The study of genetics has long been shaped by the availability of data from various regions around the world. However, despite its vast population diversity, India remains underrepresented in global genetic datasets. A recent study published in Cell Press’s journal Cell aimed to fill this critical gap and reshape our understanding of how ancient migrations, archaic admixture, and social structures have influenced Indian genetic variation.

The researchers analyzed genomic data from over 2,700 people across India, capturing genetic variation from most geographic regions, linguistic groups, and communities. Their findings revealed that the majority of modern-day Indians’ ancestry can be traced back to Neolithic Iranian farmers, Eurasian Steppe pastoralists, and South Asian hunter-gatherers.

“This study fills a critical gap and reshapes our understanding of how ancient migrations, archaic admixture, and social structures have shaped Indian genetic variation,” says senior author Priya Moorjani of the University of California, Berkeley. “Studying these subpopulations allows us to explore how ancient ancestry, geography, language, and social practices interacted to shape genetic variation.

The researchers used data from the Longitudinal Aging Study in India (LASI) and generated whole-genome sequences from 2,762 individuals in India, including people who spoke a range of different languages. They used these data to reconstruct the evolutionary history of India over the past 50,000 years at fine scale, showing how history impacts adaptation and disease in present-day Indians.

Their study showed that most Indians derive ancestry from populations related to three ancestral groups: Neolithic Iranian farmers, Eurasian Steppe pastoralists, and South Asian hunter-gatherers. This is a significant finding, as it highlights the complex population history of India and its impact on genetic variation related to disease.

The researchers also focused on the impact of archaic hominin ancestry – specifically, Neanderthal and Denisovan – on disease susceptibility. They found that some genes inherited from these archaic groups have an impact on immune functions.

One of the most striking findings was that India harbors the highest variation in Neanderthal ancestry among non-Africans. This allowed the researchers to reconstruct around 50% of the Neanderthal genome and 20% of the Denisovan genome from Indian individuals, more than any other previous archaic ancestry study.

The limitations of this work were acknowledged by the researchers, who noted that the limited availability of ancient DNA from South and Central Asia will require refinement as more data becomes available. They plan to continue studying the LASI cohort to enable a closer look at the source of genetic adaptations and disease variants across India.

Overall, this study provides a deeper understanding of the origin of functional variation and informs precision health strategies in India. It also highlights the importance of including diverse populations in genetic studies to prevent biased interpretations of genetic patterns and uncover functional variation related to all major communities.

Coastal communities worldwide are experiencing more frequent and prolonged flooding events due to rising sea levels and increased precipitation. According to a recent study by North Carolina State University and the University of North Carolina at Chapel Hill, this trend is more pronounced than previously thought, with major implications for flood risk management, urban planning, and community resilience.

The study’s authors used land-based sensors to monitor flooding events in three coastal communities: Beaufort, Carolina Beach, and Sea Level. Over a year-long period, the sensors detected flooding on 26 days in Beaufort, 65 days in Carolina Beach, and 128 days in Sea Level. In contrast, traditional methods using tidal gauge data estimated flooding frequencies as significantly lower.

“These numbers were vastly different from what the thresholds tell us,” says Katherine Anarde, co-author of the paper and assistant professor of coastal engineering at NC State. “The current methodology drastically underestimates the number of floods and fails to capture their duration.”

Researchers also found that while the National Weather Service’s minor flood threshold (NWS) sometimes overestimated flooding frequencies, it still did not accurately account for the prolonged nature of these events.

“Our findings show that traditional methods don’t adequately capture how long water takes to drain off land,” says Miyuki Hino, corresponding author and assistant professor of city and regional planning at UNC. “More accurate information on coastal flooding can inform where and how we invest resources in building more resilient communities.”

The study’s results have significant implications for flood risk management and urban planning. By using land-based sensors to monitor flooding events, researchers can provide more accurate data to inform decision-making processes.

“Every community is unique,” says Hino. “But with more accurate data, we can help communities assess what response strategy is best for them, now and in the future.”

The paper, titled “Land-based Sensors Reveal High Frequency of Coastal Flooding,” was published in Nature Communications Earth & Environment on June 2.

The southeastern coastal states are no strangers to hurricane fury. As the globe continues to warm, scientists predict that these powerful storms will only get more destructive – packing higher winds and torrential rainfall. A recent study published in Risk Analysis projects a staggering 76% increase in wind-related losses for homeowners in this region by the year 2060, and a whopping 102% increase by 2100.

University of Illinois civil engineer Eun Jeong Cha led a team that used machine learning to simulate the impact of future hurricanes on wooden single-family homes with concrete masonry in Texas, Louisiana, Mississippi, Alabama, Florida, Georgia, South Carolina, and North Carolina. Their worst-case scenario projections were based on the highest possible greenhouse gas emissions from the Intergovernmental Panel on Climate Change (IPCC).

The results are dire: losses from wind and rain-ingress will be 49-76% higher by 2060 and 71-102% higher by 2100. Hurricane wind speeds in Texas will increase by 14% in the 2050s compared to present-day levels, making it the state with the highest expected losses.

Some inland counties, such as Charleston, South Carolina, may experience a relatively large percentage increase in projected risk. At the county level, Cha’s team found variations of hurricane risk associated with climate change and differences in regional preparation for hurricane wind hazards.

“The discrepancies we found emphasize the necessity of vast regional risk assessment for federal- and state-level resource allocation and risk mitigation planning,” says Cha.

Insurance models need to account for heavier rainfall and stronger winds. Hurricane winds account for over 40% of storm-related losses in the residential sector, causing $14 billion in expected annual costs to the U.S. economy. Yet most hurricane models used by insurance companies fail to consider the impact of climate change.

“The worst-case scenario is widely used to explore high-impact possibilities for long-term planning and resilience studies,” says Cha.

Accurately estimating hurricane hazards and resulting losses is essential, says Cha. “Our findings contribute significantly to our understanding of climate change impacts on hurricane risks, providing valuable insights for policymakers, urban planners, and the insurance industry.”