relationships and selection patterns need to be accounted for in statistical analyses. Here we reanalyze one of the first generation structured association mapping studies of the Dwarf8 (d8) locus with flowering time in maize using the full range of new mapping populations, statistical approaches, and haplotype maps. Because this trait was highly correlated with population structure, we found that basic structured association methods overestimate phenotypic effects in the region, while mixed model approaches perform substantially better. Combined with analysis of the maize nested association mapping population (a multi-family crossing design), it is concluded that most, if not all, of the QTL effects at the general location of the d8 locus are from rare extended haplotypes that include other linked QTLs and that d8 is unlikely to be involved in controlling flowering time in maize. Previous independent studies have shown evidence for selection at the d8 locus. Based on the evidence of population bottleneck, selection patterns, and haplotype structure observed in the region, we suggest that multiple traits may be strongly correlated with population structure and that selection on these traits has influenced segregation patterns in the region. Overall, this study provides insight into how modern association and linkage mapping, combined with haplotype analysis, can produce results that are more robust.

SUBMITTER: Larsson SJ

PROVIDER: S-EPMC3578782 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionAs part of efforts to rapidly identify and care for individuals with COVID-19, trace and quarantine contacts, and monitor disease trends over time, most African countries implemented interventions to strengthen their existing disease surveillance systems. This research describes the strengths, weaknesses and lessons learnt from the COVID-19 surveillance strategies implemented in four African countries to inform the enhancement of surveillance systems for future epidemics on the continent.MethodsThe four countries namely the Democratic Republic of Congo (DRC), Nigeria, Senegal, and Uganda, were selected based on their variability in COVID-19 response and representation of Francophone and Anglophone countries. A mixed-methods observational study was conducted including desk review and key informant interviews, to document best practices, gaps, and innovations in surveillance at the national, sub-national, health facilities, and community levels, and these learnings were synthesized across the countries.ResultsSurveillance approaches across countries included - case investigation, contact tracing, community-based, laboratory-based sentinel, serological, telephone hotlines, and genomic sequencing surveillance. As the COVID-19 pandemic progressed, the health systems moved from aggressive testing and contact tracing to detect virus and triage individual contacts into quarantine and confirmed cases, isolation and clinical care. Surveillance, including case definitions, changed from contact tracing of all contacts of confirmed cases to only symptomatic contacts and travelers. All countries reported inadequate staffing, staff capacity gaps and lack of full integration of data sources. All four countries under study improved data management and surveillance capacity by training health workers and increasing resources for laboratories, but the disease burden was under-detected. Decentralizing surveillance to enable swifter implementation of targeted public health measures at the subnational level was a challenge. There were also gaps in genomic and postmortem surveillance including community level sero-prevalence studies, as well as digital technologies to provide more timely and accurate surveillance data.ConclusionAll the four countries demonstrated a prompt public health surveillance response and adopted similar approaches to surveillance with some adaptations as the pandemic progresses. There is need for investments to enhance surveillance approaches and systems including decentralizing surveillance to the subnational and community levels, strengthening capabilities for genomic surveillance and use of digital technologies, among others. Investing in health worker capacity, ensuring data quality and availability and improving ability to transmit surveillance data between and across multiple levels of the health care system is also critical. Countries need to take immediate action in strengthening their surveillance systems to better prepare for the next major disease outbreak and pandemic.

Project description:Quantifying human mobility has significant consequences for studying physical activity, exposure to pathogens, and generating more realistic infectious disease models. Location-aware technologies such as Global Positioning System (GPS)-enabled devices are used increasingly as a gold standard for mobility research. The main goal of this observational study was to compare and contrast the information obtained through GPS and semi-structured interviews (SSI) to assess issues affecting data quality and, ultimately, our ability to measure fine-scale human mobility. A total of 160 individuals, ages 7 to 74, from Iquitos, Peru, were tracked using GPS data-loggers for 14 days and later interviewed using the SSI about places they visited while tracked. A total of 2,047 and 886 places were reported in the SSI and identified by GPS, respectively. Differences in the concordance between methods occurred by location type, distance threshold (within a given radius to be considered a match) selected, GPS data collection frequency (i.e., 30, 90 or 150 seconds) and number of GPS points near the SSI place considered to define a match. Both methods had perfect concordance identifying each participant's house, followed by 80-100% concordance for identifying schools and lodgings, and 50-80% concordance for residences and commercial and religious locations. As the distance threshold selected increased, the concordance between SSI and raw GPS data increased (beyond 20 meters most locations reached their maximum concordance). Processing raw GPS data using a signal-clustering algorithm decreased overall concordance to 14.3%. The most common causes of discordance as described by a sub-sample (n=101) with whom we followed-up were GPS units being accidentally off (30%), forgetting or purposely not taking the units when leaving home (24.8%), possible barriers to the signal (4.7%) and leaving units home to recharge (4.6%). We provide a quantitative assessment of the strengths and weaknesses of both methods for capturing fine-scale human mobility.

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Lessons from Dwarf8 on the strengths and weaknesses of structured association mapping.

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