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Genetic and shared environmental influences on brain development from birth to age 2-years Timothy C. Bates University of Edinburgh Mike Neale Virginia Institute of Psychiatric Genetics John Gilmore University of North Carolina

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Foetal brain Illustration by Helen Spiers

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Brain development and growth Single most important biological marker for cognition (McDaniel, 2005) Linked to psychiatric outcomes including schizophrenia (Steen, Mull, Mcclure, Hamer, & Lieberman, 2006)

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Lifetime consequence, early growth Much of this critical growth occurs in the earliest years of life Cortical gray matter more than doubles in the first year (Gilmore et al., 2012). Influences known from adult studies Parental social and economic status (SES) (Hackman & Farah, 2009).

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Logarithmic link of cortical volume to SES (Noble et al. 2015)

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Forbes magazine

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Roslin Institute Common ancestor ~100 generations ago (h 2 growth ~ 0.3) However: Genes can effect biological volumes

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However Brain volumes are a highly heritable endophenotype Blokland, de Zubicaray, McMahon, & Wright, 2012 Morphology & psychological function genetic correlated Posthuma et al., 2002. Specific genetic variants identified influencing structural volumes Hibar et al., 2015; Ikram et al., 2012.

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Genes found for specific brain regional volumes

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Genetic or Environmental? No studies have combined genetic and neurological examination in infancy

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Prior data Knowledge largely from studies of adults and children Nature and number of influences during earliest years of infant brain development virtually unknown (Gilmore et al., 2012) Potential interaction effects with SES in the expression of genetic potential untested

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Unique infant twin sample Assessed longitudinally from shortly after birth through age-2 in three consecutive waves of brain imaging Characterized on family environment. 285 infants 174 DZ (87 pairs) 106 MZ (53 pairs)

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Landmark studies from 4-20 years (Geed et al., 1999) Large increases in gray and white matter from ages 4- thru 20-years Gray matter volumes similarly increasing until a period of volume-reduction initiating at adolescence Giedd et al. ( 1999) These studies also showed the importance of temporal patterning per se IQ is associated with cortical thickness in 6 to 11 year olds (Karama et al., 2009) but also Complex and non-linear pattern of prolonged cortical thickening (Shaw et al., 2006).

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Cortical thickness linked to IQ Shaw et al. (2006)

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Plasticity higher in high-IQ kids Shaw (2006)

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Shaw (2006): Higher IQ association with delay growth, and extended pruning

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Prior to age-4 Much less is known about the nature and time course of brain development. Heritabilities surprisingly high early in life. Note: studies of IQ indicate much lower heritability in children compared to adults (Haworth et al., 2010)

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Neonate heritabilities Analyses of the neonatal wave of the present sample indicated heritability values only slightly lower than those found in child samples h 2 of.73 and.85 for total intracranial and white matter volumes respectively (Gilmore et al., 2010). Period from birth to age 2 is associated both with rapid increases in gray and white matter volumes and, in one study, with the emergence of gene environment interaction (Tucker-Drob, Rhemtulla, Harden, Turkheimer, & Fask, 2011) Potential for complex interactions, and multiple genetic and environmental factors across time or operating at one, but not other developmental windows.

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Present Study Initial Steps Characterise Genetic and environmental factors influencing whole-brain volumetric development Derived separately for gray and for white matter Neonatal, and 1- and 2-years of age Testing: Continuities discontinuities modes of operation including gene environment interactions.

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Subjects This paper reports on subjects taking part in the large, prospective study of early brain development (Gilmore et al., 2010). Subjects were recruited prenatally and scanned shortly after birth, and at ages 1 and 2 years. Also assessed with the Mullen Scales of Early Learning (Mullen, 1995) at ages 1 and 2 years. Neonates were scanned at a median age of 35 days (range 9 and 161 days). Year 1 scans took place at a median age of 402 days (min 351, max 511), and year 2 follow-up scans at 770 days (min 661, max 879).

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Method MRI scans were performed on a Siemens 3T head-only scanner (Allegra, Siemens Medical System, Erlangen, Germany). The infants were not sedated, were fed before scanning, and given ear protection. To reduce movement artifact, the head was aligned in using a weak vacuum-cup. A nurse was present during all scans Heart rate and oxygen saturation were monitored with a pulse oximeter.

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Scans T1-weighted structural pulse sequences were a 3D magnetization prepared rapid gradient echo (MP- RAGE time repetition [TR] = 1820 ms, inversion time = 1100 ms, echo time = 4.38 ms, flip angle = 7, and n = 144). Proton density and T2-weighted images were obtained with a turbo spin echo sequence (TR = 6200 ms, time echo [TE]1 = 20 ms, TE2 = 119 ms, and flip angle 150). Spatial resolution was 1 1 1-mm voxel for T1-weighted images, 1.25 1.25 1.5-mm voxel with 0.5-mm inter-slice gap for proton density/T2- weight images. For children who failed or were deemed likely to fail due to difficulty sleeping, a fast sequence was done; 12 neonates had a fast T2 scan with a decreased TR, image matrix and number of slices (5270 ms, 104 3 256 mm, 50) and 3 one year olds had a fast T1 scan with a decreased image matrix (144 3 256 mm). A clinical radiologist evaluated all scans; no gross abnormalities were reported.

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Quantization Pre-processing Adaptive fuzzy c-means automatic brain tissue segmentation method was performed to correct intensity inhomogeneity (Pham & Prince, 1999). Volumes of white and gray matter were quantitated automatically Atlas-moderated iterative expectation maximization segmentation algorithm followed by parcellation achieved by nonlinear warping (Gilmore et al., 2007) Subject-specific tissue probabilistic maps. Maximize precision and longitudinally consistency of segmentation results Parcels aggregated to form whole-brain volume vectors for analysis.

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Mapping and Segmentation Gray and white matter (GM and WM respectively), and cerebrospinal fluid (CSF) maps in 2-year olds were used as probabilistic atlases to guide segmentation of 2- week-old and 1-year-old images of the same subject using an iterative, simultaneous, registration and segmentation framework. (Gilmore et al., 2012; Shi et al., 2011). Based on this 4D segmentation and registration algorithm, GM and WM volumes at were extracted from 90 regions identified at each age. T1 images were used for 1 and 2 year olds and T2 images for neonates.

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Present Study: Hypotheses Characterise Genetic and environmental factors influencing whole-brain volumetric development Derived separately for gray and for white matter Neonatal, and 1- and 2-years of age Testing: Continuities discontinuities modes of operation including gene environment interactions.

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Heritability across time The heritability of volumes may alter with time. Heritability of IQ increased from childhood into old age (Deary, Spinath, & Bates, 2006) But heritability of gray & white matter volumes high in neonates (Gilmore et al., 2012) Prediction 1: Heritability at 1 & 2 should exceed that found in neonates. Contrasting model: Unlike IQ gray and white matter volumes always under strong genetic influence

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Shared environments: Magnitude and trajectory Existing theories make strongly contrasting predictions Model 1: Large role of shared environment Model 2: Model 1: Mostly genetic

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Model 1: Large role of shared environment Large role of shared environment on IQ scores prior to school Olson et al., 2014; Tucker-Drob et al., 2011 SES implicated in brain development Noble & Farah, 2013 Predict C increasing with time (and exposure to the rate-limiting environment)

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Model 2: Model 1: Mostly genetic Very high heritability suggests little room for shared environment Familial influences on brain development may be confounding genetic effects. Contrasting prediction: minimal shared environment at all three waves.

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Developmental Hypotheses: Almost nothing is known Number of genetic and environmental factors impacting brain development over this period Time-course Increasing Decreasing Limited to a single ages Compensatory reversals of effect over time.

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One common genetic factor The generalist genes model of cognitive ability (Kovas & Plomin, 2006) Relative lack of genetic innovation in intelligence from pre-school to the teens (Bartels, Rietveld, Van Baal, & Boomsma, 2002) Predict that this heritable component will be carried by a single vector.

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Alternative hypothesis: Multiple factors: Unclear how brain development is impacted by the genome during these early periods of rapid growth: genes act (albeit with diverse mechanisms) so as to exert a monolithic effect two, three, or more distinct patterns, some with time- limited influence. Predict: General influences AND time-delimited or gray-white matter specific actions.

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Longitudinal impact of family environment Simplest model suggests a single family environment factor Significant and constant influence on gray and white matter volumes Perhaps declining in influence with time.

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Canalization model of volume growth Distinct predictions emerge from models in which brain development is tightly canalized internal programs causing brain to remain on a programmed course Able to recover from external shocks or stresses to regain this programmed trajectory via compensatory control mechanisms (Giedd et al., 1999; Gilmore et al., 2010). Applied to brain development suggests that environmental influences should be impact brain volume, but that these should decay and be reversed.

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Impact of Shared environment reversed by canalized growth program Familial environmental effects compensated (reversed) by canalization processes Waddington, 1957 Quantitatively, this would manifest as a factor strongly loading on shared environment, and showing sign-reversal between its neonatal and year 1 and 2 loadings

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4 Pathways to Brain Growth

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Factor 1: largely C

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Factor 3: Largely A

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Factor 2: A+E

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Factor 4: A+C

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4-factors over time

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4 Pathways to Brain Growth

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Time Course

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Putting it all together longitudinally

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Interactions: Gene x SES A main effect model (Bates et al, 2015)

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Gene SES moderation of genetic variance

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Scarr-Salapatek (1971)

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Worlds literature (Tucker-Drob & Bates, 2015)

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G x SES and Brain Development Main effect: significant associations of both gray- and white-matter volumes with parental SES. G x SES amplification of childrens genetic potential Tucker-Drob & Bates, 2015 Bates, Lewis, & Weiss, 2013 Predict gene SES interactions may be present, with increased heritability of brain volumes among higher SES infants.

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Logarithmic link of cortical volume to SES (Noble et al. 2015)

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Results: No support for G x SES (and reverse/wrong trend)

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Psychometric IQ result

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Annual Reviews Why no G x SES? Effects on a microscopic level?

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The post-synaptic density and IQ (Hill, Bates, & Deary, 2014)

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Gene interactions later in life? (then why are early years important?

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Forbes magazine GENES

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SES in parents partly driven by their own genes, which they pass on Ritchie, Plomin & Bates (2014)

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Summary: No evidence for interaction up to age 3 for gray or white matter

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Infant brain: Complex, Canalized, and Highly Heritable