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The carriers : what the fragile X gene reveals about family, heredity, and scientific discovery
\"Fragile X syndrome is a genetic condition that causes a range of neurodevelopmental problems including learning disabilities and cognitive impairment. Boys with the condition are more likely to be born fully affected by it, while women who are seemingly unaffected carriers have an increased risk of giving birth to an affected child. Recent research indicates that Fragile X syndrome is highly unusual in the world of genetic disorders, in that carriers, who were previously thought to show no symptoms at all, are in fact affected in their own ways: into adulthood, they can develop personality and emotional changes, tremors, and difficulty walking. The title characters in The Carriers, then, are the previous generations--mothers and grandparents--of fully affected Fragile X patients. This book aims to tell the stories of how families are affected by this genetic disorder over generations, as well as the initial science that discovered it and the current science that's teaching us how Fragile X is affecting silent carriers in ways that weren't previously recognized. Understanding psychiatric symptoms in premutation carriers is complicated by the fact that many are caring for children with Fragile X syndrome and fathers with the tremor/ataxia symptom (difficulty walking). This story particularly highlights women, who are often the carriers in question and also the genetic researchers achieving scientific breakthroughs\"-- Provided by publisher.
Book
The power of genetic diversity in genome-wide association studies of lipids
Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use
1
. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels
2
, heart disease remains the leading cause of death worldwide
3
. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS
4
–
23
have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns
24
. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine
25
, we anticipate that increased diversity of participants will lead to more accurate and equitable
26
application of polygenic scores in clinical practice.
A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.
Journal Article
Genetic studies of body mass index yield new insights for obesity biology
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (
P
< 5 × 10
−8
), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis.
Genetic correlates of obesity
In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Journal Article
The genome factor : what the social genomics revolution reveals about ourselves, our history, and the future
\"For a century, social scientists have avoided genetics like the plague. But in the past decade, a small but intrepid group of economists, political scientists, and sociologists have harnessed the genomics revolution to paint a more complete picture of human social life than ever before. The Genome Factor describes the latest astonishing discoveries being made at the scientific frontier where genomics and the social sciences intersect. The Genome Factor reveals that there are real genetic differences by racial ancestry--but ones that don't conform to what we call black, white, or Latino. Genes explain a significant share of who gets ahead in society and who does not, but instead of giving rise to a genotocracy, genes often act as engines of mobility that counter social disadvantage. An increasing number of us are marrying partners with similar education levels as ourselves, but genetically speaking, humans are mixing it up more than ever before with respect to mating and reproduction. These are just a few of the many findings presented in this illuminating and entertaining book, which also tackles controversial topics such as genetically personalized education and the future of reproduction in a world where more and more of us are taking advantage of cheap genotyping services like 23andMe to find out what our genes may hold in store for ourselves and our children. The Genome Factor shows how genomics is transforming the social sciences--and how social scientists are integrating both nature and nurture into a unified, comprehensive understanding of human behavior at both the individual and society-wide levels.\"-- Provided by publisher.
Book
Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes
1
,
2
and molecular mechanisms that are often specific to cell type
3
,
4
. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (
P
< 5 × 10
−8
) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores
5
in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
A meta-analysis of genome-wide association studies of type 2 diabetes (T2D) identifies more than 600 T2D-associated loci; integrating physiological trait and single-cell chromatin accessibility data at these loci sheds light on heterogeneity within the T2D phenotype.
Journal Article
Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors
Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (
n
= 321,223) and offspring birth weight (
n
= 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight–blood pressure association is attributable to genetic effects, and not to intrauterine programming.
An expanded GWAS of birth weight and subsequent analysis using structural equation modeling and Mendelian randomization decomposes maternal and fetal genetic contributions and causal links between birth weight, blood pressure and glycemic traits.
Journal Article
Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis
Genome-wide association studies (GWAS) have identified many risk loci for Alzheimer’s disease (AD)
1
,
2
, but how these loci confer AD risk is unclear. Here, we aimed to identify loci that confer AD risk through their effects on brain protein abundance to provide new insights into AD pathogenesis. To that end, we integrated AD GWAS results with human brain proteomes to perform a proteome-wide association study (PWAS) of AD, followed by Mendelian randomization and colocalization analysis. We identified 11 genes that are consistent with being causal in AD, acting via their
cis
-regulated brain protein abundance. Nine replicated in a confirmation PWAS and eight represent new AD risk genes not identified before by AD GWAS. Furthermore, we demonstrated that our results were independent of
APOE e4
. Together, our findings provide new insights into AD pathogenesis and promising targets for further mechanistic and therapeutic studies.
Integrating human brain proteomes with genome-wide association data followed by Mendelian randomization identifies 11 genes with potentially causal roles in Alzheimer’s disease pathogenesis.
Journal Article
Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder
Survey of postzygotic mosaic mutations (PZMs) in 5,947 trios with autism spectrum disorders (ASD) discovers differences in mutational properties between germline mutations and PZMs. Spatiotemporal analyses of the PZMs also revealed the association of the amygdala with ASD and implicated risk genes, including recurrent potential gain-of-function mutations in
SMARCA4
.
We systematically analyzed postzygotic mutations (PZMs) in whole-exome sequences from the largest collection of trios (5,947) with autism spectrum disorder (ASD) available, including 282 unpublished trios, and performed resequencing using multiple independent technologies. We identified 7.5% of
de novo
mutations as PZMs, 83.3% of which were not described in previous studies. Damaging, nonsynonymous PZMs within critical exons of prenatally expressed genes were more common in ASD probands than controls (
P
< 1 × 10
−6
), and genes carrying these PZMs were enriched for expression in the amygdala (
P
= 5.4 × 10
−3
). Two genes (
KLF16
and
MSANTD2
) were significantly enriched for PZMs genome-wide, and other PZMs involved genes (
SCN2A
,
HNRNPU
and
SMARCA4
) whose mutation is known to cause ASD or other neurodevelopmental disorders. PZMs constitute a significant proportion of
de novo
mutations and contribute importantly to ASD risk.
Journal Article