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Beyond quantitative and qualitative traits: three telling cases in the life sciences

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Abstract

This paper challenges the common assumption that some phenotypic traits are quantitative while others are qualitative. The distinction between these two kinds of traits is widely influential in biological and biomedical research as well as in scientific education and communication. This is probably due to both historical and epistemological reasons. However, the quantitative/qualitative distinction involves a variety of simplifications on the genetic causes of phenotypic variability and on the development of complex traits. Here, I examine three cases from the life sciences that show inconsistencies in the distinction: Mendelian traits (dwarfism and pigmentation in plant and animal models), Mendelian diseases (phenylketonuria), and polygenic mental disorders (schizophrenia). I show that these traits can be framed both quantitatively and qualitatively depending, for instance, on the methods through which they are investigated and on specific epistemic purposes (e.g., clinical diagnosis versus causal explanation). This suggests that the received view of quantitative and qualitative traits has a limited heuristic power—limited to some local contexts or to the specific methodologies adopted. Throughout the paper, I provide directions for framing phenotypes beyond the quantitative/qualitative distinction. I conclude by pointing at the necessity of developing a principled characterisation of what phenotypic traits, in general, are.

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Notes

  1. This standard reconstruction is, however, historically inaccurate (see Footnote #8).

  2. Note that this idealised explanation represents a standard one in quantitative genetics (e.g., Pierce 2017; Purcell 2013).

  3. For some critical discussions on nature/nurture and heritability analyses, see Block (1995), Lewontin (1974) and Tabery (2014).

  4. Mather (1941, 1943) originally called “oligogenic” the inheritance pattern characterising these traits and defined it as involving one or a few genes. However, over time, the term ‘oligogenic’ became ambiguous and almost synonymic with ‘monogenic’ (see, e.g., Katsanis 2016).

  5. For a comprehensive database of Mendelian traits, see https://omim.org (Accessed 18 August 2019). See also https://www.who.int/genomics/public/geneticdiseases/en/index2.html (Accessed 19 July 2019).

  6. However, these limitations are uniform across the searched titles; so, the research should not present any biases when examining trends in the use of the terms over time or across research areas.

  7. Note that, although most biological characteristics are thought to be quantitative, much more attention in genetics textbooks is given to Mendelian traits—the analysis of quantitative genetics is usually confined to a chapter towards the end of the book (e.g., Hartl and Jones 1998; Pierce 2017; Snustad and Simmons 2012).

  8. The unification of biometrics and Mendelism is usually attributed to Fisher’s 1918 infinitesimal model (e.g., Morrison 2007; Plomin et al. 2013; Visscher and Goddard 2019). However, well before Fisher, scholars from both sides achieved similar results or proposed ways for bridging the gap between the two, including East (1910), Johannsen (1903), Nilsson-Ehle (1909), Pearson (1900), Tammes (1911), Yule (1902), but also Mendel himself as well as Weldon in unpublished works (see Cock 1973; Jamieson and Radick 2013; Müller-Wille and Richmond 2016; Porter 2005; Radick 2005; Roll-Hansen 1978; Stamhuis 1995). I thank Staffan Müller-Wille (personal communication, January 2017) and Ida H. Stamhuis (July 2019) for pointing at these works.

  9. On the concept of genetic difference-maker and other definitions of genetic causality, see Burian and Kampourakis (2013), Griffiths and Stotz (2013), Okasha (2009) and Waters (2007).

  10. Note, for instance, that many traits that have been long considered Mendelian or monogenic, like eye and hair colour, have proved to be polygenic (see McDonald 2012; Sturm and Frudakis 2004).

  11. I thank James DiFrisco for pointing at this literature (personal communication, August 2018).

  12. In metaphysical terms, characters and states can be considered determinables and determinates, respectively (on this distinction, see Wilson 2017). For instance, ‘red eye’ and ‘brown eye’ are determinates of the determinable ‘eye colour.’.

  13. Note that my definition of characters and states is consistent with Lawrence’s (2008) but departs from that of scholars working on homology. For instance, according to Wagner, “the relationship between character identity and character states is the same as that for gene identity and alleles in genetics” (2014, pp. 53–54). This seems to imply that there is a one-to-one relationship between, for instance, the alleles a, b and c of the gene x and the relative states a*, b*, and c* of the character x*. However, in my definition, the relationship between a gene and its possible alleles (at the genotypic level) and a character and its possible states (at the phenotypic level) is just analogical and should not be understood in causal terms. Indeed, different states of the same character can have different types of developmental causes. For instance, the state a* can be due to just one difference-maker, but the state b* can be influenced by many genes or involve environmental influences. Moreover, the developmental causes of a character can differ greatly from those of its states. For example, the character ‘eye colour’ in flies develops under the influences of many genes, but the state ‘red eye’ can depend on just one genetic difference-maker. In other words, the species-specific development of a trait on the one hand, and the development of specific variants of such trait on the other, can be due to (partly) different developmental causes.

  14. For instance, at the behavioural level, IQ represents a single dimension on which all individuals can be placed. However, this dimension does not correspond to a single cognitive or biological phenomenon: rather, the behavioural generality of intelligence is realised by the interaction between many cognitive and neurobiological processes, e.g., working memory, processing speed, reasoning, metacognition, and neural plasticity, as well as linguistic, mathematical, and visuospatial abilities (see Kovacs and Conway 2016; Kray and Frensch 2002; Serpico 2018; Van der Maas et al. 2006).

  15. Here, I refer to the definition of schizophrenia adopted in contemporary psychiatric nosography (i.e., the one usually cited by behavioural geneticists), which is mostly based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), now at its fifth edition (APA 2013).

  16. Diagnostic criteria for schizophrenia involve other aspects concerning, e.g., the level of social functioning and the persistence of symptoms over time. These aspects are not relevant to my discussion.

  17. Note that, although diagnosis remains categorical, the latest edition of DSM includes a sort of ‘spectrum’ of psychotic disorders, where some conditions are characterised by fewer (or less severe) symptoms than major disorders (APA 2013, p. 122; see also Fusar-Poli et al. 2013). I thank Valentina Petrolini for pointing at this literature (personal communication, January 2020). About categorical versus dimensional approaches in psychiatry, see Keil et al. (2017).

  18. While the name ‘threshold model’ is somewhat standard, ‘quantitative-liability model’ is of my choice.

  19. On this view, mental disorders would correspond to so-called practical kinds (see Haslam 2014; Zachar 2000).

  20. Note that the model assumes that there is a frequency distribution for the severity of every symptom, and each person displays all symptoms to some degree (Jang 2005, p. 47).

  21. It is well possible that more than one schizophrenia state will be identified, each of which associated with its own typical symptoms, biomarkers, and aetiologies. This would not represent an obstacle for my proposal to conceive of schizophrenia(s) as a state(s) instead of a character(s).

  22. On this view, the schizophrenia state(s) would be stably associated with some symptoms and biomarkers, and this cluster of properties would allow us to distinguish between the schizophrenia state(s) and other possible states of the neuroendocrine-metabolic system.

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Acknowledgements

This research was supported by the University of Genoa. The funding source had no role other than financial support. I thank Andrea Borghini, James DiFrisco, Kate E. Lynch, Staffan Müller-Wille, Valentina Petrolini, John R. G. Turner, Marco Viola, and two anonymous reviewers for their feedback on previous versions of the manuscript. I also thank Cristina Amoretti, John Dupré, Flavia Fabris, Marcello Frixione, Gregory Radick, and Luca Rivelli for fruitful and enriching discussions on the topic.

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Serpico, D. Beyond quantitative and qualitative traits: three telling cases in the life sciences. Biol Philos 35, 34 (2020). https://doi.org/10.1007/s10539-020-09750-6

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