Developmental cognitive genetics: how psychology can inform genetics and vice versa

Abstract

Developmental neuropsychology is concerned with uncovering the underlying basis of developmental disorders such as specific language impairment (SLI), developmental dyslexia, and autistic disorder. Twin and family studies indicate that genetic influences play an important part in the aetiology of all of these disorders, yet progress in identifying genes has been slow. One way forward is to cut loose from conventional clinical criteria for diagnosing disorders and to focus instead on measures of underlying cognitive mechanisms. Psychology can inform genetics by clarifying what the key dimensions are for heritable phenotypes. However, it is not a one-way street. By using genetically informative designs, one can gain insights about causal relationships between different cognitive deficits. For instance, it has been suggested that low-level auditory deficits cause phonological problems in SLI. However, a twin study showed that, although both types of deficit occur in SLI, they have quite different origins, with environmental factors more important for auditory deficit, and genes more important for deficient phonological short-term memory. Another study found that morphosyntactic deficits in SLI are also highly heritable, but have different genetic origins from impairments of phonological short-term memory. A genetic perspective shows that a search for the underlying cause of developmental disorders may be misguided, because they are complex and heterogeneous and are associated with multiple risk factors that only cause serious disability when they occur in combination.

Figure 1

Schematic showing inheritance pattern for a small stretch of DNA. The grey region indicates an allelic variant associated with disorder. The region denoted by a, b, c, or d is a highly polymorphic noncoding region that can be used as a marker, because it is unlikely that two unrelated individuals will have identical DNA in this region. Combinations a–c, a–d, b–c, and b–d for this region are equally common in the offspring, so the a priori likelihood that any two children will have 0, 1, or 2 alleles IBD is 1 : 2 : 1. If affected sib pairs show a departure from this 1 : 2 : 1 IBD ratio, this suggests that the marker is close to an allelic variant of interest. The correlation between the marker, a, and the grey region will not be perfect, because stretches of DNA can become separated in meiosis due to crossing-over: The closer the marker is to the grey region, the more likely it is to be coinherited. In another set of families, we may see different DNA variants at this locus (w, x, y, and z instead of a, b, c, and d), but the same logic is applied: Thus the key issue is not the form that the marker takes, but rather whether it has the same parental origin in two affected sibs.

Figure 2

Illustration of DeFries–Fulker analysis. Data are transformed so that the population mean = 0 and the proband mean = 1. The effect of nonshared environment on impairment (e²_g) is estimated as MZ proband mean − MZ co-twin mean. The effect of genes on impairment (h²_g) is twice the difference between MZ and DZ co-twin means. The effect of shared environment (c²_g) is 1−h²_g−e²_g.

Figure 3

Causal route from auditory temporal processing deficit to language impairment, based on Tallal (2000).

Figure 4

Mean scaled scores on two receptive language tests (TROG and WISC-R Comprehension) and two expressive language tests (Word Finding and CELF-R Recalling Sentences) for the sample tested by Bishop et al. (1999a), subdivided according to whether performance is more than 1 standard deviation below the mean (−), or above this level (+) on the the Auditory Repetition Test (ART) or Children's Nonword Repetition Test (CNRep).

Figure 5

Risk factor model, in which environmental risk for language impairment is indexed by auditory deficit, and genetic risk is indexed by a deficit in phonological STM. Risk factors are probabilistic and additive, with the genetic risk having the stronger effect.

Figure 6

Illustration of frequency of underlying deficits in children with unimpaired hearing and those with mild–moderate hearing loss. All of the hearing-impaired group have auditory deficit; this is unlikely to manifest itself as language impairment unless a genetic risk is also present. Rates of double deficit are lower in those with unimpaired hearing.