The evolution of sex differences in disease genetics
William P Gilks, Jessica K Abbott, Edward H Morrow
There are significant differences in the biology of males and females, ranging from biochemical pathways to behavioural responses, which are relevant to modern medicine. Broad-sense heritability estimates differ between the sexes for many common medical disorders, indicating that genetic architecture can be sex-dependent. Recent genome-wide association studies (GWAS) have successfully identified sex-specific and sex-biased effects, where in addition to sex-specific effects on gene expression, twenty-two medical traits have sex-specific or sex-biased loci. Sex-specific genetic architecture of complex traits is also extensively documented in model organisms using genome-wide linkage or association mapping, and in gene disruption studies. The evolutionary origins of sex-specific genetic architecture and sexual dimorphism lie in the fact that males and females share most of their genetic variation yet experience different selection pressures. At the extreme is sexual antagonism, where selection on an allele acts in opposite directions between the sexes. Sexual antagonism has been repeatedly identified via a number of experimental methods in a range of different taxa. Although the molecular basis remains to be identified, mathematical models predict the maintenance of deleterious variants that experience selection in a sex-dependent manner. There are multiple mechanisms by which sexual antagonism and alleles under sex-differential selection could contribute toward the genetics of common, complex disorders. The evidence we review clearly indicates that further research into sex-dependent selection and the sex-specific genetic architecture of diseases would be rewarding. This would be aided by studies of laboratory and wild animal populations, and by modelling sex-specific effects in genome-wide association data with joint, gene-by-sex interaction tests. We predict that even sexually monomorphic diseases may harbour cryptic sex-specific genetic architecture. Furthermore, empirical evidence suggests that investigating sex-dependent epistasis may be especially rewarding. Finally, the prevalent nature of sex-specific genetic architecture in disease offers scope for the development of more effective, sex-specific therapies.

Mutant epigenetic machinery mediates climate adaptation in Arabidopsis thaliana
Xia Shen, Simon Forsberg, Mats Pettersson, Zheya Sheng, Orjan Carlborg
(Submitted on 16 Oct 2013)

The genetic basis of adaptation to climate is largely unknown. We explored the genetic regulation of climate plasticity and its contribution to adaptation using publicly available data from two collections of natural Arabidopsis thaliana accessions from a wide range of habitats. Sixteen loci with plastic alleles were mapped and many of these contained candidate genes with amino acid changes. The Chromomethylase 2 (CMT2) genotype influenced adaptation to seasonal temperature variability and accessions carrying a mutant CMT2 allele disrupting the genome-wide CHH-methylation pattern displayed a more plastic response to climate. We conclude that genetic regulation of plasticity appears to be important for climate adaptation and that genetic variation in the epigenetic machinery, leading to altered genome-wide epigenetic modifications, is one of the underlying molecular mechanisms.

The effect of paternal age on offspring intelligence and personality when controlling for paternal trait level

Ruben C. Arslan, Lars Penke, Wendy Johnson, William G. Iacono, Matt McGue
(Submitted on 18 Sep 2013)

Paternal age at conception has been found to predict the number of new genetic mutations. We examined the effect of father’s age at birth on offspring intelligence, head circumference and personality traits. Using the Minnesota Twin Family Study sample we tested paternal age effects while controlling for parents’ trait levels measured with the same precision as offspring’s. From evolutionary genetic considerations we predicted a negative effect of paternal age on offspring intelligence, but not on other traits. Controlling for parental IQ had the effect of turning a positive-zero order association negative. We found paternal age effects on offspring IQ and MPQ Absorption, but they were not robustly significant, nor replicable with additional covariates. No other noteworthy effects were found. Parents’ intelligence and personality correlated with their ages at twin birth, which may have obscured a small negative effect of advanced paternal age (< 1% of variance explained) on intelligence. We discuss future avenues for studies of paternal age effects and suggest that stronger research designs are needed to rule out confounding factors involving birth order and the Flynn effect.

Fast and accurate imputation of summary statistics enhances evidence of functional enrichment
Bogdan Pasaniuc, Noah Zaitlen, Huwenbo Shi, Gaurav Bhatia, Alexander Gusev, Joseph Pickrell, Joel Hirschhorn, David P Strachan, Nick Patterson, Alkes L. Price
(Submitted on 12 Sep 2013)

Imputation using external reference panels is a widely used approach for increasing power in GWAS and meta-analysis. Existing HMM-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants (increasing to 87% (60%) when summary LD information is available from target samples) versus 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and is computationally very fast. As an empirical demonstration, we apply our method to 7 case-control phenotypes from the WTCCC data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of $\chi^2$ association statistics) compared to HMM-based imputation from individual-level genotypes at the 227 (176) published SNPs in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of 4 lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic vs. non-genic loci for these traits, as compared to an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses.

An integrative genomic approach illuminates the causes and consequences of genetic background effects
Christopher H. Chandler, Sudarshan Chari, David Tack, Ian Dworkin
(Submitted on 2 Sep 2013)

(abridged) – The phenotypic consequences of mutations are modulated by the wild type genetic background in which they occur, sometimes dramatically so. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist, nor about the mechanisms underlying it. We also lack knowledge on how mutations interact with the genetic background to influence gene expression patterns, and how gene expression may in turn mediate mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scallopedE3 allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets, from a mapping-by-introgression experiment, as well as a tagged RNA gene expression dataset. In addition we used whole genome re-sequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutational screen. By searching for genes showing a congruent signal in multiple datasets, we identified candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers are caused by higher-order epistasis, not quantitative non-complementation of alleles. Our results also suggest that cis-regulatory variation contributes little to the background dependence of this mutant phenotype. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well.