#crossancestry — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #crossancestry, aggregated by home.social.
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DATE: September 5, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Scientists build a more accurate Alzheimer’s risk score using global DNA data
Researchers have developed a new genetic risk scoring method that improves the prediction of Alzheimer’s disease across diverse populations, moving beyond the historical reliance on European genetic data. The findings indicate that blending genetic information from multiple global groups also helps predict biological markers of the disease. The research was published in Alzheimer’s & Dementia.
Alzheimer’s disease is a progressive neurological condition that slowly destroys memory and thinking skills. To understand who might be at the highest risk of developing the disease, scientists often use polygenic risk scores. A polygenic risk score is a tool that adds up the tiny effects of thousands of genetic variations across a person’s entire genetic code to estimate their overall inherited risk for a specific condition.
To build these scores, researchers rely on genome-wide association studies. These are large-scale projects that scan the DNA of thousands of people to find small genetic differences linked to a disease. Historically, these massive genetic databases have overwhelmingly featured individuals of European descent. A 2016 study showed that genetic risk scores built primarily from people of European descent lose substantial accuracy when applied to other ancestral groups. Because human genetics vary slightly across populations due to demographic history, a score that works well for a European individual often fails to predict disease risk for someone of African or Asian descent.
The push to solve this problem has led scientists to gather more diverse genetic data. For example, a study covered by PsyPost in 2025 found novel Alzheimer’s-linked genes by analyzing multi-ancestry genome data. To make use of this newly available information, statisticians have been designing new ways to combine the data. A 2023 study demonstrated that building risk scores from diverse, multi-ethnic groups consistently outperforms scores built from just one population.
Building on this progress, Meri Okorie, Shea J. Andrews, and their colleagues at the University of California, San Francisco, sought to test these new scoring models for Alzheimer’s disease. They wanted to see if a cross-ancestry approach could accurately predict not just the clinical diagnosis of Alzheimer’s, but also the underlying physical damage occurring in the brain, across a multiethnic group of older adults.
The researchers gathered genetic summary data from hundreds of thousands of individuals of European, African, Admixed American, East Asian, and Caribbean Hispanic descent. The term Admixed American refers to populations, such as many Latino or Hispanic individuals, whose ancestry is a complex mix of Indigenous American, European, and African genetics.
Using this diverse data, the team built three different types of polygenic risk scores. The first was a single-ancestry score, created using only the European genetic data. The second was a multi-ancestry score, which simply pooled all the genetic data from the different groups together. The third was a cross-ancestry score, an advanced statistical model that jointly analyzed the data from multiple populations to adjust how much weight each genetic variant should carry based on shared genetic effects. The researchers also used a specialized mathematical step called ancestry normalization to ensure the scores were scaled fairly across different genetic backgrounds.
To test these three scores, the researchers applied them to two large groups of patients living in the United States. The first testing group came from the Alzheimer’s Disease Sequencing Project. This sample included 19,398 individuals, with 7,111 diagnosed with Alzheimer’s disease and 12,287 who were cognitively normal. The participants in this group were of European, Admixed American, and African descent.
The second testing group came from the Health and Aging Brain Study–Health Disparities. This group included 2,559 older adults of European, Admixed American, and African descent. For all analyses, the researchers adjusted for factors like age, sex, and the presence of the APOE gene. The APOE gene is a well-established genetic variation that strongly increases Alzheimer’s risk on its own, so controlling for it allowed the researchers to see the effect of the broader polygenic risk score.
In addition to looking at whether participants were diagnosed with Alzheimer’s, the researchers looked at objective medical measurements. These included cognitive test scores assessing memory and language, brain scans, and the analysis of cerebrospinal fluid. Cerebrospinal fluid is the liquid that surrounds the brain and spinal cord, and it can be tested for specific proteins that act as warning signs for Alzheimer’s disease.
The researchers found that the single-ancestry score, built only from European data, performed poorly outside of European populations. While it worked well for predicting Alzheimer’s risk in participants of European descent, its accuracy dropped noticeably in Admixed American participants. For participants of African ancestry, the single-ancestry score completely failed to predict Alzheimer’s disease risk.
In contrast, the cross-ancestry score showed the highest predictive performance across the board. When applied to participants of African ancestry, a higher cross-ancestry score was associated with a 71 percent increase in the odds of having an Alzheimer’s diagnosis compared to the baseline risk. This advanced scoring model also maintained high accuracy for Admixed American and European participants, providing a more balanced predictive tool for diverse populations.
Beyond predicting a clinical diagnosis, the cross-ancestry score successfully predicted physical and cognitive signs of the disease. Across the testing groups, higher cross-ancestry risk scores were tied to poorer performance in cognitive domains like memory, language, and executive function. Executive function refers to mental skills that include working memory, flexible thinking, and self-control.
The cross-ancestry score also aligned with biomarkers of the disease in the body. Individuals with higher genetic risk scores tended to have lower levels of amyloid-beta 42 in their cerebrospinal fluid. In the context of Alzheimer’s disease, a drop of this specific protein in the spinal fluid indicates that the protein is clumping together inside the brain to form harmful plaques.
The researchers also looked at autopsy data and advanced brain imaging. They found that participants with the highest cross-ancestry polygenic risk scores had a higher probability of being in the most severe categories of brain pathology. This indicates that the genetic risk score is not only catching surface-level symptoms but is reflecting the advanced physical accumulation of plaques and tangles that destroy brain tissue.
There are a few things to keep in mind regarding this study. The data lacked representation from South and East Asian populations in the testing phase, which limits the ability to know exactly how well the cross-ancestry score performs for those specific groups. Expanding genetic databases to include more global populations is an ongoing challenge in medical research.
The participants tested in this study were all residents of the United States. It is unknown if the models would perform with the exact same accuracy in populations living in other environments or continents, where different lifestyle and environmental factors are at play.
The researchers also point out that these risk scores currently explain only a modest fraction of a person’s total risk for Alzheimer’s disease. A polygenic risk score is just one piece of the puzzle. Age, sex, specific rare genes, and environmental factors like diet and exercise all interact to determine whether someone will actually develop the disease. This study did not look at how a person’s environment might modify their genetic risk.
The study, “Cross-ancestry polygenic risk scores enhance Alzheimer’s disease risk prediction in multiethnic cohorts,” was authored by Meri Okorie, Caroline Jonson, Alexis P. Oddi, Patricia A. Castruita, Brian Fulton-Howard, Kristine Yaffe, Jennifer S. Yokoyama, Chinedu Udeh-Momoh, and Shea J. Andrews.
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AlzheimersRisk #PolygenicRiskScore #MultiethnicGenomics #CrossAncestry #GeneticPrediction #DiversityInGenetics #AlzheimersDisease #Biomarkers #BrainHealth #NeurodegenerativeResearch
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DATE: September 5, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Scientists build a more accurate Alzheimer’s risk score using global DNA data
Researchers have developed a new genetic risk scoring method that improves the prediction of Alzheimer’s disease across diverse populations, moving beyond the historical reliance on European genetic data. The findings indicate that blending genetic information from multiple global groups also helps predict biological markers of the disease. The research was published in Alzheimer’s & Dementia.
Alzheimer’s disease is a progressive neurological condition that slowly destroys memory and thinking skills. To understand who might be at the highest risk of developing the disease, scientists often use polygenic risk scores. A polygenic risk score is a tool that adds up the tiny effects of thousands of genetic variations across a person’s entire genetic code to estimate their overall inherited risk for a specific condition.
To build these scores, researchers rely on genome-wide association studies. These are large-scale projects that scan the DNA of thousands of people to find small genetic differences linked to a disease. Historically, these massive genetic databases have overwhelmingly featured individuals of European descent. A 2016 study showed that genetic risk scores built primarily from people of European descent lose substantial accuracy when applied to other ancestral groups. Because human genetics vary slightly across populations due to demographic history, a score that works well for a European individual often fails to predict disease risk for someone of African or Asian descent.
The push to solve this problem has led scientists to gather more diverse genetic data. For example, a study covered by PsyPost in 2025 found novel Alzheimer’s-linked genes by analyzing multi-ancestry genome data. To make use of this newly available information, statisticians have been designing new ways to combine the data. A 2023 study demonstrated that building risk scores from diverse, multi-ethnic groups consistently outperforms scores built from just one population.
Building on this progress, Meri Okorie, Shea J. Andrews, and their colleagues at the University of California, San Francisco, sought to test these new scoring models for Alzheimer’s disease. They wanted to see if a cross-ancestry approach could accurately predict not just the clinical diagnosis of Alzheimer’s, but also the underlying physical damage occurring in the brain, across a multiethnic group of older adults.
The researchers gathered genetic summary data from hundreds of thousands of individuals of European, African, Admixed American, East Asian, and Caribbean Hispanic descent. The term Admixed American refers to populations, such as many Latino or Hispanic individuals, whose ancestry is a complex mix of Indigenous American, European, and African genetics.
Using this diverse data, the team built three different types of polygenic risk scores. The first was a single-ancestry score, created using only the European genetic data. The second was a multi-ancestry score, which simply pooled all the genetic data from the different groups together. The third was a cross-ancestry score, an advanced statistical model that jointly analyzed the data from multiple populations to adjust how much weight each genetic variant should carry based on shared genetic effects. The researchers also used a specialized mathematical step called ancestry normalization to ensure the scores were scaled fairly across different genetic backgrounds.
To test these three scores, the researchers applied them to two large groups of patients living in the United States. The first testing group came from the Alzheimer’s Disease Sequencing Project. This sample included 19,398 individuals, with 7,111 diagnosed with Alzheimer’s disease and 12,287 who were cognitively normal. The participants in this group were of European, Admixed American, and African descent.
The second testing group came from the Health and Aging Brain Study–Health Disparities. This group included 2,559 older adults of European, Admixed American, and African descent. For all analyses, the researchers adjusted for factors like age, sex, and the presence of the APOE gene. The APOE gene is a well-established genetic variation that strongly increases Alzheimer’s risk on its own, so controlling for it allowed the researchers to see the effect of the broader polygenic risk score.
In addition to looking at whether participants were diagnosed with Alzheimer’s, the researchers looked at objective medical measurements. These included cognitive test scores assessing memory and language, brain scans, and the analysis of cerebrospinal fluid. Cerebrospinal fluid is the liquid that surrounds the brain and spinal cord, and it can be tested for specific proteins that act as warning signs for Alzheimer’s disease.
The researchers found that the single-ancestry score, built only from European data, performed poorly outside of European populations. While it worked well for predicting Alzheimer’s risk in participants of European descent, its accuracy dropped noticeably in Admixed American participants. For participants of African ancestry, the single-ancestry score completely failed to predict Alzheimer’s disease risk.
In contrast, the cross-ancestry score showed the highest predictive performance across the board. When applied to participants of African ancestry, a higher cross-ancestry score was associated with a 71 percent increase in the odds of having an Alzheimer’s diagnosis compared to the baseline risk. This advanced scoring model also maintained high accuracy for Admixed American and European participants, providing a more balanced predictive tool for diverse populations.
Beyond predicting a clinical diagnosis, the cross-ancestry score successfully predicted physical and cognitive signs of the disease. Across the testing groups, higher cross-ancestry risk scores were tied to poorer performance in cognitive domains like memory, language, and executive function. Executive function refers to mental skills that include working memory, flexible thinking, and self-control.
The cross-ancestry score also aligned with biomarkers of the disease in the body. Individuals with higher genetic risk scores tended to have lower levels of amyloid-beta 42 in their cerebrospinal fluid. In the context of Alzheimer’s disease, a drop of this specific protein in the spinal fluid indicates that the protein is clumping together inside the brain to form harmful plaques.
The researchers also looked at autopsy data and advanced brain imaging. They found that participants with the highest cross-ancestry polygenic risk scores had a higher probability of being in the most severe categories of brain pathology. This indicates that the genetic risk score is not only catching surface-level symptoms but is reflecting the advanced physical accumulation of plaques and tangles that destroy brain tissue.
There are a few things to keep in mind regarding this study. The data lacked representation from South and East Asian populations in the testing phase, which limits the ability to know exactly how well the cross-ancestry score performs for those specific groups. Expanding genetic databases to include more global populations is an ongoing challenge in medical research.
The participants tested in this study were all residents of the United States. It is unknown if the models would perform with the exact same accuracy in populations living in other environments or continents, where different lifestyle and environmental factors are at play.
The researchers also point out that these risk scores currently explain only a modest fraction of a person’s total risk for Alzheimer’s disease. A polygenic risk score is just one piece of the puzzle. Age, sex, specific rare genes, and environmental factors like diet and exercise all interact to determine whether someone will actually develop the disease. This study did not look at how a person’s environment might modify their genetic risk.
The study, “Cross-ancestry polygenic risk scores enhance Alzheimer’s disease risk prediction in multiethnic cohorts,” was authored by Meri Okorie, Caroline Jonson, Alexis P. Oddi, Patricia A. Castruita, Brian Fulton-Howard, Kristine Yaffe, Jennifer S. Yokoyama, Chinedu Udeh-Momoh, and Shea J. Andrews.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AlzheimersRisk #PolygenicRiskScore #MultiethnicGenomics #CrossAncestry #GeneticPrediction #DiversityInGenetics #AlzheimersDisease #Biomarkers #BrainHealth #NeurodegenerativeResearch
-
DATE: September 5, 2026 at 12:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Scientists build a more accurate Alzheimer’s risk score using global DNA data
Researchers have developed a new genetic risk scoring method that improves the prediction of Alzheimer’s disease across diverse populations, moving beyond the historical reliance on European genetic data. The findings indicate that blending genetic information from multiple global groups also helps predict biological markers of the disease. The research was published in Alzheimer’s & Dementia.
Alzheimer’s disease is a progressive neurological condition that slowly destroys memory and thinking skills. To understand who might be at the highest risk of developing the disease, scientists often use polygenic risk scores. A polygenic risk score is a tool that adds up the tiny effects of thousands of genetic variations across a person’s entire genetic code to estimate their overall inherited risk for a specific condition.
To build these scores, researchers rely on genome-wide association studies. These are large-scale projects that scan the DNA of thousands of people to find small genetic differences linked to a disease. Historically, these massive genetic databases have overwhelmingly featured individuals of European descent. A 2016 study showed that genetic risk scores built primarily from people of European descent lose substantial accuracy when applied to other ancestral groups. Because human genetics vary slightly across populations due to demographic history, a score that works well for a European individual often fails to predict disease risk for someone of African or Asian descent.
The push to solve this problem has led scientists to gather more diverse genetic data. For example, a study covered by PsyPost in 2025 found novel Alzheimer’s-linked genes by analyzing multi-ancestry genome data. To make use of this newly available information, statisticians have been designing new ways to combine the data. A 2023 study demonstrated that building risk scores from diverse, multi-ethnic groups consistently outperforms scores built from just one population.
Building on this progress, Meri Okorie, Shea J. Andrews, and their colleagues at the University of California, San Francisco, sought to test these new scoring models for Alzheimer’s disease. They wanted to see if a cross-ancestry approach could accurately predict not just the clinical diagnosis of Alzheimer’s, but also the underlying physical damage occurring in the brain, across a multiethnic group of older adults.
The researchers gathered genetic summary data from hundreds of thousands of individuals of European, African, Admixed American, East Asian, and Caribbean Hispanic descent. The term Admixed American refers to populations, such as many Latino or Hispanic individuals, whose ancestry is a complex mix of Indigenous American, European, and African genetics.
Using this diverse data, the team built three different types of polygenic risk scores. The first was a single-ancestry score, created using only the European genetic data. The second was a multi-ancestry score, which simply pooled all the genetic data from the different groups together. The third was a cross-ancestry score, an advanced statistical model that jointly analyzed the data from multiple populations to adjust how much weight each genetic variant should carry based on shared genetic effects. The researchers also used a specialized mathematical step called ancestry normalization to ensure the scores were scaled fairly across different genetic backgrounds.
To test these three scores, the researchers applied them to two large groups of patients living in the United States. The first testing group came from the Alzheimer’s Disease Sequencing Project. This sample included 19,398 individuals, with 7,111 diagnosed with Alzheimer’s disease and 12,287 who were cognitively normal. The participants in this group were of European, Admixed American, and African descent.
The second testing group came from the Health and Aging Brain Study–Health Disparities. This group included 2,559 older adults of European, Admixed American, and African descent. For all analyses, the researchers adjusted for factors like age, sex, and the presence of the APOE gene. The APOE gene is a well-established genetic variation that strongly increases Alzheimer’s risk on its own, so controlling for it allowed the researchers to see the effect of the broader polygenic risk score.
In addition to looking at whether participants were diagnosed with Alzheimer’s, the researchers looked at objective medical measurements. These included cognitive test scores assessing memory and language, brain scans, and the analysis of cerebrospinal fluid. Cerebrospinal fluid is the liquid that surrounds the brain and spinal cord, and it can be tested for specific proteins that act as warning signs for Alzheimer’s disease.
The researchers found that the single-ancestry score, built only from European data, performed poorly outside of European populations. While it worked well for predicting Alzheimer’s risk in participants of European descent, its accuracy dropped noticeably in Admixed American participants. For participants of African ancestry, the single-ancestry score completely failed to predict Alzheimer’s disease risk.
In contrast, the cross-ancestry score showed the highest predictive performance across the board. When applied to participants of African ancestry, a higher cross-ancestry score was associated with a 71 percent increase in the odds of having an Alzheimer’s diagnosis compared to the baseline risk. This advanced scoring model also maintained high accuracy for Admixed American and European participants, providing a more balanced predictive tool for diverse populations.
Beyond predicting a clinical diagnosis, the cross-ancestry score successfully predicted physical and cognitive signs of the disease. Across the testing groups, higher cross-ancestry risk scores were tied to poorer performance in cognitive domains like memory, language, and executive function. Executive function refers to mental skills that include working memory, flexible thinking, and self-control.
The cross-ancestry score also aligned with biomarkers of the disease in the body. Individuals with higher genetic risk scores tended to have lower levels of amyloid-beta 42 in their cerebrospinal fluid. In the context of Alzheimer’s disease, a drop of this specific protein in the spinal fluid indicates that the protein is clumping together inside the brain to form harmful plaques.
The researchers also looked at autopsy data and advanced brain imaging. They found that participants with the highest cross-ancestry polygenic risk scores had a higher probability of being in the most severe categories of brain pathology. This indicates that the genetic risk score is not only catching surface-level symptoms but is reflecting the advanced physical accumulation of plaques and tangles that destroy brain tissue.
There are a few things to keep in mind regarding this study. The data lacked representation from South and East Asian populations in the testing phase, which limits the ability to know exactly how well the cross-ancestry score performs for those specific groups. Expanding genetic databases to include more global populations is an ongoing challenge in medical research.
The participants tested in this study were all residents of the United States. It is unknown if the models would perform with the exact same accuracy in populations living in other environments or continents, where different lifestyle and environmental factors are at play.
The researchers also point out that these risk scores currently explain only a modest fraction of a person’s total risk for Alzheimer’s disease. A polygenic risk score is just one piece of the puzzle. Age, sex, specific rare genes, and environmental factors like diet and exercise all interact to determine whether someone will actually develop the disease. This study did not look at how a person’s environment might modify their genetic risk.
The study, “Cross-ancestry polygenic risk scores enhance Alzheimer’s disease risk prediction in multiethnic cohorts,” was authored by Meri Okorie, Caroline Jonson, Alexis P. Oddi, Patricia A. Castruita, Brian Fulton-Howard, Kristine Yaffe, Jennifer S. Yokoyama, Chinedu Udeh-Momoh, and Shea J. Andrews.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AlzheimersRisk #PolygenicRiskScore #MultiethnicGenomics #CrossAncestry #GeneticPrediction #DiversityInGenetics #AlzheimersDisease #Biomarkers #BrainHealth #NeurodegenerativeResearch