Quantitative genetics of sleep in inbred mice.

The timing and the organization of sleep architecture are mainly controlled by the circadian system, while sleep need and intensity are regulated by a homeostatic process. How independent these two systems are in regulating sleep is not well understood. In contrast to the impressive progress in the molecular genetics of circadian rhythms, little is known about the molecular basis of sleep. Nevertheless, as summarized here, phenotypic dissection of sleep into its most basic aspects can be used to identify both the single major genes and small effect quantitative trait loci involved. Although experimental models such as the mouse are more readily amenable to genetic analysis of sleep, similar approaches can be applied to humans.

Detecting epistasis with restricted response patterns in pairs of biallelic loci.

Well-established examples of genetic epistasis between a pair of loci typically show characteristic patterns of phenotypic distributions in joint genotype tables. However, inferring epistasis given such data is difficult due to the lack of power in commonly used approaches, which decompose the epistatic patterns into main plus interaction effects followed by testing the interaction term. Testing additive-only or all terms may have more power, but they are sensitive to nonepistatic patterns. Alternatively, the epistatic patterns of interest can be enumerated and the best matching one is found by searching through the possibilities. Although this approach requires multiple testing correction over possible patterns, each pattern can be fitted with a regression model with just one degree of freedom and thus the overall power can still be high, if the number of possible patterns is limited. Here we compare the power of the linear decomposition and pattern search methods, by applying them to simulated data generated under several patterns of joint genotype effects with simple biological interpretations. Interaction-only tests are the least powerful; while pattern search approach is the most powerful if the range of possibilities is restricted, but still includes the true pattern.

Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.

To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10⁻⁶⁰) and 9q31.2 (P = 2.2 × 10⁻³³), we identified 30 new menarche loci (all P < 5 × 10⁻⁸) and found suggestive evidence for a further 10 loci (P < 1.9 × 10⁻⁶). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.

Genomic and proteomic approaches towards an understanding of sleep.

The basic functions of sleep are still unclear, however, recent advances in genomics and proteomics have begun to contribute to our understanding of both normal and pathological sleep. In this review, we focus primarily on normal sleep and wake that have been studied in model organisms such as mice. Mice have been especially valuable since many different inbred strains exist that differ in sleep-related traits, and genes can be altered by either mutagenesis or targeted approaches. Advances in QTL (Quantitative Trait Loci) analysis have also helped to identify important sleep related genes, and several other QTLs have been mapped as a first step toward finding the genes that underlie basic sleep traits. In addition to more traditional genetic approaches, the abundance of different mRNAs across sleep and wake can now be studied and compared in different brain regions much more thoroughly using microarray methods. Progress at the protein level has been more difficult, but a few studies have begun to investigate changes in proteins during sleep and wake, and we present some of our own preliminary data in this area. A knowledge of which genes and proteins control or respond to changes in sleep will not only help answer fundamental questions, but may also suggest novel drug targets for improving multiple aspects of sleep and wake.

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Although it has long been known that genetic factors play a major role in shaping the electroencephalogram (EEG), progress on identifying the underlying genes has, until recently, been limited. Using quantitative trait loci (QTL) analyses several genomic loci affecting the sleep EEG could be mapped in the mouse. For three of these QTLs the responsible genes were identified leading to the implication of novel signaling pathways affecting EEG traits. Moreover, the insight that in the mouse the sleep-wake dependent dynamics in the expression of EEG slow waves during sleep is under strong genetic control has paved the way for candidate gene studies in humans investigating the contribution of specific polymorphism to the trait-like inter-individual differences in the susceptibility to sleep loss. Candidate gene studies in the mouse were also instrumental in establishing an alternative, noncircadian function for clock genes in the homeostatic regulation of sleep and modulating rhythmic EEG activity of thalamocortical origin. Future efforts should combine system genetics approaches in the mouse and genome-wide association studies in humans to facilitate uncovering the molecular pathways that shape brain activity.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations.

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

The effects of dominance, regular inbreeding and sampling design on Q(ST), an estimator of population differentiation for quantitative traits.

To test whether quantitative traits are under directional or homogenizing selection, it is common practice to compare population differentiation estimates at molecular markers (F(ST)) and quantitative traits (Q(ST)). If the trait is neutral and its determinism is additive, then theory predicts that Q(ST) = F(ST), while Q(ST) > F(ST) is predicted under directional selection for different local optima, and Q(ST) < F(ST) is predicted under homogenizing selection. However, nonadditive effects can alter these predictions. Here, we investigate the influence of dominance on the relation between Q(ST) and F(ST) for neutral traits. Using analytical results and computer simulations, we show that dominance generally deflates Q(ST) relative to F(ST). Under inbreeding, the effect of dominance vanishes, and we show that for selfing species, a better estimate of Q(ST) is obtained from selfed families than from half-sib families. We also compare several sampling designs and find that it is always best to sample many populations (>20) with few families (five) rather than few populations with many families. Provided that estimates of Q(ST) are derived from individuals originating from many populations, we conclude that the pattern Q(ST) > F(ST), and hence the inference of directional selection for different local optima, is robust to the effect of nonadditive gene actions.

New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism.

Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood. Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes and a second variant, near CCNL1, with no obvious link to adult traits. In an expanded genome-wide association meta-analysis and follow-up study of birth weight (of up to 69,308 individuals of European descent from 43 studies), we have now extended the number of loci associated at genome-wide significance to 7, accounting for a similar proportion of variance as maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes, ADRB1 with adult blood pressure and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.

Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity.

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10(-8)) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits.

Seventy-five genetic loci influencing the human red blood cell.

Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

Expression quantitative trait locus mapping across water availability environments reveals contrasting associations with genomic features in Arabidopsis.

The regulation of gene expression is crucial for an organism's development and response to stress, and an understanding of the evolution of gene expression is of fundamental importance to basic and applied biology. To improve this understanding, we conducted expression quantitative trait locus (eQTL) mapping in the Tsu-1 (Tsushima, Japan) × Kas-1 (Kashmir, India) recombinant inbred line population of Arabidopsis thaliana across soil drying treatments. We then used genome resequencing data to evaluate whether genomic features (promoter polymorphism, recombination rate, gene length, and gene density) are associated with genes responding to the environment (E) or with genes with genetic variation (G) in gene expression in the form of eQTLs. We identified thousands of genes that responded to soil drying and hundreds of main-effect eQTLs. However, we identified very few statistically significant eQTLs that interacted with the soil drying treatment (GxE eQTL). Analysis of genome resequencing data revealed associations of several genomic features with G and E genes. In general, E genes had lower promoter diversity and local recombination rates. By contrast, genes with eQTLs (G) had significantly greater promoter diversity and were located in genomic regions with higher recombination. These results suggest that genomic architecture may play an important a role in the evolution of gene expression.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

High quantitative and no molecular differentiation of a freshwater snail (Galba truncatula) between temporary and permanent water habitats.

We investigate the variation in quantitative and molecular traits in the freshwater snail Galba truncatula, from permanent and temporary water habitats. Using a common garden experiment, we measured 20 quantitative traits and molecular variation using seven microsatellites in 17 populations belonging to these two habitats. We estimated trait means in each habitat. We also estimated the distributions of overall genetic quantitative variation (QST), and of molecular variation (FST), within and between habitats. Overall, we observed a lack of association between molecular and quantitative variance. Among habitats, we found QST>FST, an indication of selection for different optima. Individuals from temporary water habitat matured older, at a larger size and were less fecund than individuals from permanent water habitat. We discuss these findings in the light of several theories for life-history traits evolution.

The IBO germination quantitative trait locus encodes a phosphatase 2C-related variant with a nonsynonymous amino acid change that interferes with abscisic acid signaling.

Natural genetic variation is crucial for adaptability of plants to different environments. Seed dormancy prevents precocious germination in unsuitable conditions and is an adaptation to a major macro-environmental parameter, the seasonal variation in temperature and day length. Here we report the isolation of IBO, a quantitative trait locus (QTL) that governs c. 30% of germination rate variance in an Arabidopsis recombinant inbred line (RIL) population derived from the parental accessions Eilenburg-0 (Eil-0) and Loch Ness-0 (Lc-0). IBO encodes an uncharacterized phosphatase 2C-related protein, but neither the Eil-0 nor the Lc-0 variant, which differ in a single amino acid, have any appreciable phosphatase activity in in vitro assays. However, we found that the amino acid change in the Lc-0 variant of the IBO protein confers reduced germination rate. Moreover, unlike the Eil-0 variant of the protein, the Lc-0 variant can interfere with the activity of the phosphatase 2C ABSCISIC ACID INSENSITIVE 1 in vitro. This suggests that the Lc-0 variant possibly interferes with abscisic acid signaling, a notion that is supported by physiological assays. Thus, we isolated an example of a QTL allele with a nonsynonymous amino acid change that might mediate local adaptation of seed germination timing.