scholarly journals A non-human primate system for large-scale genetic studies of complex traits

2012 ◽  
Vol 21 (15) ◽  
pp. 3307-3316 ◽  
Author(s):  
Anna J. Jasinska ◽  
Michelle K. Lin ◽  
Susan Service ◽  
Oi-Wa Choi ◽  
Joseph DeYoung ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Genetic Studies ◽  
Primate System ◽  
Human Primate

The Use of Medical Record Linkage for Population and Genetic Studies

1969 ◽  
Vol 08 (01) ◽  
pp. 07-11 ◽  
Author(s):  
H. B. Newcombe
Keyword(s):  
Record Linkage ◽  
Large Scale ◽  
Medical Record Linkage ◽  
Canadian Province ◽  
Genetic Studies ◽  
Parental Characteristics ◽  
Family Histories ◽  
The Family ◽  
Large Populations ◽  
Machine Readable

Methods are described for deriving personal and family histories of birth, marriage, procreation, ill health and death, for large populations, from existing civil registrations of vital events and the routine records of ill health. Computers have been used to group together and »link« the separately derived records pertaining to successive events in the lives of the same individuals and families, rapidly and on a large scale. Most of the records employed are already available as machine readable punchcards and magnetic tapes, for statistical and administrative purposes, and only minor modifications have been made to the manner in which these are produced.As applied to the population of the Canadian province of British Columbia (currently about 2 million people) these methods have already yielded substantial information on the risks of disease: a) in the population, b) in relation to various parental characteristics, and c) as correlated with previous occurrences in the family histories.

scholarly journals Shared associations identify causal relationships between gene expression and immune cell phenotypes

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Christiane Gasperi ◽  
Sung Chun ◽  
Shamil R. Sunyaev ◽  
Chris Cotsapas
Keyword(s):  
Complex Traits ◽  
Direct Evidence ◽  
Immune Cell ◽  
Genetic Locus ◽  
Causal Variant ◽  
Causal Relationships ◽  
Genetic Studies ◽  
Two Factors ◽  
Gene Regulatory ◽  
Cell Phenotypes

AbstractGenetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.

scholarly journals The limits of normal approximation for adult height

2021 ◽  
Author(s):  
Sergei A. Slavskii ◽  
Ivan A. Kuznetsov ◽  
Tatiana I. Shashkova ◽  
Georgii A. Bazykin ◽  
Tatiana I. Axenovich ◽  
...  
Keyword(s):  
Complex Traits ◽  
Adult Height ◽  
Normal Approximation ◽  
Classical Model ◽  
Model Complexity ◽  
Test Case ◽  
Genetic Studies ◽  
Quantitative Genetic ◽  
Profound Influence ◽  
Log Normal

AbstractAdult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

High-throughput quantification of the mechanical competence of murine femora — A highly automated approach for large-scale genetic studies

Bone ◽  
2013 ◽  
Vol 55 (1) ◽  
pp. 216-221 ◽  
Author(s):  
D. Ruffoni ◽  
T. Kohler ◽  
R. Voide ◽  
A.J. Wirth ◽  
L.R. Donahue ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Genetic Studies

scholarly journals The daunting polygenicity of mental illness: making a new map

2018 ◽  
Vol 373 (1742) ◽  
pp. 20170031 ◽  
Author(s):  
Steven E. Hyman
Keyword(s):  
Mental Health ◽  
Large Scale ◽  
Computational Models ◽  
Model Organisms ◽  
Human Biology ◽  
Genetic Studies ◽  
Computational Tools ◽  
Experimental Systems ◽  
Terra Incognita ◽  
Phenotypic Complexity

An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic terra incognita . This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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