Trevor J. Pemberton

Key Points: A novel nonsense variant ( mcy ) in ankyrin repeat and sterile alpha motif domain-containing 6 ( Anks6 ) promotes rapid disease progression in the Han:SPRD- Cy rat carrying a missense variant in Anks6 . mcy ^−/− rats exhibit prenatal lethality characterized by laterality and cardiovascular abnormalities. These findings indicate that ANKS6 nonfunction in rats leads to prenatal lethality, whereas misfunction leads to polycystic kidney disease development. Background: Polycystic kidney disease (PKD) encompasses a group of genetic disorders characterized by the proliferation of fluid-filled renal cysts, leading to progressive renal failure and death. A key feature of PKD is its variable expressivity across patients, even when caused by the same variant, highlighting the importance of genetic background in PKD expression. Methods: We identified an ostensibly healthy Sprague Dawley rat line with a variant that modifies PKD expressivity in Han:SPRD- Cy rats (caused by a missense variant [p.Arg717Trp] in the ankyrin repeat and sterile alpha motif domain-containing 6 [ Anks6 ] gene), which we named mcy (modifier of Cy ). We used whole-genome sequencing and segregation analysis to identify the mcy variant, quantitative PCR and mRNA sequencing to evaluate its effects on gene expression, western blotting and immunohistochemistry to assess its protein consequences, and ultrasound and histology to examine its impact on rat embryonic development. Results: We identified a nonsense variant in the Anks6 gene as the genetic basis of the mcy phenotype (c.1126G>T [p.Glu376X]). Although mcy ^+/− rats are ostensibly healthy and do not develop PKD, mcy ^−/− rats exhibit laterality defects and die prenatally at E16.5 because of apparent perturbations in cardiovascular development. Notably, mcy ^+/− Cy ^+/− rats develop PKD much more rapidly than Cy ^+/− rats, and in a timeframe consistent with Cy ^−/− rats . Transcripts with the mcy variant allele seem to undergo nonsense-mediated decay, and no ANKS6 protein is detected. However, gene expression patterns in the kidneys did not differ significantly between age-matched mcy ^+/+ and mcy ^+/− rats, indicating that ANKS6 insufficiency does not cause PKD. Conclusions: We identified a novel nonsense variant in Anks6 . The findings indicate that the absence of wild-type ANKS6 accelerates PKD development in the Han:SPRD- Cy rat and that complete ANKS6 deficiency prevents normal embryonic development in rats.

The evolutionary and biological bases of the Central African “pygmy” phenotype, a characteristic of rainforest hunter-gatherers defined by reduced body size compared with neighboring farmers, remain largely unknown. Here, we perform a joint investigation in Central African hunter-gatherers and farmers of adult standing height, sitting height, leg length, and body mass index (BMI), considering 358 hunter-gatherers and 169 farmers with genotypes for 153,798 SNPs. In addition to reduced standing heights, hunter-gatherers have shorter sitting heights and leg lengths and higher sitting/standing height ratios than farmers and lower BMI for males. Standing height, sitting height, and leg length are strongly correlated with inferred levels of farmer genetic ancestry, whereas BMI is only weakly correlated, perhaps reflecting greater contributions of non-genetic factors to body weight than to height. Single- and multi-marker association tests identify one region and eight genes associated with hunter-gatherer/farmer status, and 24 genes associated with the height-related traits. Many of these genes have putative functions consistent with roles in determining their associated traits and the pygmy phenotype, and they include three associated with standing height in non-Africans (PRKG1, DSCAM, MAGI2). We find evidence that European height-associated SNPs or variants in linkage disequilibrium with them contribute to standing- and sitting-height determination in Central Africans, but not to the differential status of hunter-gatherers and farmers. These findings provide new insights into the biological basis of the pygmy phenotype, and they highlight the potential of cross-population studies for exploring the genetic basis of phenotypes that vary naturally across populations.

Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Background: Central African hunter-gatherer Pygmy populations have reduced body size compared with their often much larger agricultural non-Pygmy neighbors, potentially reflecting adaptation to the anatomical and physiological constraints of their lifestyle in tropical rainforests. Earlier studies investigating the genetics of the pygmy phenotype have focused on standing height, one aspect of this complex phenotype that is itself a composite of skeletal components with different growth patterns. Here, we extend the investigations of standing height to the variability and genetic architecture of sitting height and subischial leg length as well as body mass index (BMI) in a sample of 406 unrelated West Central African Pygmies and non-Pygmies. Results: In addition to their significantly reduced standing height compared with non-Pygmies, we find Pygmies to have significantly shorter sitting heights and subischial leg lengths as well as higher sitting/standing height ratios than non-Pygmies. However, while male Pygmies had significantly lower BMI compared with male non-Pygmies, the BMI of females were instead similar. Consistent with prior observations with standing height, sitting height and subischial leg length were strongly correlated with inferred levels of non-Pygmy genetic admixture while BMI was instead weakly correlated, likely reflecting the greater contribution of non-genetic factors to the determination of body weight compared with height. Using 196,725 SNPs on the Illumina Cardio-MetaboChip with genotypes on 358 Pygmy and 169 non-Pygmy individuals together with single-and multi-marker association approaches, we identified a single genomic region and seven genes associated with Pygmy/non-Pygmy categorization as well as 9, 10, 9, and 10 genes associated with standing and sitting height, sitting/standing height ratio, and subischial leg length, respectively. Many of the genes identified have putative functions consistent with a role in determining their associated trait as well as the complex Central African pygmy phenotype. Conclusions: These findings highlight the potential of modestly sized datasets of Pygmies and non-Pygmies to detect biologically meaningful associations with traits contributing to the Central African pygmy phenotype. Moreover, they provide new insights into the phenotypic and genetic bases of the complex pygmy phenotype and offer new opportunities to facilitate our understanding of its complex evolutionary origins.

Background: Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to detect ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. Results: We report a weighted likelihood approach for identifying ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to detect ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. Conclusions: This weighted likelihood ROA detection approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Significance: We describe a method for identifying in distinct genetic datasets observations that represent the same person. By using correlations among genetic markers close to one another in the genome, the method can succeed even if the datasets contain no overlapping markers. We show that the method can link a dataset similar to those used in genomic studies with another dataset containing markers used for forensics. Our approach can assist in maintaining backward compatibility with databases of existing forensic genetic profiles as systems move to new marker types. At the same time, it illustrates that the privacy risks that can arise from the cross-linking of databases are inherent even for small numbers of markers.

Runs of homozygosity (ROH) are important genomic features that manifest when identical-by-descent haplotypes are inherited from parents. Their length distributions and genomic locations are informative about population history and they are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. Here, we present software implementing a model-based method ( Pemberton et al., 2012) for inferring ROH in genome-wide SNP datasets that incorporates population-specific parameters and a genotyping error rate as well as provides a length-based classification module to identify biologically interesting classes of ROH. Using simulations, we evaluate the performance of this method. GARLIC is written in C ++. Source code and pre-compiled binaries (Windows, OSX and Linux) are hosted on GitHub (https://github.com/szpiech/garlic) under the GNU General Public License version 3. Supplementary data are available at Bioinformatics online.

Significance: Linguistic data are often combined with genetic data to frame inferences about human population history. However, little is known about whether human demographic history generates patterns in linguistic data that are similar to those found in genetic data at a global scale. Here, we analyze the largest available datasets of both phonemes and genotyped populations. Similar axes of human geographic differentiation can be inferred from genetic data and phoneme inventories; however, geographic isolation does not necessarily lead to the loss of phonemes. Our results show that migration within geographic regions shapes phoneme evolution, although human expansion out of Africa has not left a strong signature on phonemes.