Category Archives: digitalsociology

Digital sociology project summary

Over the course of the semester, I have been exploring the topic of race and genetics research. The first blog post (In brief) examined the role of expertise and how intersectional factors may influence who is considered an expert. While I intended this post to somehow lead towards validating the scientists when interpreting genetic findings, my research veered away from the scientists and analyzed the scientific basis for claiming genetic differences between groups of people.

As a result, the second blog post (In pictures) looks at some of the available images online obtained through a simple keyword search, which have been used by various individuals on blogs or discussion forums as evidence either for the biological nature of racial differences or the socioeconomic disadvantages/cultural construction of race.  This post also engaged some of the sociological literature around the problem of valuing problematic scientific assertions over the social, historical, and cultural context for defining race.

The third post (In trends) showed several statistical images and graphics supporting this narrative as well as additional sociological literature. Scientists have illustrated that a close correlation exists between genes and geography. However, Europe’s geography has a long history that likely contextualizes these genetic clusters and coincides with racial/ethnic classifications. This kind of discovery provides support for the viability of using genetic testing for ancestry, but also presents a significant problem for defining racial/ethnic groups in time. For example, when is a person’s heritage identified as South African versus Dutch (the Boer colonists in South Africa during the late seventeenth century)? The modern answer seems to depend on their physical appearance: white = Dutch and black = South Africa.  Despite these issues, the consumer market for genetic testing has boomed and is expected to reach $340 million by 2022.

Finally, the latest blog post (Culture) views the consumer genetic testing market for its potential benefits and inherent problems. These at-home test kits have wide appeal to millions of curious consumers, but contain black-boxed science and likely problematic methodologies.

Why does this topic matter? Science has a cache for being empirical and objective. The popularity of the at-home genetic testing kits sold to millions of consumers reinforces the bad notion that race is determinable from our genes, and therefore biological, rather than socially constructed. The social construction of race actually forms the fundamental basis for scientific categorization, but scientists get caught up in their own scientific claims or a normal person can’t get through the technical nature of scientific writing enough to decipher  this message. Alternatively, a company may be motivated by profit to make the science sound much more certain. The direct-to-consumer genetic testing industry is growing, which also indicates that the belief in the biological construction of race might grow too.

Culture: genetics testing and uncertainties

Genetics research revolutionized the modern world through its various applications on genetically modified crops, cloning sheep, and gene editing for embryos. These topics caused controversies over tampering with the natural order and ethical considerations about setting boundaries for scientific research at a societal level. However, various companies offer direct-to-consumer products relevant on a more individual level. These genetic testing kits promote a healthier life though knowledge about your risks for certain diseases or more effective drug treatments. For example, BRCA gene mutations increase a woman’s risk of breast or ovarian cancers. In 2013, the Food and Drug Administration issued warning on companies marketing their products for diagnosing and treating medical conditions without sufficient evidence of accuracy for these purposes.

However, the direct-to-consumer genetic testing market remains robust. The Guardian recently cited the lucrative market for consumer genetic testing at an estimated $70 million in 2015. MIT Technology Review reported that AncestryDNA and 23andMe each announced their millionth consumer sample in 2015. One recent article quoted a nonprofit advocate that made an analogy between a woman learning she is pregnant through a test commonly available on a drugstore shelf and where personal genetic testing kits will be in ten years.

The average consumer cost for these genetic testing kits ranges widely, but the U.S. National Library of Medicine states the average cost for at-home genetic testing ranges from several hundred dollars to more than a thousand dollars. Another informational website estimates the cost as $69 to $1,399. The cheaper tests provide basic genealogical data, but the more expensive tests might examine multiple chromosomal inheritances (showing the paternal line, the maternal line, or from both parents). The higher end tests also indicate health risks based on disease markers. For example, 23andMe offers their ancestry service for $99 and their health plus ancestry service for $199.

Why have these at-home genetic testing been popular with consumers? AncestryDNA advertises that genealogical tests will help discover “who your ancestors were and where they came from” with tantalizing potential results “from discovering their ethnicity to connecting with distant relatives.” This might appeal to adopted individuals that reach a dead end in their search for their birth family. This also appeals to individuals that cannot use official records to research their familial origins, such African-Americans. Based on research for her recent book, The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome, Alondra Nelson illustrates how many African-Americans have been curious about their ancestors from Africa and genetic testing offers a scientific method to get answers. However, the genealogical results have done more than provide a sense of ethnic identity. The information also becomes relevant for political uses, such as proving lineage in legal cases to seek reparations for slavery or seeking dual citizenship in African countries.

If we assume that the companies cannot be held responsible for what consumers do with their genetic information, then what other concerns exist about the widespread availability of these tests? First, the cost provides a possible opportunity for socioeconomic inequality, especially when companies advertise the benefits to learning about health-related risks. This places a value on foreknowledge about genetic predispositions or likelihood for certain diseases.

The test results also have the potential for unsettling or unexpected results. For example, white supremacists use genetic testing to brag about their “pure blood,” but sometimes get “bad news.” Researchers studied an online white nationalist community and found the two most common responses: rejecting the test’s validity or rationalizing the result as statistical error. While it might be simple to dismiss these reactions, direct-to-consumer genetic testing is not foolproof science.

The companies offering at-home testing kits have a profit-seeking motive for their products. As a result, they each claim their algorithms and sample populations as intellectual property. Independent scientific experts cannot assess the validity and reliability for these proprietary methods. In addition, the original genetic “admixture” research has fundamental flaws that do not get communicated to the consumer. The Human Genome Project found that all human beings share 99.9% of their genes. In other words, all genetic differences come from 0.1% of the human genome. 85% of the variation occurs within geographically distinct groups and only 15% occurs between these groups. Therefore, genetic testing claims to make statistically significant genealogical distinctions from 15% out of the 0.1%. In addition, no one has “pure” samples for reference from past populations. Companies use “reference samples” that come from modern populations with untestable sample validity. For example, 23andMe claims to “include genomes from 10,418 people who were carefully chosen to reflect populations that existed before transcontinental travel and migration were common (at least 500 years ago).” Are 10,418 people sufficient to account for each Native American nation or African tribe?

Troy Duster, former President of the American Sociological Association, raised his own concerns over genetic science and the implications for race. Genetic science casts scientific legitimacy around the idea that race has a biological basis. Duster (2015) firmly argues that race is socially constructed and social scientists need to reengage in debates about race. When carefully examined, the racial categories used by scientists in genetic research have fundamental assumptions based on social, historical, and folk knowledge. For example, Germany did not exist as a single nation until 1871. If a genetic test goes back 500 years, then would it actually identify someone’s ancestors as specifically Prussian or Bavarian? 23andMe states that “most country-level populations overlap to some degree” and “some genetic ancestries are inherently difficult to tell apart because the people in those regions mixed throughout history or have shared history.” However, an ancestry composition report provides a neat chart with percentages adding up to 100%.

Disclaimer: I received a testing kit as a gift in 2013. My ancestry results seem consistent with expectations, although the specific percentages of variation is surprising.

A final issue to mention about direct-to-consumer genetic testing kits is their fine print. Many people simply don’t read the fine print when signing up for a service or providing consent. Companies make a higher percentage of their profits through the accumulation and sale of genetic datasets for medical research than through sales of the test kits. While the companies claim to anonymize and aggregate the data, researchers found that it is possible to identify men through their genome.

Although consumer genetic testing has promise for improving our health and discovering more about our ancestral heritage, the cons currently seem to outweigh the pros in purchasing a kit. One woman bought a test for fun and found a genetic mystery leading to a discovery about babies switched at birth. My key takeaways regarding these tests would be 1) beware of opening Pandora’s box and 2) interpret your results with a grain of salt.

 

in trends

Omi and Winant (2014) “stress that race is a social construction, and not a fixed, static category rooted in some notion of innate biological differences.” Social, economic, and political forces, across time and place, have influenced racial meanings. Many scientists hoped that mapping our genome would provide definitive evidence that there is no such thing biologically as race. Humans share over 99% of the same genome.

However, some scientists have chosen to concentrate on the last partial percentage as potentially significant genetic variation due to racial or ethnic differences. In an ideal world, scientists envisioned that these studies would target diseases and treatments for particular populations. However, this has led some to argue that a biological basis for race really exists, which also might result in rather ugly social consequences, such as rationalizing racism.

An example of a widely available image online (below) shows a statistical summary of genetic data that clusters according to the countries in Europe. While the original scientific article does not mention race, the authors found a close correspondence between genes and geography. They believed that these results supported the accuracy of genetic ancestry testing based on a sample of 1,376 Europeans. This image reappears on science blogs and as evidence on popular forums about similarities between ethnic groups based on the distance shown on the map.

Shiao, Bode, Beyer, and Selvig (2012) presented a theoretical synthesis between recent genetic ancestry research and the social construction of race. The authors accepted that statistically identifiable clusters of genes seem homologous to certain racial and ethnic classifications. However, any identifiable biological basis for race and ethnicity cannot be separated from its complex social construction across time and place.

Duster (2015) urged social researchers to critically examine the social assumptions about race that have been transmogrified as science and trace their actual origins in social, historical, and folk categories of race. Some scientists have stated that genetics research studying differences between populations is scientifically appropriate, but assigning value such as genetic superiority to their findings is politics, not science. Instead, Duster argued that politics cannot be separated from science because the use of racial and ethnic categories originally come from political taxonomies. He also cautioned against ancestry testing and admixture research (a combination of genetic lineages) as fundamentally flawed science as well as depending on political history to define populations. Ancestry testing relies on “reference populations” of contemporary people along with assumptions about migrations, reproductive patterns, and historical events. The actual computer models determining an individual’s results tend to be a “black box” proprietary to the companies marketing a particular test. For example, a customer might receive results that provide the appearance of precision (example below). This “professional genetic genealogist and television consultant” provides a review of four consumer tests for determining admixture, which shows differing results across the companies.

In 2014, USA Today published an opinion piece about the genealogy “craze” as the second most popular hobby and second most visited category of website (after pornography). While the author refers to the digitization of databases as the popularizing factor, consumer genetic testing also appeals to these hobbyists.

Duster believed that these results should be qualified with more caveats. For example, for a person expecting certain results, the company’s sample might not include that population. There also is no guarantee that a match of genetic markers to a certain population or geographical area means that the individual has a close affinity to a racial or ethnic group.

Duster’s article sparked controversy in the social sciences and he published a response to seven of his commentators (2015). In particular, the intersection between science and society is visible in the question of when, rather than where, an individual might claim ancestry. For example, Italy became a unified nation state in 1858. Prior to these dates, ethnic categories would be based on regional admixtures including Milanese, Roman, and Neapolitan. How do scientists determine the appropriate admixture for a single ethnic category of Italian?

He also presented the Boers in South Africa in the mid-sixteenth century as another example requiring social context. A white person born in Africa with grandparents of this ancestry would be African. However, the observable appearance of whiteness would outweigh the “scientifically neutral” ancestry analysis. Other researchers have proven that the phenotype determines results more than genotype.

The Guardian recently published an article about the booming consumer genetic testing market, worth $70 million in 2015 and expected to rise to $340 million by 2022 (chart below by Credence Research cited in the article). In addition to ancestry, consumers hope to understand the relationship between their genes and health, which might include their response to a particular type of exercise and diet.

In addition, Duster warned against the potential problems in social scientists and geneticists collaborating on research. Social scientists may not understand the scientific details of the genetics research in order to question the assumptions, which lends multi-disciplinary legitimacy to the collaboration and normalizes the science. Genomic advocates also claim benefit in new medication targeting certain populations, but this helps rationalize racial hierarchies. Who should benefit from targeted research? Marmot (2005) attributed social factors as the root cause of health inequalities, such as significant differences in life expectancy and treatment of disease. Gravlee (2009) stated that “social inequalities shape the biology of racialized groups, and embodied inequalities perpetuate a racialized view of human biology.” House (2015) recently reiterated that social policies to improve socioeconomic determinants of health would be necessary to reverse worsening health outcomes and reduce health spending in the United States. For example, the following chart (below) shows poverty rates among senior citizens by race and ethnicity in a report looking at Medicare beneficiaries conducted by the Kaiser Family Foundation.

Yudell, Roberts, DeSalle, and Tishkoff (2016) called for a systematic effort to address issues concerning the use of race in genetic research. The authors distinguished between ancestry as “a process-based concept, a statement about an individual’s relationship to other individuals… a very personal understanding of one’s genomic heritage” versus race as “a pattern-based concept… which connect an individual to a larger preconceived geographically circumscribed or socially constructed group.”

Where does this leave a consumer curious about their ancestral heritage and health-related consequences of their genome? Scientists would be the first to admit that their research is based on limited opportunity samples (present-day volunteers) and statistical correlations, rather than absolute certainty. However, companies marketing their consumer genetic tests seem unlikely to mention the assumptions and potential inaccuracies in their product. These companies also profit from selling the data collected from their customers to pharmaceutical and biotech firms. In addition, sociologists should continue to discuss the social and historical basis for determining race and racism, which rejects a biological explanation as insufficient for the complexity in these issues. There also is room for further intersectional analysis of the socioeconomic determinants of health in determining future directions for genetic research to improve health in disadvantaged populations.

 

Bibliography
Duster, T. (2015a). A post-genomic surprise. The molecular reinscription of race in science, law and medicine. The British Journal of Sociology, 66(1), 1–27. https://doi.org/10.1111/1468-4446.12118
Duster, T. (2015b). Response to comments on ‘A post-genomic surprise.’ The British Journal of Sociology, 66(1), 83–92. https://doi.org/10.1111/1468-4446.12117_8
Gravlee, C. C. (2009). How race becomes biology: Embodiment of social inequality. American Journal of Physical Anthropology, 139(1), 47–57. https://doi.org/10.1002/ajpa.20983
House, J. S. (2016). Social Determinants and Disparities in Health: Their Crucifixion, Resurrection, and Ultimate Triumph(?) in Health Policy. Journal of Health Politics, Policy and Law, 41(4), 599–626. https://doi.org/10.1215/03616878-3620845
Marmot, M. (2005). Social determinants of health inequalities. The Lancet, 365(9464), 1099–1104. https://doi.org/10.1016/S0140-6736(05)71146-6
Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A. R., Auton, A., … Bustamante, C. D. (2008). Genes mirror geography within Europe. Nature, 456(7218), 98–101. https://doi.org/10.1038/nature07331
Omi, M., & Winant, H. (2014) Racial Formation in the United States, 3rd edition. Routledge.
Shiao, J. L., Bode, T., Beyer, A., & Selvig, D. (2012). The Genomic Challenge to the Social Construction of Race. Sociological Theory, 30(2), 67–88.
Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016). Taking race out of human genetics. Science, 351(6273), 564–565. https://doi.org/10.1126/science.aac4951

in pictures

The Human Genome Project held great promise for advances in medicine, but also had greater societal implications. Many scientists hoped that mapping our genome would provide definitive evidence that there is no such thing biologically as race. Other researchers argued that race is a biological concept and real differences exist between races. For example, Race, Evolution, and Behavior: A Life History Perspective by J. Phillipe Rushton (1995) claimed the existence of at least three basic races with the following controversial average differences:

However, other research argues that socioeconomic inequalities have been the root cause of perceived and actual differences between races. For example, the following table indicates one effect of low socioeconomic status and race on life chances, specifically going to college. The chart, however, is not meant to dismiss the cost of disadvantage to being black in comparison to low socioeconomic status because it is citing the gap in student scores between blacks and whites (not an apples to apples comparison). The context of the web article using the chart is for achieving better diversity in higher education through affirmative action. As a result, the takeaway is that the combination of race and low socioeconomic status results in significant disadvantages that would benefit from class-based affirmative action programs.

Genetics research held the potential to “settle” these arguments, but it ultimately resulted in greater uncertainty. Duster (2015) describes the “inadvertent and unintended spin-offs” of genetics research through racial and ethnic markers in forensics to solve crimes, marketing unreliable ancestry analysis based on low validity samples, targeting clinical treatments for specific populations, or looking for genetic causes for certain diseases in specific populations. (p.4) He brings up the concern that racial assumptions heavily rely on “social, historical, and folk categories, but are then transmogrified into the language of science and anointed with an imprimatur of legitimacy.” (p.23)

This leads to discovering the following graphically unsophisticated infographic (with slight variation as individual slides) of unknown origin floating around on online discussion boards and message forums, usually as “proof” about the biological basis for race. The infographic presents a danger on social media because the slides make a claim for credibility with scientific charts and academic citations. (The myths have been reordered below into groupings for further discussion).

  • Myth #1: Race has no biological basis.
  • Myth #3: Race is only skin-deep.
  • Myth #4: Races have more variation within them than between them.
  • Myth #6: There isn’t significant genetic difference between races.
  • Myth #7: There isn’t qualitative genetic evidence for racial differences.

Frank (2015) discusses that over 99.9% of the human genome is the same. However, ancestry testing companies have marketed their findings as definitive while their scientific basis is problematic. Biogeographical ancestries compare an individual’s genes to a sample of present-day populations of geographical regions. As Duster also mentions, statistical assumptions have been made that would appear no more “logical” or “objective” than socially constructed definitions of race.

  • Myth #2: Race is a social construct.

Frank admits that there seems to be a weak correlation between biogeographical ancestry and race. However, she also points out that genetic researchers heavily rely upon social definitions of race for “creating, informing, and interpreting supposedly value-neutral genetic facts about the nature of human variation.” (p.58)

According to Shiao, et. al. (2012), a theoretical synthesis is possible between the social construction of race and the categorization of statistically discernible alleles into clinal classes.  These classes would be the outliers in “otherwise continuous genetic variation, similar to social classes in otherwise continuous economic variation.” (p.69) Rather than completely rejecting the idea of genetics in racial constructionism, the authors also suggest  “a version of the feminist distinction between biological sex and socially constructed gender. (p.72)

  • Myth #5: Racial differences in intelligence are explained by socio-economic factors.
  • Myth #8: Individual successes disprove the relevance of race.

As mentioned before, socioeconomic and racial factors affect an individual’s life chances resulting in stratification. Advantages and disadvantages often continue onto the next generation of children. Most of the charts shown capture information from one point in time. A gap exists between white and black achievement “at all levels.” However, what would a generational study show? Would educational achievement be increased in children if their parents had done better than the previous generation, etc.?

Why does this matter? Phelan, et. al. (2013) used surveys to test two competing vignettes in news stories: race as genetic versus race as social construction. The authors found that because news articles about race, health, and genetics have become commonplace enough seem neutral, which “circumvents our usual tendency to check incoming persuasive messages against our preexisting social attitudes; and that the public generalizes messages about specific, genetically based racial differences in health to broader, more fundamental or essential, genetic differences between racial groups.” (p.185) The sociological challenge is that “race” brought up most of the time has its basis in social construction. Genetically based race has more limited and specific uses, such as medical research. The sociological challenge may be to reinforce this distinction that most messages about race will be a commentary about society and not genetics, while continuing to navigate the cultural sensitivities around the topic.

 

Duster, Troy. 2015. “A post-genomic surprise. The molecular reinscription of race in science, law and medicine.” The British Journal of Sociology 66:1–27.

Frank, Reanne. 2015. “Back to the Future? The Emergence of a Geneticized Conceptualization of Race in Sociology.” The ANNALS of the American Academy of Political and Social Science 661:51–64.

Phelan, Jo C., Bruce G. Link, and Naumi M. Feldman. 2013. “The Genomic Revolution and Beliefs about Essential Racial Differences: A Backdoor to Eugenics?” American Sociological Review 78:167–191.

Shiao, J., Bode, T., Beyer, A., & Selvig, D. (2012). The Genomic Challenge to the Social Construction of Race. Sociological Theory, 30(2), 67-88.

Genetics and the social construction of race bibliography

My topic has meandered through science and settled on the discussion of what genetic research means for race. Here’s the working bibliography organized by searches. I find the political and legal ramifications an interesting effect, the “so what” factor of why the possibility the genetic research might define racial differences or reaffirm the social construction of race.

in brief

The sociology of expertise is a relatively new field with contributions from the sociology of professions, sociology of work, and sociology of science and technology. Maria Azocar and Myra Marx Ferree, in the journal Sociology Compass, reviews this past work and shows how applying intersectionality offers a new perspective on gendered expertise.

The intersectional approach is defined as analyzing social inequalities and systems of power, looking at specific experiences within their context resulting in an unequal power relationship, and recognizing the role knowledge has in maintaining or changing these relationships. In each of the three following examples, a particular group has been excluded or devalued as experts in the narrative.

Different professions distinguished themselves by competing for a monopoly over knowledge, which made expertise the core aspect of this power battle. For example, male criminal lawyers translated their gender and class into credibility for asserting their superior expertise over female lawyers in Chile. An intersectional perspective contributes here by recognizing the social construction of various aspects of expertise, such as skills or job requirements, as gendered.

For sociology of work, symbolic interactionism influences perceptions of certain groups. For example, abilities related to nurturing have been categorized as women’s work. As a result, female nurses have been devalued and underpaid for “naturally female skills” compared to male doctors with expertise. Racist controlling images also influence how certain groups gets treated in the workplace. For example, black women might have to overcome a modern version of the “mammy” stereotype. Black men may have to downplay their feelings to avoid being perceived as angry from seeing racial discrimination everywhere.

In science and technology studies, the actor-network theory examines the conditions under expertise is created and disseminated in a network. For example, one particular scholarly work emphasizes only the male heroes in the narrative of recognizing autism as an epidemic. This analysis of power focuses on expert statements, but neglects the historical context of race, gender, and class that may influence the narrative.

Since the study of expertise within sociology is relatively new, the authors would suggest with these examples that incorporating an intersectional approach early would be beneficial for producing richer research.