#geneenvironmentinteraction — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #geneenvironmentinteraction, aggregated by home.social.
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DATE: May 27, 2026 at 10:00PM
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: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
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
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
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It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
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DATE: May 27, 2026 at 10:00PM
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: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
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
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
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DATE: May 27, 2026 at 10:00PM
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: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
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
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
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DATE: May 13, 2026 at 06:00PM
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: Class background influences whether genetic predisposition for intelligence drives you left or right
URL: https://www.psypost.org/how-childhood-class-alters-the-genetic-pathways-of-political-ideology/
A person’s economic political views are shaped by their genetic predisposition for cognitive performance interacting with their childhood social class. People with a higher genetic likelihood for cognitive performance tend to adopt left-wing policies if they grew up poor, and right-wing policies if they grew up wealthy. The research was published in Political Psychology.
Understanding differences in economic policy preferences is a primary goal of political science. Traditional models in political economics assume that individuals will support policies that benefit them financially. In a strictly theoretical system where flat taxes are redistributed equally, anyone earning below the average income should want complete redistribution, while anyone earning above the average should oppose it. While real political systems are messier, the fundamental dynamic generally holds.
Low-income earners tend to benefit from proportional taxation and redistribution, while high-income earners bear the costs. In recent years, researchers have found that genetics also influence political behavior. Studies using various methods have documented genetic overlaps with political preferences. This overlap means that ideological preferences partially share the same genetic architecture as other measurable traits.
Since our distant ancestors did not have modern tax systems or mass political parties, evolutionary forces could not have shaped economic ideology directly. Genetic effects on these preferences must operate through intermediate traits, which scientists call endophenotypes. Some researchers proposed that cognitive performance might act as one of these intermediate traits.
The results of previous studies on cognitive performance and economic ideology, however, have been wildly inconsistent. Some studies showed a positive link between cognitive ability and economic conservatism. Other studies found a negative link, and some found no connection at all.
Rafael Ahlskog, a researcher at the Department of Government at Uppsala University in Sweden, thought these contradictory results could be reconciled. He proposed a gene-environment interaction. This occurs when a specific genetic factor behaves differently depending on the environment surrounding the individual.
Ahlskog theorized that cognitive performance does not push a person toward a specific political ideology on its own. Instead, cognitive capacity helps people analyze complicated policy packages and accurately deduce their own class interests. Modern economies feature vast arrays of diverse taxes, regulations, and benefit programs. Evaluating how these policies interact requires analytical effort.
By applying these conceptual frameworks, the study connects the theories of classical economics with modern genetics. People who find it easy to perform the mental calculations required to navigate tax proposals will optimize their policy preferences. Those who find it more difficult might answer policy questions more randomly, or they might rely on social cues not strictly tied to their personal class background.
In addition to this, political science maintains a long-standing theory regarding the impressionable years in human development. This theory states that environmental influences on attitudes are most potent during late adolescence and early adulthood. After this period, political preferences tend to stick. Based on this, Ahlskog suggested that the perception of one’s class interest is shaped primarily by the relative economic standing of their parents during these formative years.
To test these ideas, Ahlskog analyzed data from a large sample of fraternal twins from the Swedish Twin Registry born between 1943 and 1958. Fraternal twins are siblings born at the same time who share, on average, half of their genetic sequence. Using within-family differences among fraternal twins provides an excellent natural experiment for behavioral researchers.
Researchers value within-family sibling designs because comparing two people from the broader population introduces confounding variables. Between two random strangers, a genetic correlation might be skewed by regional ancestry differences or by the environmental impacts of their parents’ genes. Fraternal twins share the exact same family environment, and their genetic differences result purely from the random shuffling of DNA during conception.
Because of this randomization, systematic downstream differences in sibling behavior have a causal interpretation. Researchers can confidently conclude that the genetic difference caused the behavioral difference, rather than an unmeasured environmental factor.
To conduct the analysis, Ahlskog utilized variation in a genetic measure called a polygenic index. A polygenic index is an individual-level predictor of a specific trait that is based entirely on a person’s DNA. Geneticists build these indices by identifying thousands of tiny DNA variations known as single nucleotide polymorphisms that correlate with a target trait. The index used in this study summarized each twin’s genetic propensity for cognitive performance based on previous large-scale genomic discoveries.
He combined this genetic data with the twins’ responses to an extensive survey conducted by the Swedish Twin Registry between 2009 and 2010. The survey included a detailed battery of over thirty political preference questions. Participants rated policy proposals on a five-point scale ranging from strongly disagree to strongly agree. Ahlskog isolated twelve items specifically dealing with economic ideology, such as opinions on taxation, welfare distribution, the public sector, and government regulation.
To measure family socioeconomic standing, Ahlskog utilized Swedish registry data covering the twins’ parents. He calculated a relative affluence score by comparing the parents’ income and education levels to other adults in their specific local parishes. This provided a localized measure of class background. Sociologists have found that people typically compare their economic status to their immediate neighbors rather than the national average.
When looking at the average effect across the entire sample, the genetic measure for cognitive performance had no impact on economic conservatism. The effect size appeared as practically zero. Without looking deeper, this might seem like a simple lack of an effect.
When Ahlskog factored in the family’s socioeconomic background, the average null effect broke apart to reveal two distinct, opposing trends. Among children raised in relatively poorer families, a higher genetic index for cognitive performance caused more left-wing economic views. These individuals favored higher taxation and wealth redistribution.
Among children from affluent backgrounds, the effect reversed entirely. A higher genetic index among these privileged individuals caused more right-wing views. They favored market reliance and reduced welfare spending. The genetic factor altered how individuals optimized their political views based entirely on their childhood class.
In the scientific taxonomy of gene-environment interactions, researchers often distinguish between dimmer effects and lens effects. A dimmer effect happens when a change in the environment alters the magnitude of a genetic influence, making it stronger or weaker. A lens effect happens when the environment actually changes the direction of the genetic influence. Ahlskog’s findings represent a rare, robust example of a lens effect for a socially relevant behavior.
The researcher also controlled for the twins’ adult income and education levels. The environmental interaction held up even when accounting for later-life resources. This suggests the genetic influence operates specifically on the early-life formation of class identity, not simply on a voter’s current bank account balance.
As a placebo test to verify his theory, Ahlskog applied the same analytical models to social ideology. Social ideology involves cultural and moral issues, such as immigration, criminal justice policy, and animal rights. Unlike economic ideology, there is no direct personal financial benefit to optimizing social preferences based on household class.
In this test, he found that a higher genetic index was naturally associated with lower social conservatism across the board. The effects operated in parallel for both the rich and the poor. There was no interaction based on socioeconomic background.
The study features a few limitations and caveats. The genetic predictor is a noisy measurement that only captures a fraction of the actual heritable traits for cognitive performance. Comparing genetic differences within local twin pairs amplifies this measurement noise even further. As a result, the reported effects are likely much smaller than the actual biological impact.
The geographical and historical realities of the respondent group also matter. The individuals in this sample grew up in Sweden during the middle of the twentieth century, a period defined by the rapid expansion of the modern welfare state. Class-based politics and labor movements were highly salient in their daily lives.
The findings might look completely different in populations where economic ideology is not the primary dividing line in public debate. In political environments where left-wing economic positions are championed by socially conservative populists, the class dynamics could manifest in alternate ways. Finding out which specific political relationships are affected by changing social cultures will require further study.
Ultimately, the findings demonstrate that genetic influences on political behavior are highly contingent on social environments. An effect that appears to be mathematically zero on average can obscure shifting dynamics beneath the surface. This heavy dependency on outside environmental factors functions as a strong argument against genetic determinism.
The study, “Class, genes, and rationality: A gene-environment interaction approach to ideology,” was authored by Rafael Ahlskog.
URL: https://www.psypost.org/how-childhood-class-alters-the-genetic-pathways-of-political-ideology/
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
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
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #GeneticsAndPolitics #CognitivePerformance #GeneEnvironmentInteraction #ClassBackground #EconomicIdeology #LeftWingRightWing #TaxPolicy #WelfarePolicy #PoliticalPsychology #TwinStudy
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https://www.europesays.com/uk/470886/ Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE | Genome Biology #AnimalGeneticsAndGenomics #Bioinformatics #EvolutionaryBiology #GeneEnvironmentInteraction #GeneticCovariance #Genetics #GenomeWideAssociationStudy #GenomeWideInteractionScan #HumanGenetics #LinkageDisequilibrium #MicrobialGeneticsAndGenomics #PlantGeneticsAndGenomics #Science #StatisticalPower #UK #UnitedKingdom