I explore the genetic and environmental basis of inheritance using modern techniques, in particular high-density genotyping arrays, and older techniques, in particular family history, to explore some longstanding questions about the way we inherit complex traits. Using pedigree data and the parent-offspring regression technique, I estimate narrow sense heritability (h2) of human lifespan in 20th Century Scotland as 0.16, lower than commonly cited studies in other populations. I also observe similar concordance between spouses as between parents and offspring - suggesting my estimate of heritability may include significant within-family environment effects and thus should be considered an upper bound. Using genome-wide array data to identify runs of homozygosity, from 150 cohorts across the world and up to 350,000 subjects per trait, I show that cognitive function and body size are associated with the total length of genome-wide runs of homozygosity. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of homozygosity on blood pressure and low-density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. An association between genome-wide homozygosity and complex traits arises due to directional dominance. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases have not. The analysis of less common single nucleotide polymorphism (SNP) variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association, whilst avoiding false positives. I show that addition of 100 population-specific exome sequences to 1,000 genomes global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%). The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%. Inheritance of complex traits remains a field wide open for discovery, both in determining the balance between nature and nurture and discovery of the specific mechanisms by which DNA causes variation in these traits, with the prospect of such discoveries illuminating biological pathways involved and, as knowledge deepens, facilitating prediction.

Depression is the most common psychiatric disorder and the leading cause of disability worldwide. Despite evidence for a genetic component, the genetic aetiology of this disorder remains elusive. To date, only one association study has identified and replicated risk loci for depression. This thesis focuses on aiding genetic discovery by revisiting the depressed phenotype and developing a quantitative trait, using data from Generation Scotland: The Scottish Family Health Study. These analyses aim to test whether this derived quantitative trait has improved statistical power to identify genetic risk variants for depression, relative to the binary classification of case/control. Measures of genetic covariation were used to evaluate and rank ten measures of mood, personality and cognitive ability as endophenotypes for depression. The highest ranking traits were subjected to principal component analysis, and the first principal component used as a quantitative measure of depression. This composite trait was compared to the binary classification of depression in terms of ability to identify risk loci in a genome-wide association study, and phenotypic variance explained by polygenic profile scores for psychiatric disorders. I also compared the composite trait to the univariate traits in terms of their ability to fulfill the endophenotype criteria as described by Gottesman and Gould, namely: being heritable, genetically and phenotypically correlated with depression, state independent, co-segregating with illness in families, and observed at a higher rate in unaffected relatives than in unrelated controls. Four out of ten traits fulfilled most endophenotype criteria, however, only two traits - neuroticism and the general health questionnaire (a measure of current psychological distress) - consistently ranked highest across all analyses. As such, three composite traits were derived incorporating two, three, or four traits. Association analyses of binary depression, univariate traits and composite traits yielded no genome-wide significant results, with most traits performing equivalently. However, composite traits were more heritable and more highly correlated with depression than their constituent traits, suggesting that analyzing these traits in combination was capturing more of the heritable component of depression. Polygenic scores for psychiatric disorders explained more trait variance for the composite traits than the univariate traits, and depression itself. Overall, whilst the composite traits generally obtained more significant results, they did not identify any further insight into the genetic aetiology of depression. This work therefore provides support for the urgent need to redefine the depressed phenotype based on objective and quantitative measures. This is essential for risk stratification, better diagnoses, novel target identification and improved treatment.

Infectious diseases are a major cause of morbidity and mortality worldwide. Streptococcus pneumoniae and Neisseria meningitidis are major causes of severe bacterial disease which can manifest as invasive disease such as bacteraemia and meningitis. Exposure to these pathogens is relatively widespread, yet only a minority of individuals develop invasive disease. A host genetic component to infectious disease susceptibility has been implied from twin and adoptee studies. A role for rare large effect genetic variants in predisposition to infection has been demonstrated through the study of individuals with primary immunodeficiencies. However, a majority of these studies have been undertaken in individuals with a history of recurrent disease or in multi-case families. The relative role of rare genetic variants of moderate to large effect at the population level has not been widely explored. This thesis presents effort made using next generation sequencing methods to identify rare genetic variants that lead to increased susceptibility to bacterial disease focussing on meningococcal disease, pleural infection(empyema), pneumococcal disease and sepsis phenotypes. Using an exome sequencing approach in 13 cases with invasive meningococcal disease, a novel mutation leading to a complement deficiency and increased risk of meningococcal infection was identified and functionally validated in one individual. This mutation in the CFP gene was demonstrated as leading to impaired properdin secretion. Further analysis implicated loss of function mutations in CD4 and ZAP70 as novel loci for meningococcal disease susceptibility. A case control association analysis for sepsis susceptibility highlighted the possible role for small Rho GTPases in sepsis pathology. By aggregating all rare predicted deleterious mutations in a gene, four genes in this pathway, (ROCK2, ARHGAP18, FYN and CDC42BPG) were implicated as having an excess of rare deleterious variants in sepsis samples compared to population controls. A similar approach identified low frequency genetic variants in the CD109 gene as predisposing to empyema susceptibility in children. Finally, preliminary evidence from adult individuals with invasive pneumococcal disease points to a potential role of the RNASE7 gene in invasive pneumococcal disease susceptibility. This association was primarily due to a predicted deleterious missense mutation present in cases and absent in controls. Taken together, these results have identified a number of potential loci with rare variants associated with susceptibility to severe phenotypes of bacterial diseases.

No description available.

No description available.

Several studies examining the genetics of adaptation have identified single alleles, of large phenotypic e ect, contributing to divergence between populations. This empirical finding is consistent with predictions made by the geometric model of adaptation, where a small number of alleles of large e ect and many alleles of small e ect are fixed as the population adapts. However, these examples of single genes of large e ect may represent a biased sample of the alleles of adaptation with polygenic allele shifts having a greater contribution than currently understood. Increasing power to detect smaller e ect variants, due to falling sequencing costs and improved statistical methods, has made the contribution of small allele frequency shifts at many loci, or polygenic adaptation, more apparent. In contrast to models predicting single genes of large e ect with large allele frequency changes, polygenic adaptation allows for small allele frequency changes across many alleles of small e ect to contribute to phenotypic change. Using artificial selection, I demonstrate the alignment of genetic e ects contributing to wing shape variation within a developmental pathway but a lack of replication of these same genetic e ects in other wild-caught populations. Secondly, using advanced intercross QTL mapping between altitudinally diverged populations, I demonstrate a polygenic basis for wing shape and size variation. Finally, using comparative developmental biology I investigate how change to cell size and number in the wing may contribute to divergence between high and low altitude populations. Together, this work provides evidence for many alleles of small e ect rather than alleles of large e ect contributing to adaptive divergence of wing shape and size and provides context for identified alleles through replication in other populations and comparative developmental biology. / Thesis / Doctor of Philosophy (PhD)

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No description available.

Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits. The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep behavior is done using our non-invasive Piezo system for mouse activity monitoring. Thus far, sleep behavior has been recorded in more than 6000 mice representing 343 knockout lines and nearly 2000 control mice. Control and KO mice have been compared using multivariate statistical approaches to identify genes that exhibit significant effects on sleep variables from Piezo data. Using these statistical approaches, significant genes affecting sleep have been identified. Genes affecting sleep in a specific sex and that specifically affect sleep during daytime and/or night have also been identified and reported. The KOMP2 consists of a broad-based phenotyping pipeline that consists of collection of physiological and biochemical parameters through a variety of assays. Mice enter the pipeline at 4 weeks of age and leave at 18 weeks. Currently, the IMPC (International Mouse Phenotyping Consortium) database consists of more than 33 million observations. Our final dataset prepared by extracting biological sample data for whom sleep recordings are available consists of nearly 1.5 million observations from multitude of phenotyping assays. Through big data analytics and sophisticated machine learning approaches, we have been able to identify predictor phenotypes that affect sleep in mice. The phenotypes thus identified can play a key role in developing our understanding of mechanism of sleep regulation.

There is solid evidence that complex human diseases can be caused by rare variants. Next generation sequencing technology has revolutionized the study of complex human diseases, and made possible detecting associations with rare variants. Traditional statistical methods can be inefficient for analyzing sequence data and underpowered. In addition, due to high cost of sequencing, it is also necessary to explore novel cost effective studies in order to maximize power and reduce sequencing cost. In this thesis, three important problems for analyzing sequence data and detecting associations with rare variants are presented. In the first chapter, we presented a new method for detecting rare variants/binary trait associations in the presence of gene interactions. In the second chapter, we explored cost effective study designs for replicating sequence based association studies, combining both sequencing and customized genotyping. In the third chapter, we present a method for analyzing multiple phenotypes in selected samples, such that phenotypes that are commonly measured in different studies can be jointly analyzed to improve power. The methods and study designs presented are important for dissecting complex trait etiologies using sequence data.